Method for constructing equal protection compliance knowledge base based on multi-dimensional semantic association

Abstract

The application relates to the technical field of computer systems and discloses a method for constructing an equal protection compliance knowledge base based on multi-dimensional semantic association, which comprises the following steps: extracting equal protection standard entities to construct a compliance rule subgraph, and analyzing network configurations to construct an asset entity subgraph; driving a heterogeneous graph attention calculation unit to generate a compliance semantic representation vector according to the compliance rule subgraph, which is used as a constraint feature to propagate along the asset entity subgraph; embedding a symbolic logic gate module into a weight distribution logic; determining asset node topological connection features, triggering mutual exclusion isolation constraints, superimposing a negative infinite polarization operator, making edge weights converge to 0, and blocking the topological diffusion of compliance risk features. The application fuses a symbolic logic rigid constraint into a deep learning reasoning architecture, eliminates neighborhood pollution caused by feature smoothing, and improves the accuracy of compliance risk perception in a complex environment.

Description

Technical Field

[0001] This invention relates to a method for constructing a compliance knowledge base based on multidimensional semantic association, belonging to the field of computer system technology. Background Technology

[0002] The current practice is to map regulatory clauses into computer-parsable logical criteria through preset rule trees or regular expressions to achieve automated verification of asset configuration status. However, with the expansion of cloud-native architecture and heterogeneous network topology, there are representational differences between the semantic features of regulatory texts and the discrete configuration parameters of physical assets. In order to maintain verification efficiency, the existing approach simplifies complex system-level constraints into linear lexical matching, which leads to missed detections when dealing with deep compliance requirements involving logical dependencies between components. Furthermore, the system maintenance cost increases non-linearly with the growth of asset size.

[0003] The evolution from static lexical matching to graph association reasoning has overcome the limitations of shallow feature comparison. However, existing graph aggregation-based architectures also suffer from insufficient underlying mechanisms. For example, Chinese invention patent application CN117494048A discloses a method for detecting and identifying enterprise violation data that integrates knowledge graphs and graph attention networks. This application heavily relies on the continuous aggregation of node features and the probabilistic smoothing mechanism in the graph model to map the risk topology propagation path. However, network security compliance involves cross-security access control and network isolation, which are characterized by strong mutually exclusive discrete rigid boundaries and discontinuities at both the physical and logical levels. The probabilistic gradual change process forces the rigid network boundary of absolute isolation to fit into a continuous feature space, resulting in a fundamental mismatch between the core presuppositions and the actual boundary conditions. Introducing graph neural networks to capture asset topological relationships is an improvement path. However, when graph models deal with rigid mutually exclusive constraints such as logical isolation defined by regulations, they are constrained by the inherent smoothness of the feature aggregation process. This causes the risk features of non-compliant nodes to propagate along the topological edges of the asset entity subgraph to compliant neighboring nodes. This feature space pollution caused by the underlying calculation method cannot be eliminated by the output masking operation, resulting in neighborhood false alarms in the audit results.

[0004] Therefore, while maintaining the semantic generalization ability of the computational model, how to reconstruct the aggregation mechanism to achieve physical blocking of the propagation path of risk features has become the technical problem to be solved by this invention. Summary of the Invention

[0005] To address the problems raised in the background art, the technical solution of this invention is as follows: A method for constructing a compliance knowledge base based on multi-dimensional semantic association, comprising the following steps:

[0006] Step 101: Obtain the information security standard text and extract compliant entities to construct a compliance rule subgraph representing compliance constraint relationships. At the same time, obtain the configuration data of the target network environment and parse the topology associations to construct an asset entity subgraph representing asset connection relationships.

[0007] Step 102: Configure the heterogeneous graph attention calculation unit and drive the heterogeneous graph attention calculation unit to calculate the compliance semantic representation vector based on the adjacency matrix of the compliance rule subgraph. Use the compliance semantic representation vector as the constraint feature quantity and perform feature propagation pre-calculation along the topological edge of the asset entity subgraph.

[0008] Step 103: Configure the symbolic logic gating module and embed the logic decision link of the symbolic logic gating module into the weight allocation logic of the heterogeneous graph attention calculation unit;

[0009] Step 104: Extract the logical topological connection features between adjacent asset nodes in the asset entity subgraph. Based on the compliance semantic representation vector, match and determine the logical topological connection features. When the determination result triggers the mutual exclusion constraint defined in the compliance rule subgraph, use the symbolic logic gating module to superimpose the negative infinite polarization operator on the corresponding unnormalized attention score, so as to make the edge weight coefficient converge to 0 through normalization processing, thereby blocking the topological diffusion of the node representation vector with compliance risk identification in the asset entity subgraph during the tensor calculation stage.

[0010] Preferably, the process of blocking using the symbolic logic gating module in step 104 further includes: step 1041, monitoring the traffic exchange attribute data between the first node and the second node in the asset entity subgraph; step 1042, retrieving the access control benchmark value for the traffic exchange attribute data in the compliance rule subgraph; step 1043, when the traffic exchange attribute data does not meet the access control benchmark value, the symbolic logic gating module generates a polarization control quantity to shut down the logical path for feature aggregation from the first node to the second node.

[0011] Preferably, the process of constructing the compliance rule subgraph in step 101 includes: step 1011, identifying the compliance subject, controlled object, and logical constraint operator in the information security standard text; step 1012, establishing the semantic association edge between the compliance subject and the controlled object, and assigning an initial weight to the semantic association edge according to the constraint strength of the logical constraint operator, forming a constraint topology of the multidimensional semantic space.

[0012] Preferably, the process of constructing the asset entity subgraph in step 101 includes: step 1013, parsing the routing table entries, firewall access control lists, and virtual LAN identifiers in the configuration data; step 1014, converting the routing table entries and firewall access control lists into logical edge features between asset nodes in the asset entity subgraph, and using the virtual LAN identifier to define the logical isolation domain attribute to which the asset node belongs.

[0013] Preferably, the calculation of the compliance semantic representation vector in step 102 includes: step 1021, using the hierarchical aggregation mechanism of the heterogeneous graph attention calculation unit to perform self-attention transformation calculation on the compliance rule subgraph; step 1022, extracting the hidden layer tensor after the self-attention transformation calculation as the compliance semantic representation vector.

[0014] Preferably, after step 104, the method further includes: step 1044, periodically acquiring incremental configuration data of the target network environment and updating the asset entity subgraph; step 1045, comparing the distribution status of edge weight coefficients before and after the update, and when it is found that the edge weight coefficients that originally converged to 0 have become non-zero values, outputting a compliance item failure warning signal.

[0015] Preferably, the method further includes: step 105, constructing a joint loss function, which includes a topological loss term characterizing the accuracy of subgraph isomorphic matching and a compliance deviation term characterizing the semantic constraint alignment depth; step 106, using backpropagation logic to adjust the weight parameters of the heterogeneous graph attention calculation unit to minimize the joint loss function and complete the adaptive optimization of the knowledge base.

[0016] Preferably, blocking the topological diffusion of the node representation vector with compliance risk identifier in step 104 includes: step 1046, defining the spatial boundary of the representation vector of compliant nodes in the asset entity subgraph; step 1047, ensuring that the offset of the feature tensor with violation mark to the representation vector of the compliant node during the aggregation process does not exceed the preset robustness threshold by limiting the edge weight coefficient.

[0017] Preferably, the method further includes: step 107, extracting high-risk access paths in the asset entity subgraph based on the distribution of edge weight coefficients; step 108, mapping the high-risk access paths to the corresponding clauses of the information security standard text, and generating a compliance audit report with asset logical location information.

[0018] Compared with the prior art, the beneficial effects of the present invention are:

[0019] 1. In the construction of a multi-dimensional semantic association-based compliance knowledge base, this invention projects unstructured legal and regulatory provisions and discrete system architecture configurations onto a unified non-Euclidean semantic vector space by constructing a bidirectional feature interaction mechanism between standard definition subgraphs and asset entity subgraphs. This achieves deep penetration and alignment of compliance constraints into the physical asset topology. This structured mapping method utilizes the topology aggregation capability of graph attention networks to automatically extract and quantify the hidden logical dependencies between asset components, thereby identifying high-dimensional semantic conflicts that traditional keyword matching or regularization engines cannot reach, and improving the perception accuracy of systemic compliance risks in complex cloud-native environments.

[0020] 2. By embedding a symbolic logic gating unit in the attention coefficient calculation kernel of the heterogeneous graph attention network, the underlying tension blocking of strongly mutually exclusive compliance rules is achieved. When the physical connection state between asset nodes triggers the isolation constraint defined by the compliance standard, the symbolic logic gating unit superimposes a negative infinite polarization operator on the unnormalized attention scalar, so that the edge weight coefficients after exponential normalization converge to 0. This mechanism physically cuts off the propagation of non-compliant features from the topological path at the initial stage of tensor flow, avoiding irreversible smooth penetration of risk features into compliant neighboring nodes in multi-layer convolution iteration, effectively solving the inherent feature pollution problem of graph neural networks, and eliminating neighborhood compliance false alarms caused by feature averaging.

[0021] 3. By organically combining the discrete rigid constraints of symbolic logic with the high-dimensional generalization reasoning of deep learning, a hybrid computing architecture with logical self-consistency is constructed. The feature transformation unit of graph attention network is used to absorb the semantic ambiguity of regulatory texts, while the polarization blocking operator endogenous to the aggregation kernel ensures the rigid execution of bottom-line rules. This makes the knowledge base construction process both fault-tolerant to handle non-standard working conditions and maintains absolute immunity to deterministic compliance boundaries. This synergistic mechanism enables the system to achieve nonlinear self-consistency between continuous spatial reasoning and discrete logic intervention without destroying the differentiability of the computation graph when dealing with dynamically evolving heterogeneous network topologies, thereby enhancing the engineering robustness of the compliance automated audit solution. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the construction process of the compliance knowledge base for multidimensional semantic association and polarization operator intervention in this invention.

[0023] Figure 2 This is a system logic architecture diagram for the present invention that integrates deep learning reasoning and symbolic logic hard constraints.

[0024] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0026] A method for constructing a cybersecurity compliance knowledge base based on multidimensional semantic association includes the following steps:

[0027] Step 101: Obtain the information security standard text and extract compliant entities to construct a compliance rule subgraph representing compliance constraint relationships. At the same time, obtain the configuration data of the target network environment and parse the topology associations to construct an asset entity subgraph representing asset connection relationships.

[0028] Step 102: Configure the heterogeneous graph attention calculation unit and drive the heterogeneous graph attention calculation unit to calculate the compliance semantic representation vector based on the adjacency matrix of the compliance rule subgraph. Use the compliance semantic representation vector as the constraint feature quantity and perform feature propagation pre-calculation along the topological edge of the asset entity subgraph.

[0029] Step 103: Configure the symbolic logic gating module and embed the logic decision link of the symbolic logic gating module into the weight allocation logic of the heterogeneous graph attention calculation unit;

[0030] Step 104: Extract the logical topological connection features between adjacent asset nodes in the asset entity subgraph. Based on the compliance semantic representation vector, match and determine the logical topological connection features. When the determination result triggers the mutual exclusion constraint defined in the compliance rule subgraph, use the symbolic logic gating module to superimpose the negative infinite polarization operator on the corresponding unnormalized attention score, so as to make the edge weight coefficient converge to 0 through normalization processing, thereby blocking the topological diffusion of the node representation vector with compliance risk identification in the asset entity subgraph during the tensor calculation stage.

[0031] Preferably, the process of blocking using the symbolic logic gating module in step 104 further includes: step 1041, monitoring the traffic exchange attribute data between the first node and the second node in the asset entity subgraph; step 1042, retrieving the access control benchmark value for the traffic exchange attribute data in the compliance rule subgraph; step 1043, when the traffic exchange attribute data does not meet the access control benchmark value, the symbolic logic gating module generates a polarization control quantity to shut down the logical path for feature aggregation from the first node to the second node.

[0032] Preferably, the process of constructing the compliance rule subgraph in step 101 includes: step 1011, identifying the compliance subject, controlled object, and logical constraint operator in the information security standard text; step 1012, establishing the semantic association edge between the compliance subject and the controlled object, and assigning an initial weight to the semantic association edge according to the constraint strength of the logical constraint operator, forming a constraint topology of the multidimensional semantic space.

[0033] Preferably, the process of constructing the asset entity subgraph in step 101 includes: step 1013, parsing the routing table entries, firewall access control lists, and virtual LAN identifiers in the configuration data; step 1014, converting the routing table entries and firewall access control lists into logical edge features between asset nodes in the asset entity subgraph, and using the virtual LAN identifier to define the logical isolation domain attribute to which the asset node belongs.

[0034] Preferably, the calculation of the compliance semantic representation vector in step 102 includes: step 1021, using the hierarchical aggregation mechanism of the heterogeneous graph attention calculation unit to perform self-attention transformation calculation on the compliance rule subgraph; step 1022, extracting the hidden layer tensor after the self-attention transformation calculation as the compliance semantic representation vector.

[0035] Preferably, the normalized edge weight coefficients in step 104 The calculation logic follows the following rules: ,in, Let be the edge weight coefficient between asset node i and asset node j. δ is the original attention score, M is the negative infinite polarization operator, and δ is the binary triggering coefficient output by the symbolic logic gating module. When the mutual exclusion constraint is triggered, δ takes the value of 1.

[0036] Preferably, after step 104, the method further includes: step 1044, periodically acquiring incremental configuration data of the target network environment and updating the asset entity subgraph; step 1045, comparing the distribution status of edge weight coefficients before and after the update, and when it is found that the edge weight coefficients that originally converged to 0 have become non-zero values, outputting a compliance item failure warning signal.

[0037] Preferably, the method further includes: step 105, constructing a joint loss function, which includes a topological loss term characterizing the accuracy of subgraph isomorphic matching and a compliance deviation term characterizing the semantic constraint alignment depth; step 106, using backpropagation logic to adjust the weight parameters of the heterogeneous graph attention calculation unit to minimize the joint loss function and complete the adaptive optimization of the knowledge base.

[0038] Preferably, blocking the topological diffusion of the node representation vector with compliance risk identifier in step 104 includes: step 1046, defining the spatial boundary of the representation vector of compliant nodes in the asset entity subgraph; step 1047, ensuring that the offset of the feature tensor with violation mark to the representation vector of the compliant node during the aggregation process does not exceed the preset robustness threshold by limiting the edge weight coefficient.

[0039] Preferably, a method for constructing a compliance knowledge base based on multidimensional semantic association further includes: step 107, extracting high-risk access paths in the asset entity subgraph according to the distribution of edge weight coefficients; step 108, mapping the high-risk access paths to the corresponding clauses of the compliance standard text, and generating a compliance audit report with asset logical location information.

[0040] Example 1: In an automated audit scenario for compliance of a financial transaction system encompassing a core database server, business middleware, and internet access gateway, this technical solution addresses the risk diffusion problem caused by the failure of logical isolation across security bureaus. It simulates real audit conditions by constructing a topology graph containing 5000 heterogeneous asset nodes. The internet access gateway points to the core database area through multi-level unauthorized routing, forming a hidden topology dependency. The solution acquires the compliance standard text and identifies the compliance subjects, controlled objects, and logical constraint operators. Semantic association edges are established between compliance subjects and controlled objects, and initial weights are assigned to these semantic association edges based on the constraint strength of the logical constraint operators, constructing a compliance rule subgraph representing compliance constraint relationships. The solution then executes the above text... During this identification process, the system loads an entity extraction pipeline that combines large-scale pre-trained word vectors with a conditional random field architecture. It uses a sequence labeling mechanism to automatically extract compliant and controlled entity words from text blocks and uses a dependency parsing algorithm to deeply extract verb-object and subject-verb dependency features from the context. This allows business expressions such as access prohibition or logical isolation to be directly extracted into structured logical constraint operators. The system parses routing table entries, firewall access control lists, and virtual LAN identifiers in the target network environment configuration data. It converts routing table entries and firewall access control lists into logical edge features between asset nodes in the asset entity subgraph and uses virtual LAN identifiers to define the logical isolation domain attributes to which asset nodes belong, thus constructing an asset entity subgraph that represents the asset connection relationships.

[0041] A heterogeneous graph attention computation unit is configured to perform self-attention transformation computation on the compliance rule subgraph using a hierarchical aggregation mechanism. The hidden layer tensor after the self-attention transformation computation is extracted as the compliance semantic representation vector. Before performing interactive computation on the aggregated features, in order to eliminate the differences in the underlying representations of different modalities, the system calls a large language model with a bidirectional encoder structure to extract high-dimensional text sequence features of the compliance rule nodes. Simultaneously, a feedforward neural network containing a multilayer perceptron is constructed to nonlinearly increase the dimensionality of the discrete network configuration parameters of the asset nodes. Finally, a cross-normalization mechanism is used to jointly fine-tune the output matrices on both sides to ensure that the two sets of features with different physical dimensions are stably projected onto the shared matrix. Within the 768-dimensional non-Euclidean semantic vector space, the logical decision link of the symbolic logic gating module is embedded into the weight allocation logic of the heterogeneous graph attention calculation unit. Logical topological connection features between adjacent asset nodes in the asset entity subgraph are extracted. Traffic exchange attribute data between the first and second nodes in the asset entity subgraph is monitored, and access control baseline values ​​for this traffic exchange attribute data are retrieved from the compliance rule subgraph. When this traffic exchange attribute data triggers the mutual exclusion constraint defined in the compliance rule subgraph, a negative infinite polarization operator is superimposed on the corresponding unnormalized attention score using the symbolic logic gating module, causing the edge weight coefficients to converge to 0. The specific calculation formula is as follows: ,in, Let be the edge weight coefficient between asset node i and asset node j. M is the unnormalized attention scalar, M is the negative infinite polarization operator, and δ is the binary triggering coefficient output by the symbolic logic gating module. When the mutual exclusion constraint is triggered, δ takes the value of 1.

[0042] After the feature propagation calculation preparation is completed, the parameter optimization stage of the heterogeneous graph attention calculation unit is initiated. The joint loss function is calculated based on the cross-entropy information metric and penalty function theory. The calculation formula is as follows: The joint loss function L is composed of a weighted sum of two terms, the first of which is the topology loss term, where... The label represents a binary connection label between asset node i and asset node j in the asset entity subgraph. The value is 1 when a connection exists and 0 otherwise. The term represents the marginal weight coefficients of the model output; the latter term is the compliance deviation term, where... This represents the binarized trigger coefficient corresponding to the k-th logical topology connection feature. The Euclidean distance between the extracted first and second node representation vectors is represented by λ, which is the coefficient for adjusting the penalty intensity allocation. The system collects the average node degree in the asset entity subgraph, sets the reciprocal of the average node degree as the value of λ, calculates the partial derivative of the joint loss function L with respect to the internal tensor matrix, and performs backpropagation to correct the learnable parameters of each layer. When the node representation vector with compliance risk identification undergoes multi-layer convolution iteration in the asset entity subgraph, the binarization trigger coefficient δ is switched to 1, and the corresponding edge weight coefficient is adjusted. By zeroing out tensor operations, the spread path of illegal features along topological edges to nodes in the core database area is blocked. This solves the problem of feature smoothing and contagion in graph neural networks during multi-layer convolution, enabling the illegal access path of the Internet access gateway to be accurately located without generating false alarms for other legitimate asset nodes in the core area. Ultimately, this improves the accuracy of compliance risk perception in complex environments.

[0043] Example 2: For a core financial transaction system with 5000 logical asset nodes, in a verification experiment for automated auditing of compliance with information security standards, to demonstrate the technical effectiveness of embedding the symbolic logic gating module into the weight allocation logic of the heterogeneous graph attention computation unit, a graph tensor processing platform based on a large-scale parallel computing architecture was constructed. This platform includes at least 32GB of video memory to support feature matrix operations on large-scale heterogeneous graphs. The experimental data comes from a compliance benchmark item library extracted according to the GB / T22239-2019 information security standard and anonymized heterogeneous asset configuration logs from a real production environment, covering original input items such as routing policies, firewall access control lists, and logical isolation domain definitions. The experiment also included testing the number of layers in the heterogeneous graph attention neural network. The technical trade-off in this design lies in the optimal balance between the breadth of coverage of multi-level topological neighborhood information and the risk of feature oversmoothing in deep network computation. By establishing an inverse proportional mapping model between the number of network layers L and the convergence rate of node feature variance, the value of L is determined to be 3 when the semantic information entropy increment of the node representation vector in the third-order neighborhood tends to be stable and the slope of variance decrease has not reached the preset saturation threshold. This ensures the depth of cross-security path perception while avoiding excessive collapse of the computation space. In specific deployment, this inverse proportional mapping model is set by an empirical formula as the result of dividing the positive proportional constant by the absolute value of the first-order difference of the feature variance of all compliant nodes in two adjacent computation layers and rounding down. This positive proportional constant is uniquely calibrated in the model initialization stage based on the initial measurement of information of global topological features of the graph. As the computation progresses to the third layer, the absolute value of the first-order difference decreases sharply due to the feature convergence effect, triggering the set saturation threshold boundary.

[0044] During the experiment, an experimental group consisting of the scheme of this invention and a control group using a standard heterogeneous graph attention network but lacking a symbolic logic gating module were established. Gaussian white noise with a signal-to-noise ratio of 20dB was actively injected into the edge features of the asset entity subgraph, while a configuration drift pulse with a simulated frequency of 50Hz was used to simulate the uncertainty of sensor data acquisition in a real industrial environment. During feature propagation in the experimental group, when a logical conflict occurs between the flow exchange attribute data between the first and second nodes and the mutual exclusion constraint in the compliance rule subgraph, the binarized trigger coefficient δ output by the symbolic logic gating module switches from 0 to 1. At this time, the extracted unnormalized attention scalar... By superimposing the negative infinite polarization operator M, the corresponding edge weight coefficients are observed. Within the precision range of floating-point operations, the system rapidly converges to 0, synchronously executing a vector space boundary limiting procedure. Based on the theory of local geometric continuity, it extracts the baseline representation vector output by compliant nodes in the first iteration round, obtains the corresponding representation vectors of all compliant adjacent nodes within their respective first-order topological neighborhoods, calculates the Euclidean distance between the aforementioned compliant adjacent node representation vectors and the baseline representation vector, and extracts the maximum value in the distance calculation result set as a preset robustness threshold. During the current convolution iteration cycle, it monitors the spatial offset of the input feature tensor from the baseline representation vector of compliant nodes. When the spatial offset exceeds the robustness threshold, it physically truncates the feature transmission link of the corresponding risk node, blocking the non-compliant feature tensor from participating in the adjacent node aggregation calculation and summation step. The specific action instructions of this physical truncation mechanism on the computational kernel are reflected in the system's construction of the bottom-level adjacency matrix of the entire graph. Directly exceeding authority by calling the memory address forces the element values ​​in the adjacency matrix representing the propagation link of the risk node to be overwritten to an absolute zero state. This hard isolation at the memory addressing level blocks any data flow of violation feature data during sparse matrix multiplication, constructing an equivalent logical and physical disconnection without changing the actual wiring of the server cluster. In contrast, under the same conflict conditions, the edge weight coefficient of the control group remained between 0.12 and 0.28, causing the violation risk feature to spread smoothly along the topological edges. Recording the node representation vectors of the two sample groups after the third-order convolution iteration, the vector space offset of the core compliance area node in the control group reached 0.76, showing obvious feature pollution, while the vector space offset of the experimental group remained stable below 0.04, confirming the suppressive effect of the underlying polarization blocking mechanism on neighborhood compliance false alarms.

[0045] Gradient verification experiments were conducted for different node scales, collecting audit accuracy and system response latency data for 1000, 3000, and 5000 nodes respectively, and observing the nonlinear evolution characteristics of computational performance under boundary conditions. When the connection density of asset nodes exceeded the preset performance inflection point, i.e., the average degree was greater than 60, the compliance risk perception precision of the control group dropped sharply from the initial 82.5% to 58.4%, demonstrating the failure of traditional graph models in dense topologies. This is due to the excessive fusion of compliant and non-compliant nodes in the vector space caused by unconstrained feature aggregation. In contrast, under the extreme load condition with a connection density of 100, the experimental group, relying on the symbolic logic gating module, achieved a higher accuracy. The rigid adjudication of the attention mechanism maintains an audit precision rate of approximately 95.2%, and the growth trend of inference time shows a linear correlation with node size. The stability of this method in addressing the inherent defects of graph computing architectures is verified through the comparison of the above performance indicators. This experiment fully reproduced the execution process of the method for constructing a compliance knowledge base based on multi-dimensional semantic association through original configuration data collection, heterogeneous graph topology construction, symbolic logic trigger condition calibration, and weight convergence measurement after polarization operator intervention. The experimental results show that, under dynamic conditions with injected environmental noise and topology disturbances, the collaborative operation of the symbolic logic gating module and the heterogeneous graph attention computing unit can improve the edge weight coefficients between asset nodes. The determination results of the logical constraint operator generate deterministic state transitions; the audit results obtained by the experimental group are more than 35% better than those of the control group in terms of precision, and show extremely high logical stability in rigid compliance scenarios such as core database protection, thus proving that integrating symbolic logic constraints into the deep learning inference architecture can effectively eliminate neighborhood pollution caused by feature smoothing.

[0046] Example 3: In the scenario of security bureau logical isolation auditing in a multi-tenant cloud-native environment, there is a technical bottleneck of semantic mismatch between cross-security bureau access policies and dynamically changing network routing table entries. In addition, frequent updates to asset topology cause violations to spread implicitly through unauthorized routing paths. The solution is to obtain the information security compliance standard text and identify the compliant entities and controlled objects in it, and establish semantic association edges between compliant entities and controlled objects. The solution is to parse the dynamic routing table records and virtual LAN identifiers in the cloud environment configuration data, use a hash mapping algorithm to convert IP addresses into unique node identifiers in the asset entity subgraph, and construct an edge feature tensor with a hidden layer dimension of 128 based on the physical connection hop count.

[0047] The heterogeneous graph attention computation unit is driven by a network with a layer number L of 3. It aggregates the neighborhood features of the asset entity subgraphs using the neural network and calculates the unnormalized attention scalar. Initiate the calibration process for the negative infinite polarization operator M, select the lower limit of single-precision floating-point numbers on the computing platform, and set the value of M to [value missing]. When the symbolic logic gating module detects that the traffic exchange attribute data between nodes violates the mutual exclusion constraint, the binary trigger coefficient δ output by the symbolic logic gating module changes from 0 to 1. The specific calculation formula is as follows: ,in, Let be the edge weight coefficient between asset node i and asset node j. Let M be the unnormalized attention scalar, and M be the negative infinite polarization operator, whose value is... δ is the binarization trigger coefficient, i is the index of the first node, and j is the index of the second node; since The numerical value is affected by the negative infinite polarization operator M, resulting in overflow before the exponential operation, and the output edge weight coefficients... By keeping the value at 0, the spread of violation characteristics along the topological path to compliant neighboring nodes is cut off, enabling the location of hidden violation access paths. Moreover, even with a 40% increase in asset topology density, the audit precision rate did not decrease.

[0048] Example 4: In scenarios involving the alignment of compliance texts across different industry sectors, such as the financial industry and government intranets, to ensure the initial weights of semantically related edges in the compliance rule subgraph are portable across domains, the system runs an offline calibration program for the compliance corpus before deployment. It selects an industry reference corpus containing 50,000 security and compliance legal provisions, calculates the association density value between logical constraint operators and controlled objects using statistical mutual information, and uses this association density value as a basis to map it to the real number interval of 0 to 1 using a linear normalization function, thereby determining the initial weights of the semantically related edges. Furthermore, when encountering clauses with multiple meanings, it compares the conditional probabilities under different contexts and selects the connection path corresponding to the maximum conditional probability as the feature edge of the compliance rule subgraph.

[0049] When a new hardware computing node is connected to the target network environment, to eliminate the impact of hardware architecture differences on the tensor truncation effect, the system initiates a pre-deployment calibration process. This process monitors the minimum representable negative value threshold of single-precision floating-point numbers under the current processor instruction set environment, and sets a baseline value for the negative infinite polarization operator M based on 0.8 times this minimum representable negative value threshold. A self-test script including 100 simulated interference pulses is then run to track the edge weight coefficients in real time. In the register overflow state after triggering the mutual exclusion constraint, if the detected numerical residual is greater than Then The negative infinite polarization operator M is adjusted to reduce the step size until the output value returns to zero after the exponential normalization operation, so that the weight shutdown response delay of the symbolic logic gating module is less than 10ms under different memory architectures.

[0050] Example 5: In a large-scale government cloud environment compliance dynamic audit scenario operating across quarters, the system encounters the risk of semantic library lag caused by periodic revisions of regulatory clauses and frequent changes in cloud-native assets. This leads to a distribution deviation between the initial weights of semantically related edges in the compliance rule subgraph and the current audit conditions. To address the timeliness issue of the calculation model in a dynamic environment, the system initiates a semantic drift monitoring program based on a sliding time window during the operation of the asset entity subgraph. The length of the sliding time window is set to 30 calendar days, and the newly extracted compliance subject vectors within the current window and the benchmark vectors in the industry reference corpus are extracted. The distribution offset ΔS of the semantically related edges is calculated using cosine similarity. The specific calculation logic is as follows: Where ΔS is the distribution offset. This is the representation vector of the compliant entity within the current window. This serves as a benchmark vector in the industry reference corpus, and is used as a quantitative criterion for judging the stability of knowledge base features.

[0051] When the distribution offset ΔS is detected to be greater than the reconstruction threshold of 0.15 for three consecutive sampling periods, the system drives the heterogeneous graph attention calculation unit to recalibrate the topological connection features of the compliance rule subgraph using the traffic exchange attribute data collected in the most recent natural day, and simultaneously corrects the conditional probability distribution in the semantic disambiguation dictionary, so that the binarization trigger coefficient δ will produce a state jump when facing the newly defined mutual exclusion constraint. Moreover, the knowledge base is adaptively synchronized within 24 hours after the regulatory version is updated, so that the accuracy fluctuation of compliance audit for core exchange links is maintained within 1%, avoiding false alarms in the neighborhood caused by model expiration, and realizing the continuous effectiveness of the compliance knowledge base during operation.

[0052] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for constructing a compliance knowledge base based on multi-dimensional semantic association, characterized in that, Includes the following steps: Step 101: Obtain the information security standard text and extract compliant entities to construct a compliance rule subgraph representing compliance constraint relationships. At the same time, obtain the configuration data of the target network environment and parse the topology associations to construct an asset entity subgraph representing asset connection relationships. Step 102: Configure the heterogeneous graph attention calculation unit and drive the heterogeneous graph attention calculation unit to calculate the compliance semantic representation vector based on the adjacency matrix of the compliance rule subgraph. Use the compliance semantic representation vector as the constraint feature quantity and perform feature propagation pre-calculation along the topological edge of the asset entity subgraph. Step 103: Configure the symbolic logic gating module and embed the logic decision link of the symbolic logic gating module into the weight allocation logic of the heterogeneous graph attention calculation unit; Step 104: Extract the logical topological connection features between adjacent asset nodes in the asset entity subgraph. Based on the compliance semantic representation vector, match and determine the logical topological connection features. When the determination result triggers the mutual exclusion constraint defined in the compliance rule subgraph, use the symbolic logic gating module to superimpose the negative infinite polarization operator on the corresponding unnormalized attention score, so as to make the edge weight coefficient converge to 0 through normalization processing, thereby blocking the topological diffusion of the node representation vector with compliance risk identification in the asset entity subgraph during the tensor calculation stage.

2. The method for constructing a compliance knowledge base based on multi-dimensional semantic association according to claim 1, characterized in that, The process of blocking using the symbolic logic gating module in step 104 further includes: step 1041, monitoring the traffic exchange attribute data between the first node and the second node in the asset entity subgraph; step 1042, retrieving the access control benchmark value for the traffic exchange attribute data in the compliance rule subgraph; step 1043, when the traffic exchange attribute data does not meet the access control benchmark value, the symbolic logic gating module generates a polarization control quantity to shut down the logical path for feature aggregation from the first node to the second node.

3. The method for constructing a compliance knowledge base based on multi-dimensional semantic association according to claim 1, characterized in that, The process of constructing the compliance rule subgraph in step 101 includes: step 1011, identifying the compliance subject, controlled object and logical constraint operator in the information security standard text; step 1012, establishing the semantic association edge between the compliance subject and the controlled object, and assigning an initial weight to the semantic association edge according to the constraint strength of the logical constraint operator to form a constraint topology of the multidimensional semantic space.

4. The method for constructing a compliance knowledge base based on multi-dimensional semantic association according to claim 1, characterized in that, The process of constructing the asset entity subgraph in step 101 includes: step 1013, parsing the routing table entries, firewall access control lists, and virtual LAN identifiers in the configuration data; step 1014, converting the routing table entries and firewall access control lists into logical edge features between asset nodes in the asset entity subgraph, and using the virtual LAN identifier to define the logical isolation domain attribute to which the asset node belongs.

5. The method for constructing a compliance knowledge base based on multi-dimensional semantic association according to claim 1, characterized in that, Step 102, which calculates the compliance semantic representation vector, includes: Step 1021, using the hierarchical aggregation mechanism of the heterogeneous graph attention calculation unit to perform self-attention transformation calculation on the compliance rule subgraph; Step 1022, extracting the hidden layer tensor after the self-attention transformation calculation as the compliance semantic representation vector.

6. The method for constructing a compliance knowledge base based on multi-dimensional semantic association according to claim 1, characterized in that, Step 104 is followed by: Step 1044, periodically acquiring incremental configuration data of the target network environment and updating the asset entity subgraph; Step 1045, comparing the distribution of edge weight coefficients before and after the update, and when it is found that the edge weight coefficients that originally converged to 0 have become non-zero values, outputting a compliance item failure warning signal.

7. The method for constructing a compliance knowledge base based on multi-dimensional semantic association according to claim 1, characterized in that, The method further includes: step 105, constructing a joint loss function, which includes a topological loss term characterizing the accuracy of subgraph isomorphic matching and a compliance deviation term characterizing the semantic constraint alignment depth; step 106, using backpropagation logic to adjust the weight parameters of the heterogeneous graph attention calculation unit to minimize the joint loss function and complete the adaptive optimization of the knowledge base.

8. The method for constructing a compliance knowledge base based on multi-dimensional semantic association according to claim 1, characterized in that, Step 104, blocking the topological diffusion of the representation vector of a node with compliance risk identification, includes: Step 1046, defining the spatial boundary of the representation vector of compliant nodes in the asset entity subgraph; Step 1047, by limiting the edge weight coefficient, ensuring that the offset of the feature tensor with violation mark generated by the representation vector of the compliant node during the aggregation process does not exceed the preset robustness threshold.

9. The method for constructing a compliance knowledge base based on multi-dimensional semantic association according to claim 1, characterized in that, The method further includes: step 107, extracting high-risk access paths in the asset entity subgraph based on the distribution of edge weight coefficients; step 108, mapping the high-risk access paths to the corresponding clauses of the information security standard text, and generating a compliance audit report with asset logical location information.

Images