Risk and hidden danger management system and method based on big data

Abstract

The invention provides a risk hidden danger management system and method based on big data, and belongs to the technical field of risk hidden danger management, and the system comprises a quantum optimization risk modeling module, a multi-modal edge intelligent sensing module, a dynamic causal inference engine module and a risk game adversarial network module. According to the system and the method provided by the invention, the core problems of response lag, one-sided hidden danger identification, low resource allocation efficiency and the like of a traditional risk management system in a complex environment are solved. The computing power bottleneck of real-time analysis of massive heterogeneous data is overcome by fusing quantum computing and edge intelligence technologies; the tracing problem of the multi-factor coupling risk is solved by utilizing dynamic causal reasoning; the sample limitation of rare risk prediction is broken through by means of the generative adversarial network; and finally, a complete closed-loop system from risk perception, intelligent research and judgment to autonomous intervention is constructed, full-life-cycle management and control of hidden, dynamic and cross-dimension risks are realized, and the safety prevention and control capability in a high-risk scene is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of risk hidden danger management, and in particular to a risk hidden danger management system and method based on big data. Background Art

[0002] Existing risk management systems and methods generally have the following problems when facing complex, dynamic, and high-dimensional risk scenarios:

[0003] 1. Limitations of data processing:

[0004] Traditional systems rely on structured data and fixed rules, making it difficult to effectively process multimodal, heterogeneous data (such as the fusion analysis of vibration, images, and logs). This results in incomplete extraction of hidden danger features. This is manifested in low utilization of unstructured data, reliance on manually defined risk thresholds, and an inability to adapt to dynamic environments.

[0005] 2. Real-time performance and computing power bottlenecks:

[0006] Cloud-based centralized processing analysis systems suffer from high latency, making it difficult to meet the millisecond-level response requirements of high-risk scenarios. Specifically, factory equipment failure warnings often lead to false alarms or missed alarms due to data transmission delays;

[0007] Training complex risk models requires a lot of computing power (such as deep neural networks).

[0008] 3. Staticization of causal relationships:

[0009] Traditional methods rely on fixed causal logic and are unable to dynamically capture changes in risk transmission pathways. Specifically, they can only analyze historically known risk patterns, failing to account for emerging hazards; and they ignore the impact of temporal and spatial dimensions (such as the correlation between equipment aging and seasonal temperatures).

[0010] 4. Insufficient ability to predict rare risks:

[0011] Statistical models that rely on historical data lack the ability to generalize to low-probability, high-risk events. Specifically, the accuracy of predicting adverse events is extremely low, and the resulting emergency response plans often fall outside of actual physical constraints.

[0012] 5. Disconnect between resource allocation and execution:

[0013] Risk analysis results are disconnected from intervention measures, relying on manual decision-making, leading to resource misallocation. Specifically, maintenance personnel frequently handle low-priority alarms, ignoring critical hazards; and automated equipment cannot directly receive risk instructions, requiring manual processing.

[0014] Therefore, this field is in urgent need of a technical solution that can solve the above problems.

[0015] The information disclosed in this background technology section is only intended to enhance understanding of the overall background of the invention and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the Invention

[0016] The purpose of the present invention is to provide a technical solution that can solve the core problems of traditional risk management systems in complex environments, such as delayed response, one-sided hidden danger identification, and inefficient resource allocation.

[0017] To achieve the above object, the present invention provides the following solutions:

[0018] A risk hidden danger management system based on big data, comprising:

[0019] Quantum optimization risk modeling module, which uses quantum annealing algorithms to perform ultra-parallel optimization on massive amounts of heterogeneous risk data;

[0020] Multimodal edge intelligent perception module, used to deploy biomimetic sensor networks to collect multimodal signals of physical environment, human behavior and network data in real time;

[0021] A dynamic causal reasoning engine module, which dynamically updates the causal chain of hidden dangers through a hybrid architecture of Bayesian networks and deep reinforcement learning;

[0022] The risk game adversarial network module is used to generate a risk adversarial network, generate virtual hidden danger scenarios and compete with real data to optimize the system's generalization ability for unknown risks.

[0023] A risk hidden danger management method based on big data, comprising:

[0024] Step 1: Quantum risk modeling: Encode the hidden danger data into a quantum bit sequence and map it to the Ising model of the quantum annealer; solve the risk minimization objective function through quantum annealing;

[0025]

[0026] Among them H risk is the Hamiltonian of the risk system. The smaller the value, the lower the system risk. ij represents the quantum coupling strength between potential risk nodes i and j, reflecting the risk transmission probability; σ i and σ j is the quantum spin state of the potential danger nodes i and j, and its value range is {-1, +1}; h i is the local risk bias field of node i, the direct impact of the external environment on a single hidden danger;

[0027] Step 2: Multimodal edge perception:

[0028] The simulated sensor node converts the environmental vibration signal into a sparse pulse sequence through pulse coding;

[0029] The edge gateway runs a lightweight SNN model to detect abnormal pulse patterns in real time;

[0030] Step 3: Dynamic Causal Inference:

[0031] The causal engine extracts spatiotemporal data from the data lake and constructs a time-varying causal graph with each edge weight W. t Updated over time:

[0032] W t =αW t-1 +(1-α)·RL-Adjustment;

[0033] Among them, W t is the causal edge weight at time t, representing the impact strength of the hidden danger, with a value range of [0, 1]. α is the historical weight decay coefficient, controlling the inertia of the causal relationship, with a value range of (0, 1). RL-Adjustment is the reinforcement learning adjustment amount, calculated by the DQN agent based on the intervention effect feedback and dynamically adjusted by the reinforcement learning agent based on the historical intervention effect.

[0034] Step 4: Risk confrontation training;

[0035] The generator of Risk-GAN synthesizes virtual hidden danger events, and the discriminator outputs a risk score S∈[0,1];

[0036] Optimize the system by minimizing the adversarial loss function:

[0037] Γ=E[logD(x)]+E[log(1-D(G(z)))]+λ·Causal-Consistency

[0038] Where D(x) is the score of the discriminator on the real risk data x, with a value range of (0, 1). The closer to 1, the more credible the risk; G(z) is the virtual hidden danger event synthesized by the generator based on the noise z; z is the latent space noise, usually following a Gaussian distribution; λ is the causal consistency penalty coefficient, which forces the generated event to conform to physical logic; Causal-Consistency is the causal consistency loss, which calculates the KL divergence between the generated event and the causal graph;

[0039] Step 5: Autonomous intervention execution:

[0040] The system triggers the optimal intervention strategy and completes closed-loop control through the execution terminal;

[0041] Optimal strategy selection:

[0042]

[0043] Among them, a* is the optimal intervention action; is the state prediction of the potential node i after executing action a; the value range is {-1, +1}; Entropy i is the risk entropy of node i.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The big data-based risk hidden danger management system provided by the present invention includes: a quantum optimization risk modeling module, a multimodal edge intelligent perception module, a dynamic causal reasoning engine module, and a risk game adversarial network module. The system and method provided by the present invention solve the core problems of traditional risk management systems in complex environments, such as delayed response, one-sided hazard identification, and inefficient resource allocation. By integrating quantum computing and edge intelligence technology, the computing power bottleneck of real-time analysis of massive heterogeneous data has been overcome; dynamic causal reasoning has been used to solve the problem of tracing the source of multi-factor coupled risks; and adversarial generative networks have been used to break through the sample limitations of rare risk predictions. Finally, a complete closed-loop system from risk perception, intelligent analysis to autonomous intervention has been established, achieving full life cycle management and control of hidden, dynamic, and cross-dimensional risks, significantly improving safety and control capabilities in high-risk scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 This is a system architecture diagram provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0049] The purpose of the present invention is to provide a system and method that can solve the core problems of traditional risk management systems in complex environments, such as delayed response, one-sided hidden danger identification, and inefficient resource allocation.

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] Example 1:

[0052] This example uses the risk management and control of a chemical park as an application scenario. The park contains multiple high-risk equipment such as reactors, gas pipelines, and storage tanks. Traditional systems have difficulty achieving accurate prevention and control due to high data heterogeneity, complex risk transmission, and large response delays. This example solves the following problems through a risk management system based on big data:

[0053] (1) Real-time fusion analysis of multi-source data (vibration, temperature, gas concentration, operation log);

[0054] (2) Mining the implicit causal relationship between equipment failure and human operation errors;

[0055] (3) Active prediction of rare combined risks (such as leakage + static spark).

[0056] like Figure 1 As shown, the system consists of:

[0057] 1. Edge intelligent perception layer:

[0058] Bionic SNN sensor node:

[0059] It is deployed at key locations such as the outer wall of the reactor and pipeline connections, and has a built-in pulse neural network chip.

[0060] Function: Collect vibration signals (10kHz sampling rate) and convert the original waveform into a sparse pulse sequence through pulse coding (e.g. abnormal high-frequency vibration → pulse frequency > 200Hz).

[0061] Data transmission: Only the pulse pattern summary is uploaded (1kbps bandwidth occupied), not the original waveform data (100kbps).

[0062] Quantum Optimization Modeling Module:

[0063] Hardware: D-Wave 2000Q quantum annealer (cloud-based deployment).

[0064] Input data: pulse feature matrix (edge layer summary) + historical maintenance records + operator behavior log.

[0065] Modeling process:

[0066] Encode potential risk nodes (such as "aging of reactor seal" and "employees not wearing anti-static wristbands") into quantum bits;

[0067] Defining the coupling strength J ij(For example, the positive correlation weight between seal aging and temperature increase is +0.7);

[0068] The lowest energy state is solved through quantum annealing, and a list of high-risk nodes is output (for example, the risk entropy of "seal ring aging" reaches 0.92).

[0069] 3. Dynamic Causal Inference Engine:

[0070] Causal graph update logic:

[0071] Initial causal edge: based on expert knowledge (e.g., “temperature exceeds standard → seal life is reduced”);

[0072] Real-time update: When an operational error does not lead to leakage, the edge weight (W t from 0.8→0.6).

[0073] Counterfactual intervention model:

[0074] Assuming that the "employee not wearing an anti-static wristband" node is eliminated, the predicted fire probability decreases by 67%.

[0075] 4. Risk-GAN

[0076] Generator input: potential noise vector z + current causal graph structure.

[0077] Synthetic case: Generate a virtual event of "pipeline corrosion 80% + employee accidentally presses the emergency stop button + monitoring system power failure".

[0078] The discriminator outputs a score of D = 0.91 (high risk), triggering the system to update the causal graph and generate an emergency plan.

[0079] 5. Autonomous intervention execution terminal:

[0080] Explosion-proof inspection robots and automatic valve controllers.

[0081] Execution logic: If the quantum model outputs "seal ring aging" as the highest entropy node, the robot will go to the location and perform ultrasonic thickness detection; after confirming aging, it will automatically close the associated pipeline valve and notify maintenance.

[0082] The implementation steps of the method are:

[0083] Step 1: Data collection and edge processing:

[0084] The SNN sensor monitors the reactor vibration in real time and detects a sudden increase in frequency to 250Hz (normal threshold 200Hz), generating a pulse sequence of “0101” (encoding abnormal shock);

[0085] The edge gateway aggregates the temperature sensor data (85°C → pulse code "1001"), compresses it, and uploads it to the cloud.

[0086] Step 2: Quantum Risk Modeling:

[0087] The cloud maps pulse codes and employee inspection records (1 missed inspection today) into quantum bits σ1, σ2;

[0088] Quantum annealing solves σ1=+1 (seal ring risk activation), σ2=-1 (employee operation risk suppression); the output needs to prioritize the aging of the seal ring.

[0089] Step 3: Dynamic causal tracing:

[0090] The causal engine found that the correlation weights of "seal ring aging", "temperature exceeding the standard", and "employee missed inspection" were 0.9 and 0.6 respectively;

[0091] Simulating the counterfactual: If the cooling water flow rate is increased (lowering the temperature to 70°C), the leakage probability decreases by 58%;

[0092] Generates the command: "Start the backup cooling pump".

[0093] Step 4: Risk confrontation training:

[0094] Risk-GAN synthetic events: "90% seal aging + cooling pump failure + employee absenteeism";

[0095] The discriminator score D=0.95, and the system adds the event to the training library to enhance the ability to identify complex risks.

[0096] Step 5: Closed-loop intervention implementation:

[0097] The automatic control system starts the standby cooling pump and the temperature drops to 72°C;

[0098] The robot inspection confirmed that the seal thickness was 1.2mm (critical value 1.5mm) and marked it as an urgent maintenance task;

[0099] The central control screen showed that the risk entropy dropped from 0.92 to 0.31, and the alarm was lifted.

[0100] Effect of the embodiment:

[0101] Improved efficiency: It only takes 380ms from data collection to intervention execution (traditional systems take 5.2s).

[0102] Cost reduction: 12 fewer unexpected shutdowns per year, saving approximately RMB 5 million in losses;

[0103] Enhanced safety: Successfully issued early warnings for three hidden leakage incidents, avoiding major accidents.

[0104] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0105] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A risk management system based on big data, characterized in that: include: Quantum optimization risk modeling module, which uses quantum annealing algorithms to perform ultra-parallel optimization on massive amounts of heterogeneous risk data; Multimodal edge intelligent perception module, used to deploy biomimetic sensor networks to collect multimodal signals of physical environment, human behavior and network data in real time; A dynamic causal reasoning engine module, which dynamically updates the causal chain of hidden dangers through a hybrid architecture of Bayesian networks and deep reinforcement learning; The risk game adversarial network module is used to generate a risk adversarial network, generate virtual hidden danger scenarios and compete with real data to optimize the system's generalization ability for unknown risks.

2. A risk management method based on big data, characterized in that: include: Step 1: Quantum risk modeling: Encode the hidden danger data into a quantum bit sequence and map it to the Ising model of the quantum annealer; Solve the risk minimization objective function through quantum annealing; Among them H risk is the Hamiltonian of the risk system. The smaller the value, the lower the system risk. ij represents the quantum coupling strength between potential risk nodes i and j, reflecting the risk transmission probability; σ i and σ j is the quantum spin state of the potential danger nodes i and j, and its value range is {-1, +1}; h i is the local risk bias field of node i, the direct impact of the external environment on a single hidden danger; Step 2: Multimodal edge perception: The simulated sensor node converts the environmental vibration signal into a sparse pulse sequence through pulse coding; The edge gateway runs a lightweight SNN model to detect abnormal pulse patterns in real time; Step 3: Dynamic Causal Inference: The causal engine extracts spatiotemporal data from the data lake and constructs a time-varying causal graph with each edge weight W. t Updated over time: W t =αW t-1 +(1-α)·RL-Adjustment; Among them, W t is the causal edge weight at time t, representing the impact strength of the hidden danger, with a value range of [0, 1]. α is the historical weight decay coefficient, controlling the inertia of the causal relationship, with a value range of (0, 1). RL-Adjustment is the reinforcement learning adjustment amount, calculated by the DQN agent based on the intervention effect feedback and dynamically adjusted by the reinforcement learning agent based on the historical intervention effect. Step 4: Risk confrontation training; The generator of Risk-GAN synthesizes virtual hidden danger events, and the discriminator outputs a risk score S∈[0,1]; Optimize the system by minimizing the adversarial loss function: Γ=E[logD(x)]+E[log(1-D(G(z)))]+λ·Causal-Consistency Where D(x) is the score of the discriminator on the real risk data x, with a value range of (0, 1). The closer to 1, the more credible the risk; G(z) is the virtual hidden danger event synthesized by the generator based on the noise z; z is the latent space noise, usually following a Gaussian distribution; λ is the causal consistency penalty coefficient, which forces the generated event to conform to physical logic; Causal-Consistency is the causal consistency loss, which calculates the KL divergence between the generated event and the causal graph; Step 5: Autonomous intervention execution: The system triggers the optimal intervention strategy and completes closed-loop control through the execution terminal; Optimal strategy selection: Among them, a* is the optimal intervention action; is the state prediction of the potential node i after executing action a; the value range is {-1, +1}; Entropy i is the risk entropy of node i.

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