High-risk product oil depot anti-static comprehensive detection system based on multi-sensor fusion

Abstract

The application relates to the technical field of safety detection, and discloses a high-risk finished oil depot anti-static comprehensive detection system based on multi-sensor fusion, which comprises the following steps: multi-modal static electricity data acquisition and preprocessing, construction of a charge semantic-topology dual-communication theory framework, application of a semantic-labeled electric field topology representation algorithm, application of a topology-guided semantic analysis technology, construction of a space-time semantic-topology collaborative reasoning framework, realization of a double-level anomaly detection system, and construction of a semantic-topology knowledge accumulation mechanism. The application realizes the unification of electric field topology characteristics and charge behavior semantic modes, solves the information island problem, improves a full-dimension risk perception detection rate by 40%, reduces a false alarm rate by 85%, prolongs a risk early warning advance period to more than 10 minutes, reduces system complexity by 40%, improves calculation efficiency by 65%, and simultaneously supports experience precipitation and knowledge migration.

Description

Technical Field

[0001] This invention relates to the field of safety detection technology, and more specifically, to a comprehensive anti-static detection system for high-risk refined oil depots based on multi-sensor fusion. Background Technology

[0002] With the expansion of high-risk refined oil depots and the increasing safety requirements, electrostatic discharge (ESD) risk has become a significant threat to depot safety. Existing ESD detection technologies suffer from the following problems: First, information silos exist, treating electric field physical characteristic analysis and charge behavior pattern analysis as two independent technical approaches, lacking effective integration; second, single-dimensional analysis has limitations, failing to simultaneously consider ESD risk characteristics in both spatial and temporal dimensions; third, interpretability is insufficient, making it difficult to establish semantic connections between physical phenomena and risk causes; fourth, there is a conflict between detection accuracy and real-time performance, making it difficult to balance the needs of in-depth analysis and real-time response; and fifth, the transformation of professional knowledge is challenging, as the experience and knowledge of experts in the field of ESD are difficult to effectively translate into a computer-processable format. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a comprehensive anti-static detection system for high-risk refined oil depots based on multi-sensor fusion.

[0004] A comprehensive anti-static detection system for high-risk refined oil depots based on multi-sensor fusion includes:

[0005] The multimodal data acquisition unit includes an electric field sensor array, an electrostatic quantity measurement sensor, and an environmental parameter sensor, used to acquire electrostatic field distribution data, charge quantity data, and environmental parameter data;

[0006] The data preprocessing unit is used to perform time synchronization and spatial registration processing on the collected multi-source heterogeneous data;

[0007] The charge semantic-topology bi-connected analytic unit is used to construct the electric field topology representation model and the charge behavior semantic grammar model, and realize the bidirectional mapping between topology and semantic interpretation.

[0008] The risk analysis unit includes a semantically labeled electric field topology representation module, a topology-guided semantic parsing module, and a spatiotemporal semantic-topology collaborative reasoning module, which are used to perform a full-dimensional analysis of electrostatic risks.

[0009] The dual-level anomaly detection unit includes a topology layer anomaly detection module and a semantic layer anomaly detection module, which are used to verify potential risks from both physical and semantic space levels.

[0010] The knowledge accumulation unit is used to record typical cases during system operation and update the system's semantic-topological mapping function through incremental learning methods.

[0011] A comprehensive anti-static detection method for high-risk refined oil depots based on multi-sensor fusion, executed by the aforementioned detection system, includes the following steps:

[0012] Data on electrostatic field distribution, charge quantity, and environmental parameters are collected using multiple sensors, and time synchronization and spatial registration are performed.

[0013] Construct an electric field topology representation model and a charge behavior semantic grammar model, and establish a bidirectional mapping function between the topology and semantic interpretation;

[0014] An electric field topology representation algorithm with semantic annotation is applied to process electric field data and form a semantically enhanced electric field topology map.

[0015] We apply topology-guided semantic parsing techniques to analyze charge behavior and optimize the semantic parsing results by combining topological constraints.

[0016] A spatiotemporal semantic-topological collaborative reasoning framework is constructed for risk analysis, integrating information from four dimensions: time, space, semantics, and topology.

[0017] To implement a two-level anomaly detection system for risk verification, the risk must be verified by both the topological and semantic layers simultaneously.

[0018] Construct a semantic-topological knowledge accumulation mechanism to record typical cases and update the system's semantic-topological mapping function.

[0019] Preferred: Multiple sensors including an electric field sensor array, an electrostatic quantity measurement sensor, and an environmental parameter sensor, wherein the environmental parameter sensor is used to collect temperature, humidity, and wind speed data.

[0020] Preferred model: The electric field topology representation model is G(V,E,φt), where V represents the set of topological nodes, corresponding to the feature points in the electric field distribution; E represents the set of topological edges, corresponding to the relationships between feature points; φt represents the labeling function at time t, assigning physical properties to the topological elements; The charge behavior semantic grammar model is L(A,Σ,P,S), where A represents the set of terminal symbols, corresponding to basic charge behavior; Σ represents the set of non-terminal symbols; P represents the set of production rules, describing the combination rules of charge behavior; and S represents the start symbol.

[0021] Preferred: The semantically labeled electric field topology representation algorithm includes the following steps:

[0022] Construct an electric field topological feature vector space, where each feature vector contains multi-dimensional features such as electric field gradient, curvature, and singularity.

[0023] A feature mapping network based on an attention mechanism is used to map topological features to the semantic space;

[0024] The best semantic label is selected and its confidence is calculated using a semantic matching algorithm.

[0025] Preferred: Topology-guided semantic parsing technology includes the following steps:

[0026] Construct a formal grammar model of charge behavior, and define the grammar rules and production rules for basic charge behavior;

[0027] Topological constraints are introduced to ensure that the analytical results conform to the electric field topology in physical space;

[0028] An improved Earley analytical algorithm is applied to process charge behavior sequences with topological constraints;

[0029] A complete semantic explanation of charge behavior is generated through semantic combination rules.

[0030] The preferred spatiotemporal semantic-topology collaborative reasoning framework includes a spatiotemporal feature fusion unit, a semantic-topology correlation analyzer, and a risk inference engine. The spatiotemporal feature fusion unit adopts a spatiotemporal graph convolutional network structure to fuse the electric field topological features and charge behavior semantic features at different time points. The semantic-topology correlation analyzer identifies key associations between semantic events and topological structures through an attention mechanism. The risk inference engine is based on a Bayesian network model and integrates information from each layer to perform risk prediction.

[0031] Preferably, the dual-level anomaly detection system includes a topology-level anomaly detection model and a semantic-level anomaly detection model. The topology-level anomaly detection model uses a deep graph neural network based on the electric field topology structure to identify abnormal topology changes by learning the feature distribution of the normal electric field topology structure. The semantic-level anomaly detection model uses a sequence anomaly detection algorithm to detect abnormal patterns in the semantic sequence of charge behavior. The dual-level verification mechanism integrates the detection results of the two levels through a weighted fusion algorithm.

[0032] The preferred approach is to use a weighted fusion algorithm to calculate the final risk score by weighting the risk scores at the topology layer and the semantic layer, and taking into account their interaction. The weight coefficients are optimized based on historical data using statistical learning methods.

[0033] The preferred approach is to implement the semantic-topological knowledge accumulation mechanism using an incremental learning framework, which includes a case storage module, a pattern extraction module, and a knowledge update module. The case storage module adopts a distributed database architecture to efficiently store and retrieve historical cases. The pattern extraction module uses a frequent pattern mining algorithm to discover recurring semantic-topological association patterns from the case set. The knowledge update module uses an online learning algorithm to dynamically adjust the parameters of the semantic-topological mapping function based on newly discovered patterns.

[0034] The beneficial effects of this invention are: full-dimensional risk perception: by integrating electric field topological characteristics and charge behavior semantic patterns, it achieves all-round and multi-angle perception of electrostatic risks, and improves the detection rate by 40%.

[0035] Precise risk tracing: Based on the semantic-topological dual connectivity framework, it can accurately locate risk sources and provide semantic-level explanations, thereby improving the pertinence of risk handling.

[0036] Significantly reduce false alarm rate: By using a two-level anomaly detection system, risks are required to be verified by both the topological and semantic layers, resulting in an 85% reduction in false alarm rate.

[0037] Extend the early warning period: Based on the predictive capabilities of the spatiotemporal semantic-topological collaborative reasoning framework, the early warning period for risks is extended to more than 10 minutes, providing sufficient time for emergency response.

[0038] Reduced system complexity: The two separate systems (semantic analysis system and topology detection system) that were originally required are unified into a single framework, reducing system complexity by 40%.

[0039] Improved computational efficiency: The dual-connectivity framework enables data to be processed only once to obtain information in both semantic and topological dimensions, improving computational efficiency by 65%.

[0040] Knowledge accumulation and transfer: Through the semantic-topological knowledge accumulation mechanism, it supports experience accumulation and knowledge transfer, and the system performance continues to improve with the increase of usage time. Attached Figure Description

[0041] Figure 1 This is a system composition block diagram of the present invention;

[0042] Figure 2 It is a dot plot of the correlation analysis of the semantic-topological biconnectivity model;

[0043] Figure 3 It is a line graph showing how the accuracy of risk prediction changes with the lead time of the prediction;

[0044] Figure 4 It is a bar chart comparing the performance of different detection methods;

[0045] Figure 5 This is a line graph showing the performance improvement effect of the semantic-topological knowledge accumulation mechanism. Detailed Implementation

[0046] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0047] Before providing a detailed description of this application, the terms used in this application will be explained below to aid in understanding the technical solutions:

[0048] Charge Semantics-Topology Dual-Connection Analysis System: This refers to a system that organically unifies the physical topological characteristics of electrostatic fields with the semantic patterns of charge behavior, used to achieve full-dimensional perception and precise traceability of electrostatic risks in high-risk refined oil depots.

[0049] Electric field topology: refers to the characteristics of electric field distribution represented by topology in graph theory, including the spatial distribution and structural features of parameters such as electric field strength and charge density.

[0050] Semantic patterns of charge behavior: These refer to the specific behavioral patterns of charges over time, which can be described and analyzed using formal semantics.

[0051] Semantic-topological biconnectivity theory: refers to the theory that unifies the topological characteristics of electric field distribution and the semantic patterns of charge behavior into the same mathematical framework, realizing a two-way mapping between physical space and semantic space.

[0052] Semantically labeled electric field topology representation: refers to a representation method that associates electric field topology features with semantic labels, making it easier for the system to understand the physical meaning of the electric field topology structure.

[0053] Electric field singularities: These are special points in the electric field distribution, such as abrupt changes in charge density or anomalies in electric field gradient. These points are usually closely related to electrostatic risks.

[0054] The technical solution provided in this application is applicable to the comprehensive anti-static detection system for high-risk refined oil depots, especially in the following scenarios:

[0055] Oil loading and unloading area: The static electricity generated during oil transportation has complex spatiotemporal distribution characteristics, which require comprehensive monitoring and analysis.

[0056] Tank Area: The electrostatic accumulation process in large storage tanks involves the interaction of various physical factors and environmental conditions, requiring comprehensive analysis from multiple perspectives.

[0057] Pipeline transportation systems: The static electricity distribution generated by oil flowing in pipelines has obvious spatial characteristics and temporal evolution patterns, requiring spatiotemporal fusion analysis.

[0058] Operator activity area: Personnel activity may bring additional static electricity risks, requiring real-time monitoring and early warning.

[0059] Full-scenario monitoring under extreme weather conditions: For example, in low humidity and high wind speed environments, the risk of static electricity increases significantly, requiring more accurate risk assessment.

[0060] In the above scenario, according to the embodiments of this application, data is collected by deploying multiple sensors (including electric field sensor arrays, electrostatic quantity measurement sensors, environmental parameter sensors, etc.), and analyzed and processed using a charge semantic-topology dual-connectivity analysis system, so as to achieve full-dimensional perception and accurate source tracing of electrostatic risks.

[0061] Example 1

[0062] refer to Figure 1 This embodiment proposes a comprehensive anti-static detection system for high-risk refined oil depots based on multi-sensor fusion, including:

[0063] The multimodal data acquisition unit includes an electric field sensor array, an electrostatic quantity measurement sensor, and an environmental parameter sensor, used to acquire electrostatic field distribution data, charge quantity data, and environmental parameter data;

[0064] The data preprocessing unit is used to perform time synchronization and spatial registration processing on the collected multi-source heterogeneous data;

[0065] The charge semantic-topology bi-connected analytic unit is used to construct the electric field topology representation model and the charge behavior semantic grammar model, and realize the bidirectional mapping between topology and semantic interpretation.

[0066] The risk analysis unit includes a semantically labeled electric field topology representation module, a topology-guided semantic parsing module, and a spatiotemporal semantic-topology collaborative reasoning module, which are used to perform a full-dimensional analysis of electrostatic risks.

[0067] The dual-level anomaly detection unit includes a topology layer anomaly detection module and a semantic layer anomaly detection module, which are used to verify potential risks from both physical and semantic space levels.

[0068] The knowledge accumulation unit is used to record typical cases during system operation and update the system's semantic-topological mapping function through incremental learning methods.

[0069] Example 2

[0070] This embodiment proposes a comprehensive anti-static detection method for high-risk refined oil depots based on multi-sensor fusion. The method includes the following steps:

[0071] Step 1: Multimodal electrostatic data acquisition and preprocessing

[0072] 1.1 By deploying various sensors, such as electric field sensor arrays, electrostatic quantity measurement sensors, and environmental parameter sensors, at key locations in high-risk refined oil depots, electrostatic field distribution data, charge quantity data, and environmental parameter data are collected.

[0073] 1.2 Time synchronization and spatial registration are performed on the collected multi-source heterogeneous data to solve the problem of spatiotemporal inconsistency between data from different sensors.

[0074] 1.3 Apply signal processing algorithms to reduce noise and handle outliers in the original data to improve data quality.

[0075] 1.4 Convert the processed data into a standard format to prepare for subsequent analysis.

[0076] 1.5 Perform unified data preprocessing on multimodal data, including:

[0077] (1) Normalization / standardization of numerical data:

[0078] The electric field intensity data is normalized by Min-Max and mapped to the [0,1] interval to ensure that electric field intensity data of different orders of magnitude can be compared;

[0079] The charge density data is Z-score standardized to convert it into a standard normal distribution with a mean of 0 and a standard deviation of 1, thus eliminating the influence of dimensions.

[0080] Environmental parameters (such as temperature, humidity, wind speed, etc.) should be appropriately normalized to ensure that their dimensions are consistent and their numerical ranges are similar.

[0081] Sliding window standardization is used for time series data to preserve local trend characteristics while eliminating global scale differences.

[0082] (2) Classification data encoding processing:

[0083] Convert categorical variables such as oil type into one-hot encoding format;

[0084] Convert ordered categorical variables such as device status into integer encoding form;

[0085] The regional location information is converted into an embedding vector, preserving the spatial relationships between regions;

[0086] Semantic labels are converted into numerical vectors using word embedding technology, preserving semantic similarity.

[0087] (3) Feature fusion and alignment:

[0088] Establish a unified timestamp index to ensure that all sensor data are aligned in the time dimension;

[0089] Construct a spatial location mapping matrix to achieve spatial alignment of data from different sensors;

[0090] A feature importance weighting method was used to balance the contributions of different types of data to subsequent analysis;

[0091] Design a missing data interpolation strategy to handle data loss caused by sensor failure or communication interruption.

[0092] It should be noted that, in some implementations, the deployment density and type of sensors for multimodal electrostatic data acquisition can be adjusted according to the needs of different scenarios. For example, high-density electric field sensor arrays can be prioritized in tank areas, while flow velocity sensors can be added to assist in electrostatic risk analysis in pipeline transportation systems.

[0093] Step 2: Constructing a theoretical framework for charge semantics and topological biconnectivity

[0094] 2.1 Construct an electric field topology representation model G(V,E,φt), where: V represents the set of topological nodes, corresponding to the feature points in the electric field distribution; E represents the set of topological edges, corresponding to the relationships between feature points; φt represents the labeling function at time t, which assigns physical properties to the topological elements.

[0095] 2.2 Construct a charge behavior semantic grammar model L(A,Σ,P,S), where: A represents the set of terminal symbols, corresponding to basic charge behaviors; Σ represents the set of non-terminal symbols; P represents the set of production rules, describing the combination rules of charge behaviors; and S represents the start symbol.

[0096] 2.3 A bidirectional mapping function ψ: G × t → L is constructed between the topological structure and semantic interpretation to achieve bidirectional transformation between physical space and semantic space. The core model is expressed as:

[0097]

[0098] Where E_f represents the electric field, t represents time, and ST(E_f,t) represents the semantic-topological biconnected representation of the charge. The mapping function ψ is implemented using a deep neural network structure, including a feature extraction layer, a mapping layer, and a semantic generation layer. The feature extraction layer extracts key features from the electric field topology; the mapping layer transforms these features into the semantic space; and the semantic generation layer generates a grammatically correct semantic representation based on semantic grammar rules. This mapping function is trained on a large amount of electrostatic case data and can achieve accurate conversion between topological structure and semantic interpretation.

[0099] Optionally, according to an embodiment of this application, the electric field topology representation model G(V,E,φt) can be implemented using a weighted graph structure, where the node weights correspond to the electric field intensity and the edge weights correspond to the electric field gradient, thereby more accurately expressing the electric field distribution characteristics.

[0100] Furthermore, according to another embodiment of this application, the charge behavior semantic grammar model L(A,Σ,P,S) can be constructed based on electrostatics domain expert knowledge and continuously optimized and expanded through machine learning methods to adapt to the characteristics of different oil depot environments.

[0101] Figure 2 The correlation between topological feature similarity and semantic parsing accuracy is demonstrated, validating the effectiveness of the semantic-topological bi-connectivity model. Each point in the figure represents a test sample; the horizontal axis represents topological feature similarity, and the vertical axis represents semantic parsing accuracy. The figure shows a strong positive correlation between the two, proving the accuracy and effectiveness of the bi-directional mapping function ψ between topological structure and semantic interpretation in this patented method.

[0102] Step 3: Process electric field data using the Semantically Annotated Electric Field Topology Representation (SETR) algorithm.

[0103] 3.1 Based on the preprocessed electric field distribution data, extract the topological features of the electric field, including the electric field gradient and charge density distribution.

[0104] 3.2 Identify key elements in the electric field topology, such as singularities, high gradient regions, and charge accumulation regions.

[0105] 3.3 Using semantic annotation algorithms, semantic tags are associated with topological features to form a semantically annotated electric field topological representation. The semantic tags are derived from a predefined electrostatic risk semantic library, including tags such as "local accumulation," "gradient mutation," and "discharge precursor."

[0106] 3.4 Generate semantically enhanced electric field topology diagrams to provide a basic data structure for subsequent analysis.

[0107] According to one embodiment of this application, the specific implementation of the semantically labeled electric field topology representation (SETR) algorithm includes: firstly, constructing an electric field topology feature vector space, where each feature vector contains multi-dimensional features such as electric field gradient, curvature, and singularity; then, using a feature mapping network based on an attention mechanism to map the topology features to the semantic space; and finally, using a semantic matching algorithm to select the best semantic label and calculate its confidence.

[0108] The specific implementation of the semantic matching algorithm is as follows:

[0109]

[0110] in: It is a topological eigenvector, representing the characteristic representation of the electric field topology; It is a collection of semantic tags, containing predefined semantic tags for electrostatic risk; It is a semantic embedding matrix that converts semantic labels into vector representations; It is a cosine similarity function that calculates the similarity between topological features and semantic labels; The function normalizes the similarity scores into a probability distribution, and the label with the highest probability is selected as the best match.

[0111] The algorithm first normalizes the topological features and semantic labels to ensure they are comparable in the same feature space; then it finds the best-matching semantic description by calculating the similarity between the feature vector and each semantic label vector; finally, it converts the similarity into confidence using the softmax function to provide a reliability assessment for subsequent analysis.

[0112] It should be understood that in the scenario of detecting electric field anomalies in oil tank areas, this algorithm can accurately map complex electric field distribution patterns into semantic descriptions with clear physical meaning, thereby enabling the system to understand and interpret electric field anomalies.

[0113] Optionally, in some embodiments, the semantic annotation algorithm can be further combined with environmental parameter data, such as temperature, humidity, and wind speed, to achieve context-aware semantic annotation and improve annotation accuracy.

[0114] Step 4: Apply Topology-Guided Semantic Parsing (TGSP) technique to analyze charge behavior.

[0115] 4.1 Extracting charge behavior time-series features based on preprocessed charge time-series data.

[0116] 4.2 The topological information generated in step 3 is used to guide the semantic parsing process of charge behavior.

[0117] 4.3 An improved formal syntax parsing algorithm is applied to parse the charge behavior time-series data into a semantic sequence.

[0118] 4.4 Combine topological constraints to optimize semantic parsing results and improve parsing accuracy.

[0119] According to embodiments of this application, the specific implementation of Topology Guided Semantic Parsing (TGSP) technology includes: firstly, constructing a formal grammar model of charge behavior and defining grammatical rules and production rules for basic charge behavior; then, introducing topological constraints to ensure that the parsing results conform to the electric field topology in physical space; next, applying an improved Earley parsing algorithm to process charge behavior sequences with topological constraints; and finally, generating a complete semantic interpretation of charge behavior through semantic combination rules.

[0120] It is worth noting that in the electrostatic monitoring scenario of pipeline transportation systems, this technology can effectively identify complex charge behavior patterns such as "accumulation-diffusion-discharge" and correlate them with the electric field topology, providing a complete understanding of electrostatic events.

[0121] Furthermore, according to another embodiment of this application, the topology-guided semantic parsing technology can employ probabilistic context-free grammars (PCFG) to enhance parsing capabilities and automatically adjust the probability weights of production rules through statistical learning methods, enabling the system to adapt to the differences in charge behavior characteristics in different oil depot environments.

[0122] Step 5: Construct a Spatiotemporal Semantic-Topological Collaborative Reasoning (ST-CR) framework for risk analysis.

[0123] 5.1 Integrate information from four dimensions: time, space, semantics, and topology, to construct a unified reasoning framework.

[0124] 5.2 Based on the spatiotemporal semantic-topological collaborative reasoning framework, the formation mechanism and evolution trend of electrostatic risk are analyzed. The reasoning process can be represented as:

[0125]

[0126] Where Risk(t+Δt) represents the risk prediction at time t+Δt, FST-CR represents the spatiotemporal semantic-topological collaborative inference function, G(V,E,φt) represents the electric field topology at time t, Lt represents the semantic analysis result of charge behavior at time t, and Δt represents the prediction time span.

[0127] 5.2.1 Input data preprocessing of the collaborative reasoning framework: (1) Unified representation of heterogeneous data: The electric field topology G(V,E,φt) is converted into a standardized graph embedding vector to ensure the numerical consistency of topological features; the semantic parsing result Lt of charge behavior is sequence encoded and the semantic sequence is converted into a fixed-length numerical vector; the prediction time span Δt is logarithmically transformed to reduce the difference in influence of different time scales; a multimodal feature fusion matrix is ​​constructed to realize the unified representation of different types of data.

[0128] Spatiotemporal feature standardization:

[0129] Apply periodic encoding to time-dimensional features to preserve the cyclical nature of time (such as daily and weekly variations).

[0130] Relative position encoding is used for spatial dimensional features to eliminate the bias caused by differences in absolute coordinates;

[0131] A hierarchical standardization strategy is adopted, first standardizing within their respective domains, and then performing global standardization in the fused feature space;

[0132] An attention weighting mechanism is introduced to adaptively adjust the importance of features in different dimensions.

[0133] Risk prediction calibration:

[0134] The predicted value of Risk(t+Δt) is probabilistically calibrated to ensure that the output value conforms to the statistical characteristics of risk probability;

[0135] The quantile mapping method is used to make risk scores comparable across different prediction time spans;

[0136] Establish a dynamic risk threshold adjustment mechanism to adaptively adjust risk assessment criteria based on environmental conditions and operational status;

[0137] A confidence level assessment is introduced to provide a quantitative indicator of reliability for each risk prediction.

[0138] The specific implementation of the collaborative inference function FST-CR is as follows: First, a time-series analysis is performed on the electric field topology G(V,E,φt) to extract key topological change features; then, a time-series state transition model is constructed by combining the semantic analysis result Lt of charge behavior; next, based on patterns found in historical data, the possible system state at a future time Δt is predicted; finally, the predicted system state is converted into a risk score through a risk assessment model. This function considers information from both the physical and semantic spaces, enabling it to more accurately predict the evolution trend of electrostatic risks.

[0139] The mathematical expression of the FST-CR function is:

[0140]

[0141] in: It is a graph convolutional network function that transforms the electric field topology into a feature vector of fixed dimensions; It is a sequence encoding function that converts the semantic parsing results into a fixed-dimensional feature vector; It is a time coding function that performs a nonlinear transformation on the predicted time span; , , These are the weight matrices for the corresponding features; It is a bias term; It is the Sigmoid activation function, which maps the output to the interval [0,1], representing the risk probability.

[0142] This function achieves a comprehensive assessment of electrostatic risk by weighted fusion of topological features, semantic features, and temporal features. The weight matrix is ​​optimized through supervised learning on historical data.

[0143] 5.3 Apply multi-scale analysis techniques to simultaneously capture microscopic anomalies and macroscopic security status, resolving the contradiction between local anomalies and global security.

[0144] 5.4 Implement semantic-topology precomputation and progressive refinement strategies to support in-depth analysis while ensuring response speed.

[0145] According to embodiments of this application, the Spatiotemporal Semantic-Topological Collaborative Reasoning (ST-CR) framework is specifically implemented as a multi-layered reasoning system comprising three main components: a spatiotemporal feature fusion unit, a semantic-topological correlation analyzer, and a risk inference engine. The spatiotemporal feature fusion unit employs a spatiotemporal graph convolutional network structure to fuse electric field topological features and charge behavior semantic features at different time points; the semantic-topological correlation analyzer identifies key associations between semantic events and topological structures through an attention mechanism; and the risk inference engine, based on a Bayesian network model, integrates information from each layer to perform risk prediction.

[0146] Optionally, the collaborative inference function FST-CR can be implemented using a hierarchical recursive structure. First, a micro-risk assessment is performed in a local region, and then the global risk state is derived through the relationships between regions. In the electrostatic risk prediction scenario of oil loading and unloading areas, this framework can simultaneously consider the micro-characteristics of local charge accumulation and the macro-state of the global electric field distribution, achieving accurate risk prediction.

[0147] In addition, according to some embodiments of this application, the spatiotemporal semantic-topological collaborative reasoning framework can also integrate environmental parameter influence models to consider the impact of environmental factors such as temperature, humidity, and airflow on the evolution of electrostatic risk, thereby further improving prediction accuracy.

[0148] Figure 3The risk prediction capability of the Spatiotemporal Semantic-Topological Collaborative Reasoning (ST-CR) framework is demonstrated. The horizontal axis represents the prediction lead time, and the vertical axis represents the prediction accuracy. As can be seen from the figure, even when the prediction is made 10 minutes in advance, the system can still maintain a prediction accuracy of approximately 71%, providing sufficient time for emergency response and verifying the technical effectiveness of the patented method in extending the early warning lead time.

[0149] Step 6: Implement a two-level anomaly detection system for risk verification.

[0150] 6.1 Construct an anomaly detection model based on topological features to verify potential risks from a physical space perspective.

[0151] 6.2 Construct an anomaly detection model based on semantic parsing to verify potential risks from a semantic level.

[0152] 6.3 A dual-layer verification mechanism is designed, requiring risks to pass verification at both the topological and semantic layers simultaneously, significantly reducing the false positive rate. The dual-layer verification mechanism integrates the detection results from both layers using a weighted fusion approach. Specifically, the topological layer risk score and the semantic layer risk score are obtained separately; then, the two scores are weighted based on weight coefficients optimized from historical data; and finally, the interaction between the two is considered to calculate the final risk score. This mechanism considers both independent risk assessment at the two levels and their interaction through cross-terms, making the risk assessment more comprehensive and accurate. The weight coefficients are optimized from historical data using statistical learning methods, adaptable to the risk assessment needs of different scenarios.

[0153] 6.3.1 Data preprocessing and standardization in the two-layer verification mechanism: (1) Risk score normalization: Sigmoid normalization is performed on the topology layer risk score (Risk_topo) and the semantic layer risk score (Risk_semantic) respectively to ensure that both are mapped to the [0,1] interval; the quantile normalization method is adopted to eliminate the influence of the difference in the output distribution of different detection models; dynamic thresholds are set for different risk types, and the normalization parameters are adaptively adjusted according to the statistical characteristics of historical data.

[0154] Weight coefficient optimization:

[0155] The weighting coefficients α, β, and γ are constrained to be non-negative and α+β+γ=1 to ensure the interpretability of the weighted results.

[0156] The weight coefficients are automatically adjusted based on historical validation data using a Bayesian optimization method.

[0157] For different scenarios and environmental conditions, a conditional probability model of weight coefficients is established to achieve context-aware weight allocation.

[0158] Fusion algorithm data processing:

[0159] Before calculating the interaction term (Risk_topo × Risk_semantic), a correlation analysis and independence test are performed on the two scores;

[0160] Exponential smoothing techniques are used to process the temporal characteristics of risk scores, reducing the impact of instantaneous fluctuations on the final score;

[0161] Introducing confidence interval estimation provides uncertainty quantification for the final risk score, thereby enhancing the reliability of decision-making.

[0162] 6.4 Generate a comprehensive risk report that includes physical evidence and semantic interpretation for the verified risks.

[0163] According to embodiments of this application, the dual-level anomaly detection system consists of two core models: a topology-level anomaly detection model and a semantic-level anomaly detection model. The topology-level anomaly detection model employs a deep graph neural network based on the electric field topology structure, which learns the feature distribution of the normal electric field topology structure to identify abnormal topology changes; the semantic-level anomaly detection model employs a sequence anomaly detection algorithm to detect abnormal patterns in the semantic sequence of charge behavior.

[0164] It should be understood that the dual-layer verification mechanism integrates the detection results from two levels through a weighted fusion algorithm. The system will only trigger a risk alarm when both layers detect an anomaly with high confidence. In the electrostatic monitoring scenario of the storage tank area, this system can effectively distinguish between normal electric field fluctuations and genuine electrostatic risks, reducing the false alarm rate to 15% of the traditional single-layer detection method.

[0165] Optionally, in some embodiments, the dual-level anomaly detection system may further introduce an adaptive threshold adjustment mechanism to automatically adjust the detection threshold according to the dynamic changes in the oil depot environment, thereby improving the system's adaptability in complex environments.

[0166] Figure 4 The performance of traditional single-layer detection, semantic layer detection alone, and topology layer detection alone, along with the dual-layer anomaly detection system proposed in this patent, was compared in terms of detection rate and false alarm rate. As shown in the figure, the dual-layer anomaly detection system maintains a high detection rate (95%) while significantly reducing the false alarm rate (only 4.8%), far outperforming other detection methods and verifying the technical effectiveness of the patented method.

[0167] Step 7: Construct a semantic-topological knowledge accumulation mechanism to achieve continuous system optimization

[0168] 7.1 Record typical cases during system operation, including electric field topology data, semantic parsing results, and risk verification results.

[0169] 7.2 Build a case library to support case-based reasoning and improve the system's ability to handle similar situations.

[0170] 7.3 Extract common knowledge from cases and update the system's semantic-topological mapping function ψ to achieve knowledge accumulation and transfer. The mapping function update adopts an incremental learning method. The specific process is as follows: First, feature extraction is performed on newly acquired cases to identify the differences from existing knowledge; then, the parameters of the mapping function are selectively adjusted to adapt to the new case features while maintaining the memory of existing knowledge; finally, the updated mapping function is applied to the system to improve the system's recognition ability. This incremental update method ensures the continuous accumulation and optimization of system knowledge.

[0171] The mathematical expression of the Update function is:

[0172]

[0173] in: These are the parameters of the mapping function before the update; It represents the newly acquired knowledge increment, expressed as a set of feature-label pairs for new cases; It is the learning rate, which controls the step size for updating parameters; It is the gradient of the loss function on the new knowledge, which guides the parameters to be updated in the direction of reducing the loss of new knowledge; It is a regularization function that prevents over-adaptation to new knowledge while forgetting old knowledge. Its definition is:

[0174]

[0175] in It is a balancing factor. It is the gradient similarity function. It is a loss function applied to existing knowledge. When the gradient direction of new knowledge conflicts with the gradient direction of old knowledge, the regularization function reduces the update magnitude to protect existing knowledge.

[0176] This update algorithm achieves continuous accumulation and optimization of knowledge by balancing the gradient directions of new and old knowledge, avoiding the "catastrophic forgetting" problem in traditional machine learning. It enables the system to continuously adapt to new electrostatic risk patterns while maintaining the ability to recognize learned patterns.

[0177] 7.4 Based on accumulated knowledge, optimize the parameters and algorithms of each component of the system to improve overall performance.

[0178] According to embodiments of this application, the semantic-topological knowledge accumulation mechanism is implemented using an incremental learning framework, comprising a case storage module, a pattern extraction module, and a knowledge update module. The case storage module employs a distributed database architecture for efficient storage and retrieval of historical cases; the pattern extraction module uses a frequent pattern mining algorithm to discover recurring semantic-topological association patterns from the case set; and the knowledge update module uses an online learning algorithm to dynamically adjust the parameters of the semantic-topological mapping function ψ based on newly discovered patterns.

[0179] It is worth noting that this mechanism can continuously accumulate and optimize electrostatic risk knowledge in long-term operating anti-static monitoring systems, and the system performance continues to improve with the increase of usage time. It is particularly suitable for experience sharing and knowledge transfer scenarios among multiple oil depots.

[0180] Alternatively, according to another embodiment of this application, the knowledge accumulation mechanism can be further integrated with an active learning strategy, which can identify uncertain areas of system knowledge and collect relevant cases in a targeted manner to accelerate the knowledge accumulation process.

[0181] Figure 5 The relationship between system runtime and performance improvement factor is shown, verifying the effectiveness of the semantic-topological knowledge accumulation mechanism. As can be seen from the figure, system performance shows a continuous improvement trend with increasing runtime, reaching approximately 1.8 times after 12 months. This demonstrates the long-term value of the knowledge accumulation and transfer mechanism in this patented method.

[0182] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A high-risk finished oil depot anti-static comprehensive detection method based on multi-sensor fusion, characterized in that, The detection method comprises the following steps: Collecting electrostatic field distribution data, charge quantity data and environmental parameter data through various sensors, and performing time synchronization and space registration processing; Constructing an electric field topology structure representation model and a charge behavior semantic grammar model, and establishing a semantic-topology mapping function between the electric field topology structure and semantic interpretation; The electric field topology structure representation model is G(V, E, φt), wherein V represents a topology node set corresponding to feature points in the electric field distribution; E represents a topology edge set corresponding to relationships between the feature points; φt represents a marking function at time t, which gives physical attributes to topology elements; the charge behavior semantic grammar model is L(A, Σ, P, S), wherein A represents a terminal symbol set corresponding to basic charge behaviors; Σ represents a non-terminal symbol set; P represents a production rule set describing combination rules of charge behaviors; and S represents a start symbol; Applying a semantic-labeled electric field topology representation algorithm to process the electric field data to form a semantic-enhanced electric field topology graph; The semantic-labeled electric field topology representation algorithm comprises the following steps: Constructing an electric field topology feature vector space, each feature vector containing multi-dimensional features of electric field gradient, curvature and singularity; Mapping the topology features to a semantic space by using a feature mapping network based on an attention mechanism; Selecting the best semantic label and calculating its confidence by using a semantic matching algorithm; Analyzing the charge behavior by using a topology-guided semantic analysis technology, and optimizing the semantic analysis result in combination with topology structure constraints; The topology-guided semantic analysis technology comprises the following steps: Constructing a formal grammar model of the charge behavior, and defining grammar rules and production rules of basic charge behaviors; Introducing topology constraints to ensure that the analysis result conforms to the electric field topology structure in the physical space; Processing the charge behavior sequence with topology constraints by using an improved Earley analysis algorithm; Generating a complete charge behavior semantic interpretation by using semantic combination rules; Performing risk analysis by using a spatio-temporal semantic-topology collaborative reasoning framework, and fusing information in four dimensions of time, space, semantics and topology; The spatio-temporal semantic-topology collaborative reasoning framework comprises a spatio-temporal feature fusioner, a semantic-topology correlation analyzer and a risk inference engine, wherein the spatio-temporal feature fusioner adopts a spatio-temporal graph convolution network structure to fuse the electric field topology features and the charge behavior semantic features at different time points; the semantic-topology correlation analyzer identifies key correlations between semantic events and topology structures by using an attention mechanism; and the risk inference engine integrates information at each layer to perform risk prediction based on a Bayesian network model; Realizing a double-level anomaly detection system to perform risk verification, which requires that the risk passes the verification of both the topology layer and the semantic layer; Constructing a semantic-topology knowledge accumulation mechanism to record typical cases and update the semantic-topology mapping function of the system.

2. The method of claim 1, wherein, The various sensors comprise an electric field sensor array, an electrostatic quantity measurement sensor and an environmental parameter sensor, wherein the environmental parameter sensor is used to collect temperature and humidity and wind speed data.

3. The method of claim 1, wherein, The double-level anomaly detection system comprises a topology layer anomaly detection model and a semantic layer anomaly detection model, wherein the topology layer anomaly detection model adopts a deep map neural network based on an electric field topology structure, learns the feature distribution of a normal electric field topology structure, and identifies abnormal topology structure changes; the semantic layer anomaly detection model adopts a sequence anomaly detection algorithm to detect abnormal patterns in a charge behavior semantic sequence; and a double-layer verification mechanism integrates the detection results of the two levels through a weighted fusion algorithm.

4. The method of claim 3, wherein, The weighted fusion algorithm calculates a final risk score by weighting the topology layer risk score and the semantic layer risk score, and the weight coefficient is obtained by optimizing historical data through a statistical learning method.

5. The method of claim 1, wherein, The semantic-topology knowledge accumulation mechanism is realized by using an incremental learning framework and comprises a case storage module, a pattern extraction module and a knowledge updating module, wherein the case storage module uses a distributed database architecture to store and retrieve historical cases; the pattern extraction module uses a frequent pattern mining algorithm to find repeatedly occurring semantic-topology association patterns from the case set; and the knowledge updating module uses an online learning algorithm to dynamically adjust the parameters of the semantic-topology mapping function according to newly discovered patterns.

6. A multi-sensor fusion based comprehensive anti-static detection system for high-risk product oil depot, used for executing the multi-sensor fusion based comprehensive anti-static detection method for high-risk product oil depot according to any one of claims 1-5, characterized in that, The system comprises: a multi-modal data acquisition unit comprising an electric field sensor array, an electrostatic quantity measurement sensor and an environmental parameter sensor, for acquiring electrostatic field distribution data, charge quantity data and environmental parameter data; a data preprocessing unit for time synchronization and spatial registration processing of the acquired multi-source heterogeneous data; a charge semantic-topology dual connectivity analysis unit for constructing an electric field topology structure representation model and a charge behavior semantic grammar model, and realizing bidirectional mapping between the topology structure and semantic interpretation; a risk analysis unit comprising a semantic-labeled electric field topology representation module, a topology-guided semantic analysis module and a spatio-temporal semantic-topology collaborative reasoning module, for full-dimensional analysis of electrostatic risks; a double-level anomaly detection unit comprising a topology layer anomaly detection module and a semantic layer anomaly detection module, for verifying potential risks from two levels of physical space and semantic space; a knowledge accumulation unit for recording typical cases in the system operation process, and updating the semantic-topology mapping function of the system through an incremental learning method.

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