Complex system vulnerability prediction and control method based on seven operators and control engine

Abstract

The application discloses a complex system vulnerability prediction and control method and a control engine based on seven operators, relates to the technical field of complex system vulnerability prediction and control, and specifically comprises M operators, P operators, I operators, D operators, R operators, U operators, C operators and E operators which are sequentially connected and form a closed loop. The application adopts the flow transfer logic of M-P-I-D-R-U-C-E and the spiral rising mechanism of E triggering M, breaks through the single-loop feedback bottleneck, realizes a "preview-evolution" closed loop, upgrades the traditional "after-the-fact remedy" to "before-the-fact preview+limit evolution" through the preposition sand table deduction of the P operator and the dimension transition of the E operator, and fundamentally reduces the black swan vulnerability. The seven operators only define the logical flow transfer relationship and do not depend on specific hardware or business, can be deployed as an AI intelligent agent cognitive kernel or a macro risk control engine, and have a very wide protection range.

Description

Technical Field

[0001] This invention relates to the field of vulnerability prediction and control technology for complex systems, specifically to a vulnerability prediction and control method and control engine for complex systems based on the seven-operator. Background Technology

[0002] As artificial intelligence and social networks expand in scale, the nonlinear coupling within systems deepens. Existing system control and risk management technologies can be mainly divided into the following two categories:

[0003] The first category is closed-loop control technology based on classical cybernetics and reinforcement learning mechanisms. The core logic of this technology involves pre-setting target system operating parameters, collecting the system's current operating state in real time through a sensing module, comparing the current state with the preset target value to obtain deviation data, and then having the controller or reinforcement learning agent calculate the corresponding state adjustment based on the deviation data. Finally, the execution module outputs the corresponding control action to complete system state calibration and enter the next round of deviation sensing and adjustment cycle. This technology is essentially a single-loop feedback control mechanism, relying entirely on the state deviation after system operation for reward and punishment feedback and parameter correction. It can achieve small-scale system deviation calibration under normal steady-state scenarios and is a fundamental core technology for current agent-based autonomous control and dynamic system regulation.

[0004] The second category is risk control and diagnostic model technology tailored to specific fields. This technology is a scenario-bound, specialized management and control technology. Its core implementation involves collecting massive amounts of historical operational data and risk sample data for single, specific business scenarios such as financial lending, industrial equipment operation and maintenance, and power dispatching. Based on algorithms such as artificial neural networks and rule trees, it completes scenario-specific model training. In actual operation, real-time monitoring data is input into the trained model, which outputs the probability of risk occurrence for the corresponding scenario. Ultimately, it triggers single preset intervention strategies such as credit rejection, shutdown alarms, and parameter fine-tuning to achieve risk identification and handling in specific scenarios. It is widely used in standardized risk management in vertical fields.

[0005] However, the two main existing technologies mentioned above have fundamental technical shortcomings when dealing with complex system management scenarios involving large scale, strong coupling, and high dynamics. They cannot meet the high-order requirements of extreme scenario prediction, cross-domain general modeling, and control of structural changes in the system. The specific shortcomings are as follows:

[0006] Lacking the ability to anticipate and predict, the system is highly vulnerable to extreme scenarios. Existing reinforcement learning and closed-loop control rely on post-event bias optimization and lack the ability to predict and extrapolate multiple paths. When faced with unknown extreme disturbances such as black swan events, they can only respond passively and cannot avoid risks in advance, resulting in weak stability and poor fault tolerance of the system under extreme disturbances.

[0007] The lack of a cross-domain universal meta-model architecture results in high costs for model reuse. Existing technologies are all scenario-customized models that have not extracted the underlying universal logic of complex system evolution. Models from different domains cannot be interoperable and reused, leading to not only redundant resources from repeated research and development but also failing to achieve unified modeling from micro-level AI cognition to macro-level social governance.

[0008] Lacking the computational capabilities for system phase transitions and dimensional shifts, it does not support qualitative evolution. Existing models, based on linear or locally nonlinear assumptions, can only perform parameter fine-tuning at the quantitative level of the system. When the system reaches a critical point and experiences a structural phase transition, facing the risk of collapse, it cannot achieve computable control from quantitative calibration to qualitative transition, easily leading to system deadlock or paralysis, and failing to autonomously reconstruct and evolve.

[0009] In summary, current approaches to both cognitive control of general autonomous intelligent agents (AGI) and risk governance of socio-economic networks lack a unified meta-model to simultaneously address the following issues: ① the inability to anticipate extreme scenarios; ② the lack of universal modeling for systems across different domains; and ③ the inability of systems to self-reconstruct and evolve when encountering extreme bottlenecks, resulting in deadlock. Therefore, there is an urgent need to construct a cross-domain, universally applicable underlying architecture for complex systems to achieve pre-prediction of complex system vulnerabilities and computable, precise control over system qualitative leaps. Summary of the Invention

[0010] This invention provides a method and control engine for predicting and controlling the vulnerability of complex systems based on the seven-operator, which solves the problem mentioned in the background that the existing technology cannot simultaneously realize quantitative negative feedback regulation and qualitative critical point structural transition in a single closed loop, leading to control deadlock or collapse under extreme disturbances.

[0011] This invention provides the following technical solution: a method for predicting and controlling the vulnerability of complex systems based on the seven-operator, comprising the following steps:

[0012] S1. Start the control engine, call the M operator to define the boundary of the target system, extract the yin and yang polarity characteristics of the system, and generate the initial constraints and isolation interfaces for evolution; the specific operations are as follows: construct the system state space; use multidimensional state data with a dimension greater than 2 in the multidimensional state monitoring time series dataset as input to perform principal component analysis; select the two principal components with the highest contribution rates; according to the load matrix, map the two principal components back to the original physical variable space, find the physical variable pair with the highest correlation coefficient, and define them as the positive driving variable X and the negative constraint variable X; perform initial equilibrium calculation and boundary constraint function setting, and output the boundary parameter set and the pair of opposite variables;

[0013] S2. Based on the output data of the M operator, the P operator constructs a sandbox simulation space based on the current state, generates a multi-path possibility distribution for future evolution, and proactively exposes potential vulnerable nodes.

[0014] S3. Utilize the I operator to perceive and capture weak signals and state shifts within and at the boundaries of the system in real time, and extract characteristic data of the waxing and waning of yin and yang in the system.

[0015] S4. Use the D operator to decompose causal paths, trace vulnerability transmission links, and locate risk sources.

[0016] S5. Based on the pre-simulation distribution of the P operator and the tracing results of the D operator, the R operator is used to determine the threshold at the phase transition critical point of the system, and to adjudicate the system's state switching strategy or intervention command.

[0017] S6. Use the U operator to convert the decision command of the R operator into the system's control variables or energy release actions, driving the system to transition to the target state.

[0018] S7. The C operator is used to monitor the system feedback after the execution of the U operator in real time, calculate the execution deviation, and perform negative feedback correction. When the C operator determines that the deviation exceeds the calibrable threshold of the current dimension, the E operator triggers the system boundary reconstruction and the underlying rule rewriting to achieve a qualitative leap. After the leap is completed, the M operator is triggered again to define the boundary of the new dimension.

[0019] Preferably, S1 adopts a cold start mechanism. When the control engine starts for the first time without historical data, the state transition probability matrix T of the P operator is initialized to a uniform distribution. The I operator sets an initial gradient warning threshold Gthreshold based on engineering experience. After the control engine runs for the first cycle, T and Gthreshold are iteratively updated using the collected data.

[0020] Preferably, the specific calculation logic of the P operator is as follows: 1. Using the output data based on the M operator as input data;

[0021] 2. Based on the transition probability matrix T obtained by dynamic Bayesian network learning, N Monte Carlo simulations are performed, and the step size T is adjusted adaptively.

[0022] 3. Quantify the vulnerability index of each node, generate the vulnerable node set and evolution probability distribution, and output them.

[0023] Preferably, in the D operator, the specific operations for causal path decomposition and risk tracing are as follows: 1. Obtain input data;

[0024] II. Quantification of risk propagation weights of directed edges using a transfer entropy algorithm based on kernel density estimation;

[0025] 3. Starting from the warning node, search backwards along the directed edge, select the path with the largest cumulative propagation weight product as the root cause path, extract the starting node of the path as the root cause node of the risk, and output the source tracing result.

[0026] Preferably, the online tuning steps of the PID parameters based on gradient descent in the C operator are as follows: updating the PID gain and calculating the correction amount using an online tuning algorithm based on gradient descent.

[0027] Preferably, the specific operation of the E operator is as follows: acquiring input data, adopting a multi-objective weighted fitness function, and normalizing the Hamming distance; setting connectivity constraints, node degree upper limit constraints, and resource conservation constraints; based on the fitness function and the triple constraints, selecting the optimal individual with the largest fitness value, decoding the optimal individual to obtain the reconstructed adjacency matrix and the reconstructed rule set, triggering the M operator restart, and outputting the reconstructed adjacency matrix, the reconstructed rule set, and the M operator restart signal.

[0028] The complex system vulnerability prediction and control engine based on seven operators includes M operator, P operator, I operator, D operator, R operator, U operator, C operator and E operator connected in sequence to form a closed loop. Each operator is implemented by the processor executing program instructions in memory.

[0029] The M operator is used to define the boundary of the target system, distinguish between the internal state and the external environment, extract the yin and yang polarity characteristics of the system, and generate the initial constraints and isolation interfaces for evolution.

[0030] The P operator is used to construct a sandbox simulation space based on the current state, generate a multi-path possibility distribution for future evolution, and proactively expose potential vulnerable nodes.

[0031] The I operator is used to sense and capture weak signals and state shifts within and at the boundaries of the system in real time, and to extract characteristic data of the waxing and waning of the system's yin and yang.

[0032] The D operator is used to decompose the causal path of the offset signal captured by the I operator, and to trace the transmission link of vulnerability and risk source in the system network.

[0033] The R operator is used to determine the threshold at the phase transition critical point of the system based on the pre-simulation distribution of the P operator and the source tracing results of the D operator, and to adjudicate the system's state switching strategy or intervention command.

[0034] The U operator is used to convert the decision command of the R operator into the system's control variables or energy release actions, driving the system to transition to the target state.

[0035] The C operator is used to monitor the system feedback after the U operator is executed, calculate the execution deviation, and perform negative feedback correction. When the C operator determines that the deviation exceeds the calibrable threshold of the current dimension, the E operator triggers system boundary reconstruction and underlying rule rewriting to achieve a qualitative leap. After the leap is completed, the M operator is triggered again to define the boundary of the new dimension.

[0036] Preferably, the P operator performs the operation within the boundary defined by the M operator.

[0037] Preferably, the R operator has a safety degradation strategy. The triggering rule of the safety degradation strategy is as follows: when any operator experiences a calculation timeout or missing input data, after the installation degradation strategy is triggered, the output of the R operator maintains the control command of the previous cycle or the preset safety standby command, and at the same time freezes the deadlock judgment count of the C operator to prevent the E operator from being mistakenly triggered due to data abnormalities.

[0038] Preferably, the feature data extracted by the I operator includes the inverse relationship between the opposing variables and the gradient rate of change.

[0039] An electronic device includes at least one processor and a memory communicatively connected to the at least one processor; wherein: the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the aforementioned method for predicting and controlling the vulnerability of complex systems based on the seven-operator.

[0040] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for predicting and controlling the vulnerability of complex systems based on the seven-operator.

[0041] A computer program product comprising a computer program or instructions that, when executed by a processor, implement the aforementioned method for predicting and controlling the vulnerability of complex systems based on the seven-operator.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] (1) Break through the single-loop feedback bottleneck and realize the “pre-evolution” closed loop: Through the pre-sand table deduction of the P operator and the dimensional leap of the E operator, the traditional “post-remediation” is upgraded to “pre-pre-evolution + extreme evolution”, which fundamentally reduces the vulnerability of black swans.

[0044] (2) Achieving a cross-domain and universal meta-model architecture: Seven Operators only specifies the logical flow relationship and does not depend on specific hardware or business. It can be deployed as the cognitive kernel of AI intelligent agents or as a macro risk control engine, with an extremely wide protection range.

[0045] (3) Provides a computable system evolution translation framework: For the first time, quantitative and qualitative changes are mapped to C operator (negative feedback convergence) and E operator (topology reconstruction), solving the engineering problem that the critical point of the system is not computable and uncontrollable. Attached Figure Description

[0046] Figure 1The flowchart shows the vulnerability prediction and control method for complex systems based on seven-operator closed loop proposed in this invention.

[0047] Figure 2 This is a diagram of the vulnerability prediction and control engine for complex systems based on a seven-operator closed loop proposed in this invention.

[0048] Figure 3 This is a diagram showing the deployment architecture of the control engine on a server / cloud platform. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] In specific embodiments of the present invention, the specific terms used have the following clear engineering technical meanings: "Yin-Yang polarity characteristics": specifically refers to a pair of mutually opposing and mutually inverse physical state variables extracted through data dimensionality reduction in the state space of a complex system, such as "supply and demand" and "power generation output and load demand", and the system maintains a dynamic balance between the two through regulation.

[0051] "Qualitative leap" / "Dimensional leap": Specifically refers to the evolutionary action of the system to globally reconstruct its underlying topology (adjacency matrix) and control logic rule set when the system deviation exceeds the conventional feedback adjustment capability, so that the system switches from the current failed structure to a new usable structure;

[0052] "Spiral Ascent Mechanism": This specifically refers to the iterative process by which a system, after being reconstructed through the aforementioned topology and rules, re-enters a new round of state boundary definition and closed-loop control, thereby improving the system's hierarchy and resilience.

[0053] This invention provides embodiments: Please refer to Figure 1 The method for predicting and controlling the vulnerability of complex systems based on the seven operators is executed by the control engine. It involves sequentially running a closed loop consisting of the seven MPIDRUC operator modules within the current system dimension. A transition path is added between the C and M operators, triggered by the C operator, processed by the E operator, and returning to the M operator. This upgrades the traditional "post-event remediation" to "pre-event simulation + extreme evolution," fundamentally reducing system vulnerability. Specifically, it includes the following steps:

[0054] S1. Start the control engine. The control engine adopts a cold start mechanism and an exception handling mechanism. The cold start mechanism is as follows: When the engine starts for the first time and there is no historical data, the state transition probability matrix T of the P operator is initialized to a uniform distribution; the I operator sets the initial gradient warning threshold G based on engineering experience. threshold After the system completes its first cycle, it iteratively updates T and G using the collected data. threshold .

[0055] Anomaly handling mechanism: When any operator experiences a calculation timeout (exceeding the set time limit) or missing input data (sensor disconnection), the safety degradation strategy of the R operator is triggered, outputting the control command of the previous step or the preset safety standby command, while freezing the deadlock judgment count of the C operator to prevent the E operator from being triggered by data anomalies.

[0056] The M-operator is invoked to define the boundary of the target system, extract the yin and yang polarity characteristics of the system, and generate initial constraints and isolation interfaces for evolution. The inputs to the M-operator are: the node set V, the edge set E, and the multidimensional state monitoring time series dataset S of the target system. hist The specific operation of the M operator is as follows: System state space construction: Construct the system state space S={s1,s2,...,s...} n};

[0057] Automatic extraction and orthogonalization of complementary variables: using S hist Multidimensional state data with a middimensional dimension greater than 2 is used as input for principal component analysis (PCA). The two principal components with the highest contribution rates, PC1 and PC2 (which must satisfy the orthogonality condition PC1·PC2=0), are selected. Based on the loading matrix, PC1 and PC2 are mapped back to the original physical variable space, and the pair of physical variables with the highest correlation coefficient is identified and defined as the positive driving variable X. + and reverse constraint variable X - ;

[0058] Initial balance calculation: Calculate the initial balance B0 = X + -X - (Equation 1)

[0059] Boundary constraint function setting: Set the boundary constraint function Fcons(S)=0; (Equation 2)

[0060] Output: Boundary parameter set {S, Fcons} and complementary variable pair (X) + ,X - ).

[0061] S2, based on the output data of the M operator (boundary parameter set, pairs of complementary variables, current system state S) t The P operator constructs a sandbox simulation space based on the current state, generates a multi-path possibility distribution for future evolution, and proactively exposes potential vulnerable nodes.

[0062] The specific operation is as follows: Monte Carlo state transition tree construction: Based on the transition probability matrix T learned by the Dynamic Bayesian Network (DBN), N Monte Carlo simulations are performed, and the step size T is adaptively adjusted: T=⌈α / |G t | avg ⌉, where α is the preset time horizon coefficient, and its value range can be set within the interval [1, 10] according to the time sensitivity of different systems, |G t | avg The gradient is the recent average gradient; the larger the gradient (the faster the degradation), the longer the extrapolation step size. When the system degrades, the average gradient |G|... t | avg When the system state deteriorates faster, the calculated inference step size T becomes longer adaptively, thus exposing vulnerable nodes in the later period in advance; conversely, when the system is running smoothly, the step size becomes shorter, focusing on short-term risks, thereby balancing computing power consumption and predictive foresight.

[0063] Node vulnerability index quantification: V i = P(node ​​i failed) × Impact(node ​​i failed), where the failure probability P is obtained from simulation statistics; Impact is quantified by calculating the change in the average path length of the network after removing node i;

[0064] Vulnerability Node Set Generation: Output the vulnerability node set N vul and probability distribution P(S) t+T );

[0065] Output: N vul and P(S) t+T ).

[0066] S3. Utilize the I operator to perceive and capture weak signals and state shifts within and at the boundaries of the system in real time, and extract characteristic data of the waxing and waning of yin and yang in the system.

[0067] The specific operation is as follows: Input: Real-time sensor data stream;

[0068] Specific calculation logic:

[0069] Deviation calculation: ΔX=X + real -X - real ;

[0070] Gradient calculation: G t =d(ΔX) / dt, which can be discretized as Gt≈(ΔX) t -ΔX t-1 ) / Δt;

[0071] Weak signal warning: When |Gt |>G threshold Timely triggering of warnings;

[0072] Output: State offset gradient G t and warning signs.

[0073] S4. Utilize the D operator to decompose causal paths, trace vulnerability transmission links, and locate risk sources; the specific operations of D operator's causal path decomposition and risk tracing are as follows: Input: System correlation diagram, G t Early warning source nodes;

[0074] Transmission weight calculation: The transmission weight of the directed edge i→j is quantified using a transfer entropy algorithm based on kernel density estimation: W i→j =TE i→j =Σp(j t+1 ,j t i t )×log[p(j t+1 |j t i t ) / p(j t+1 |j t The probability distribution p is obtained by nonparametric fitting from historical data using a Gaussian kernel density estimator.

[0075] Risk source identification: Starting from the warning node, search backwards along the directed edges, and select the path with the largest cumulative propagation weight product as the Path. root The starting point is R. cause ;

[0076] Output: Path root and R cause .

[0077] S5. Based on the pre-simulation distribution of the P operator and the tracing results of the D operator, the R operator is used to determine the threshold at the phase transition critical point of the system, and to adjudicate the system's state switching strategy or intervention command.

[0078] The specific operation is as follows: Input: P(S) t+T ), N vul Path root ∫ΔXdt;

[0079] Dual determination: Proactive determination E[V]>Θphase or real-time determination ∫ΔXdt>Θphase;

[0080] Intervention command optimization solution: The multi-objective optimization problem is solved using the Particle Swarm Optimization (PSO) algorithm: minJ=E[V]+λ×Cost(Ctrl); stCtrlmin≤Ctrl≤Ctrlmax; Fsafe(Ctrl)≥0; where the tradeoff coefficient λ is adaptively adjusted: λ=λ0×e -γ·|et| Where λ0 is the basic cost trade-off coefficient, and is the deviation-sensitive attenuation coefficient, whose value range can be set to [0.1, 1.0]; when the deviation |e t When the deviation is extremely large (the system faces the risk of collapse), the exponential term approaches 0, the λ value drops sharply, and the optimization algorithm automatically reduces the weight of control cost and prioritizes solving the stability-preserving strategy; when the deviation is small, the λ value recovers, and the system takes control cost optimization into account.

[0081] Output: Optimal intervention command Ctrl * .

[0082] S6. Utilize the U operator to transform the decision instructions of the R operator into system control variables or energy release actions, driving the system to transition to the target state; specifically, Ctrl... * Mapped to the physical control vector ΔUt,

[0083] ΔU t =[δ1,...,δ m ] T Issue the actuator and confirm.

[0084] S7. Use the C operator to monitor the system feedback after the execution of the U operator in real time, calculate the execution deviation, and perform negative feedback correction. When the C operator determines that the deviation exceeds the calibrable threshold of the current dimension, the E operator triggers the system boundary reconstruction and underlying rule rewriting to achieve a qualitative leap. After the leap is completed, the M operator is triggered again to define the boundary of the new dimension.

[0085] The negative feedback calibration and deadlock determination process of the C operator is as follows: Input: actual system output S t+1 Target state S target ;

[0086] Calculate deviation: e t =S target -S t+1 ;

[0087] Adaptive PID calibration: The PID gain is updated using an online tuning algorithm based on gradient descent: K p (t+1)=K p (t)+η p ·e t ·∂e t / ∂K p Calculate the correction amount: u adj =Kp ·e t +K i ·∫e t dt+K d ·de t / dt;

[0088] Deadlock determination: when |e t |>ε max And Δt > Δt for an extended period lock When deadlock is detected, the E operator is triggered. That is, when the absolute value of the deviation exceeds the maximum permissible deviation threshold, and the abnormal state is continuously maintained for more than the preset deadlock duration threshold (for example, in a power grid system, it can be set to the level of hundreds of milliseconds, which is determined by the system inertial time constant), deadlock is detected and the E operator is triggered.

[0089] Output: u adj e t Deadlock determination result.

[0090] The specific operation of the E operator is as follows: obtain input data: deadlock trigger signal, current adjacency matrix A, rule set R;

[0091] A multi-objective weighted fitness function is adopted, and the fitness function is: Fitness = w1 / (Vulnerability+1) + w2 / (Avg_Error+1) +

[0092] w3×Hamming(A cand A deadlock ) / N edges Where: denominator N edges The total number of edges in the network is used to normalize the Hamming distance; the weights w1, w2, and w3 in the multi-objective weighted fitness function are all adjustable parameters greater than 0; in actual engineering deployment, the tolerance of different target systems to "vulnerability", "average error" and "topology difference" is dynamically configured; this invention does not limit the specific values ​​to adapt to the topology reconstruction needs of different fields.

[0093] Set connectivity constraints ∀i,j∈V,∃path(i,j) (based on graph theory connected component algorithm for determination);

[0094] Node degree upper limit constraint Σ j=1n A ij ≤D max D max Take 1.5 times the maximum degree of the original network;

[0095] Resource conservation constraint ΣCtrl new ≤ΣCtrl old +ΔR allow ;

[0096] Based on the fitness function and triple constraints, the optimal individual with the largest fitness value is selected. The optimal individual is decoded to obtain the reconstructed adjacency matrix A' and the reconstructed rule set R'. The underlying structure is replaced with A' and R' obtained from the optimal individual, triggering the M operator to restart. The reconstructed adjacency matrix A', the reconstructed rule set R', and the M operator restart signal are output.

[0097] As described above, the vulnerability prediction and control method for complex systems based on the seven operators provided by this invention adopts the flow logic of MPIDRUCE and the spiral ascent mechanism of E triggering M, breaking through the single-loop feedback bottleneck and realizing a "pre-evolution" closed loop: through the pre-sand table deduction of the P operator and the dimensional leap of the E operator, the traditional "post-event remedy" is upgraded to "pre-event pre-evolution + extreme evolution", fundamentally reducing black swan vulnerability. The seven operators only specify the logical flow relationship and do not depend on specific hardware or business. They can be deployed as the cognitive kernel of AI intelligent agents or as a macro risk control engine, with an extremely wide protection range; and for the first time, quantitative change and qualitative change are mapped to the C operator (negative feedback convergence) and the E operator (topology reconstruction), solving the engineering problem that the system critical point is uncomputable and uncontrollable.

[0098] Please see Figure 2 This invention provides a vulnerability prediction and control engine for complex systems based on seven operators, including M operator, P operator, I operator, D operator, R operator, U operator, C operator and E operator connected in sequence to form a closed loop; the M operator (boundary delimitation and polarity module) is used to define the boundary of the target system, distinguish the internal state from the external environment, extract the yin and yang polarity characteristics of the system (i.e., positive and negative opposite variables, such as supply / demand), generate the initial constraints and isolation interface for evolution;

[0099] The P operator performs operations within the boundary defined by the M operator. The P operator (evolutionary pre-simulation module) is used to construct a sandbox simulation space based on the current state, generate a multi-path possibility distribution for future evolution, and proactively expose potential vulnerable nodes.

[0100] The I operator (Observation and Analysis Module) is used to sense and capture weak signals and state shifts within and at the boundaries of the system in real time, and to extract characteristic data of the waxing and waning of the system's yin and yang. The extracted characteristic data includes the waxing and waning of opposing variables and the gradient rate of change.

[0101] The D operator (source tracing module) is used to decompose the causal path of the offset signal captured by the I operator, and to trace the transmission link of vulnerability and risk source in the system network.

[0102] The R operator (threshold adjudication module) is used to determine the threshold at the phase transition critical point of the system based on the pre-simulation distribution of the P operator and the source tracing results of the D operator, and adjudicate the system's state switching strategy or intervention command. The R operator also has a safety degradation strategy, which is triggered as follows: when any operator experiences a calculation timeout or missing input data, after the degradation strategy is triggered, the output of the R operator maintains the control command of the previous cycle or the preset safety standby command, and at the same time freezes the deadlock judgment count of the C operator to prevent the E operator from being mistakenly triggered due to data abnormalities.

[0103] The U operator (execution module) is used to convert the decision instructions of the R operator into system control variables or energy release actions, driving the system to transition to the target state;

[0104] The C operator (closed-loop calibration module) is used to monitor the system feedback after the execution of the U operator, calculate the execution deviation, and perform negative feedback correction. When the C operator determines that the deviation exceeds the calibrable threshold of the current dimension, the E operator (dimensional transition module) triggers the system boundary reconstruction and underlying rule rewriting to achieve a qualitative leap. After the leap is completed, the M operator is triggered again to define the boundary of the new dimension.

[0105] The aforementioned MPIDRUCE operators constitute the smallest indivisible closed-loop iterative unit, with each operator implemented by the processor executing program instructions from memory. This invention can be implemented using x86 / ARM servers and GPU accelerator cards. The software employs Python / C++ / Go, data storage uses a time-series database (InfluxDB) and a graph database (Neo4j), and communication supports MQTT / OPC-UA / REST, etc. It is suitable for complex systems such as supply chains, power, transportation, finance, education, and distributed computing. The seven-operator control engine can be deployed on server / cloud platform software systems and can serve as the underlying cognitive and control engine for various complex systems or autonomous intelligent agents. It provides full lifecycle governance capabilities from vulnerability perception, risk tracing, threshold adjudication to closed-loop calibration and dimensional transition. By completing the entire closed-loop control process of "vulnerability prediction + risk tracing + threshold adjudication + closed-loop calibration + dimensional transition" in complex systems, the system possesses pre-emptive simulation capabilities and extreme evolutionary capabilities.

[0106] The deployment architecture of the Seven Calculator Control Engine on server / cloud platforms is as follows: Figure 3As shown. The product adopts a layered architecture design, from top to bottom, including: Infrastructure Layer: Computing devices (CPU / GPU / TPU), memory, network interfaces, and operating system, providing the runtime environment for the engine. Engine Core Layer: Eight modules (M to E) operate according to a seven-operator closed-loop logic, using a shared data bus publish-subscribe pattern for state vector and instruction transmission to achieve closed-loop iterative control. Business Adaptation Layer: Provides standardized interfaces and adapters for scenarios such as supply chain risk control, K12 education, and AGI cognitive kernel, supporting rapid integration with different business systems. Interaction Layer: Monitoring dashboards, API gateways, SDKs, etc., providing users with visual monitoring and system integration capabilities.

[0107] Typical deployment scenarios are:

[0108] (1) Supply Chain Complex Network Risk Management Products

[0109] Inputs include multi-source heterogeneous data such as order data, inventory levels, logistics timeliness, policy changes, and weather warnings.

[0110] Outputs: Vulnerable node early warning, risk transmission path visualization, intervention suggestions (such as activating backup suppliers, adjusting safety stock), and topology refactoring solutions.

[0111] Target users: large manufacturing enterprises, supply chain platform operators, and government economic management departments.

[0112] (2) K12 cognitive reconstruction autonomous intelligent agent products

[0113] Inputs include: student answer data, behavioral trajectories, multimodal interaction signals such as micro-expressions / voice, and subject knowledge graphs.

[0114] Outputs include: personalized teaching path recommendations, knowledge gap diagnosis reports, cognitive phase transition threshold determination, and dimensional transition strategies (such as switching teaching frameworks or resetting reward functions).

[0115] Target users: online education platforms, school administration systems, and training institutions.

[0116] (3) General AGI cognitive kernel products

[0117] Input: Multimodal perception data (visual, language, sensor), task objective description.

[0118] Outputs: Behavioral strategy sequence, risk prediction signals, rule rewriting suggestions, and system self-organizing evolution path.

[0119] Target users: large model manufacturers, robotics companies, and automation control platforms.

[0120] Example 1: Risk Governance of Complex Supply Chain Networks

[0121] The engine is deployed within a regional supply chain network. The M operator defines core enterprises and suppliers as internal entities, and policies as external entities, extracting supply and demand polarities. The P operator, through Monte Carlo simulation, identifies single-point-of-failure suppliers (vulnerable nodes) in advance. The I operator detects slight increases in delivery delay rates (gradient change rate). The D operator traces upstream raw material shortages and their transmission chains. The R operator initiates backup supplier decisions when inventory risk exceeds a threshold. The U operator issues procurement orders. The C operator fine-tunes safety stock parameters to achieve quantitative accumulation. When a global event causes the original topology to fail, and the deviation exceeds the C operator's threshold, the E operator triggers a dimensional shift, rewriting the "global centralized procurement" topology into a "regional multi-center alliance" and restarting the M operator. The result: the system did not deadlock when facing a supply chain disruption crisis, achieving architectural-level self-organizing evolution.

[0122] Example 2: K12 Cognitive Reconstruction of Autonomous Intelligent Agent

[0123] The engine is deployed as the core of the teaching AI. The M operator defines the boundaries of subject knowledge; the P operator infers potential knowledge barriers for students; the I operator captures subtle signals of pauses in answering questions; the D operator traces the root cause to the "division concept not mastered" gap; the R operator decides to downgrade the review; the U operator pushes intervention exercises; and the C operator fine-tunes the teaching strategy to converge to the target. When the C operator determines that a student is extremely averse to learning (deviation exceeds the calibration threshold), the E operator triggers a dimensional leap, reconstructing the teacher-student interaction mode and the underlying reward function, and restarting the M operator. Result: When the agent encounters cognitive deadlock, it successfully resolves the collapse through a dimensional leap and enters a new learning level.

[0124] Example 3: Resilience Control of Regional Microgrids

[0125] The M operator is used to extract power generation output and load demand; the P operator is used to deduce the probability of line icing under extreme weather conditions and the vulnerability of key transformers at nodes; the I operator collects operating signals in real time through frequency sensors on the grid-connected side, captures the frequency offset gradient, and outputs anomaly warnings; the C operator continuously monitors the quantitative change of system frequency deviation, and triggers the E operator when the absolute value of the frequency deviation exceeds the maximum allowable deviation threshold after a line disconnection fault occurs, and the abnormal state is maintained for 500ms; the E operator receives the current grid-connected topology adjacency matrix A, and uses a genetic algorithm to perform evolutionary optimization with a preset multi-objective fitness function, while satisfying the triple grid-specific constraints of node degree upper limit not exceeding 4, total power generation not less than total load power, and full topology connectivity, to obtain the island reconstruction adjacency matrix A. ′ By updating scheduling rules and executing non-core load shedding operations, the microgrid islanded topology can be reconfigured. Effect: This can prevent large-scale power outages across the entire grid.

[0126] In addition, alternatives to the P operator (evolutionary pre-simulation) include: besides Monte Carlo simulation, scenario generation based on large language models (LLM) or simulation simulation based on digital twins can also be used to achieve multi-path distribution.

[0127] In Example 4, the multi-path evolution distribution of the P operator no longer relies on Monte Carlo simulation. Instead, the boundary parameters and opposing variable pairs defined by the M operator are transformed into cue words and input into a pre-trained Large Language Model (LLM). The LLM generates textual descriptions of multiple future extreme risk scenarios based on the context and transforms them into a probability distribution of system state transition paths through a parsing framework, thus achieving the technical effect of proactively exposing potential vulnerable nodes.

[0128] Alternatives to the D operator (source attribution): In addition to logical causal graphs, Bayesian network inference or node feature attribution based on graph neural networks (GNNs) can also be used to achieve risk attribution.

[0129] Example 5: In this example, the D operator does not use kernel density to estimate the transfer entropy, but instead constructs a graph neural network model. System nodes are treated as graph nodes, and relationships are treated as edges. The offset gradients captured by the I operator are used as node features input to the trained GNN model. The GNN's interpretation algorithm (such as GNNExplainer) is used to calculate the attribution contribution of each node to the warning node. The reverse propagation path with the largest contribution is extracted as the root source path to achieve risk source tracing and location.

[0130] Alternatives to the E-operator (dimensional transition): In addition to directly rewriting the rules, dimensional transitions can be achieved by resetting the reward function space of reinforcement learning in AI agent scenarios; in physical systems, they can be achieved by switching redundant backup architectures.

[0131] In Example 6, in the AI ​​agent scenario, the "underlying rule rewriting" of the E operator is achieved by resetting the reward function space of the reinforcement learning agent. When the C operator determines that the agent is trapped in deadlock (e.g., unable to obtain positive rewards for multiple consecutive rounds), the E operator resets the current sparse reward function to a new reward function with an exploration guidance term, enabling the agent to escape the local optimum deadlock, achieve a leap in cognitive dimension, and restart the M operator.

[0132] An electronic device includes at least one processor and a memory communicatively connected to the at least one processor; wherein: the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a method for predicting and controlling the vulnerability of complex systems based on the seven operators.

[0133] A computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for predicting and controlling the vulnerability of complex systems based on the seven-operator.

[0134] A computer program product comprising a computer program or instructions, which, when executed by a processor, implements a method for predicting and controlling the vulnerability of complex systems based on the seven-operator.

[0135] In summary, this method and control engine for predicting and controlling the vulnerability of complex systems based on the seven operators breaks through the bottleneck of single-loop feedback and achieves a unified closed loop of quantitative and qualitative change; it is universally applicable across domains, covering scenarios such as supply chain, education, and power; and it provides computable translation, engineering philosophical concepts.

[0136] The contents not described in detail in this specification are prior art known to those skilled in the art. Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting and controlling the vulnerability of complex systems based on the seven-operator, characterized in that, Includes the following steps: S1. Start the control engine, call the M operator to define the boundary of the target system, extract the yin and yang polarity characteristics of the system, and generate the initial constraints and isolation interfaces for evolution; the specific operation is: construct the system state space. Principal component analysis was performed using multidimensional state data with a dimension greater than 2 in the multidimensional state monitoring time series dataset as input; the two principal components with the highest contribution rates were selected. Based on the load matrix, the two principal components are mapped back to the original physical variable space. The pair of physical variables with the highest correlation coefficient is found and defined as the positive driving variable X and the negative constraint variable X. The initial equilibrium degree is calculated and the boundary constraint function is set, and the boundary parameter set and the pair of opposite variables are output. S2. Based on the output data of the M operator, the P operator constructs a sandbox simulation space based on the current state, generates a multi-path possibility distribution for future evolution, and proactively exposes potential vulnerable nodes. S3. Utilize the I operator to perceive and capture weak signals and state shifts within and at the boundaries of the system in real time, and extract characteristic data of the waxing and waning of yin and yang in the system. S4. Use the D operator to decompose causal paths, trace vulnerability transmission links, and locate risk sources. S5. Based on the pre-simulation distribution of the P operator and the tracing results of the D operator, the R operator is used to determine the threshold at the phase transition critical point of the system, and to adjudicate the system's state switching strategy or intervention command. S6. Use the U operator to convert the decision command of the R operator into the system's control variables or energy release actions, driving the system to transition to the target state. S7. The C operator is used to monitor the system feedback after the execution of the U operator in real time, calculate the execution deviation, and perform negative feedback correction. When the C operator determines that the deviation exceeds the calibrable threshold of the current dimension, the E operator triggers the system boundary reconstruction and the underlying rule rewriting to achieve a qualitative leap. After the leap is completed, the M operator is triggered again to define the boundary of the new dimension.

2. The method for predicting and controlling the vulnerability of complex systems based on the seventh operator according to claim 1, characterized in that: S1 employs a cold start mechanism. When the control engine starts for the first time without historical data, the state transition probability matrix T of the P operator is initialized to a uniform distribution; the I operator sets the initial gradient warning threshold G based on engineering experience. threshold After the control engine completes its first cycle, it iteratively updates T and G using the collected data. threshold .

3. The method for predicting and controlling the vulnerability of complex systems based on the seventh operator according to claim 1, characterized in that: The specific calculation logic of the P operator is as follows: First, take the output data based on the M operator as the input data; 2. Based on the transition probability matrix T obtained by dynamic Bayesian network learning, N Monte Carlo simulations are performed, and the step size T is adjusted adaptively.

3. Quantify the vulnerability index of each node, generate the vulnerable node set and evolution probability distribution, and output them.

4. The method for predicting and controlling the vulnerability of complex systems based on the seventh operator according to claim 1, characterized in that: In the D operator, the specific operations for causal path decomposition and risk tracing are as follows:

1. Obtain input data; II. Quantification of risk propagation weights of directed edges using a transfer entropy algorithm based on kernel density estimation; 3. Starting from the warning node, search backwards along the directed edge, select the path with the largest cumulative propagation weight product as the root cause path, extract the starting node of the path as the root cause node of the risk, and output the source tracing result.

5. The method for predicting and controlling the vulnerability of complex systems based on the seventh operator according to claim 1, characterized in that: The steps for online tuning of PID parameters based on gradient descent in the C operator are as follows: update the PID gain and calculate the correction amount using an online tuning algorithm based on gradient descent.

6. The method for predicting and controlling the vulnerability of complex systems based on the seventh operator according to claim 1, characterized in that: The specific operation of the E operator is as follows: acquire input data, adopt a multi-objective weighted fitness function, and normalize the Hamming distance; set connectivity constraints, node degree upper limit constraints, and resource conservation constraints; based on the fitness function and the triple constraints, select the optimal individual with the largest fitness value, decode the optimal individual to obtain the reconstructed adjacency matrix and the reconstructed rule set, trigger the M operator to restart, and output the reconstructed adjacency matrix, the reconstructed rule set, and the M operator restart signal.

7. A vulnerability prediction and control engine for complex systems based on the seven-operator, characterized by: It includes M operator, P operator, I operator, D operator, R operator, U operator, C operator and E operator connected in sequence to form a closed loop. Each operator is implemented by the processor executing program instructions in memory. The M operator is used to define the boundary of the target system, distinguish between the internal state and the external environment, extract the yin and yang polarity characteristics of the system, and generate the initial constraints and isolation interfaces for evolution. The P operator performs operations within the boundary defined by the M operator. The P operator is used to construct a sandbox simulation space based on the current state, generate a multi-path possibility distribution for future evolution, and proactively expose potential vulnerable nodes. The I operator is used to sense and capture weak signals and state shifts within and at the boundaries of the system in real time, and to extract characteristic data of the waxing and waning of the system's yin and yang. The characteristic data includes the waxing and waning of opposing variables and the gradient rate of change. The D operator is used to decompose the causal path of the offset signal captured by the I operator, and to trace the transmission link of vulnerability and risk source in the system network. The R operator is used to determine the threshold at the phase transition critical point of the system based on the pre-simulation distribution of the P operator and the source tracing results of the D operator, and to adjudicate the system's state switching strategy or intervention command. The R operator also has a safety degradation strategy. The triggering rule of the safety degradation strategy is as follows: when any operator experiences a calculation timeout or missing input data; after the installation degradation strategy is triggered, the output of the R operator maintains the control command of the previous cycle or the preset safety standby command, and at the same time freezes the deadlock determination count of the C operator to prevent the E operator from being mistakenly triggered due to data abnormalities. The U operator is used to convert the decision command of the R operator into the system's control variables or energy release actions, driving the system to transition to the target state. The C operator is used to monitor the system feedback after the U operator is executed, calculate the execution deviation, and perform negative feedback correction. When the C operator determines that the deviation exceeds the calibrable threshold of the current dimension, the E operator triggers system boundary reconstruction and underlying rule rewriting to achieve a qualitative leap. After the leap is completed, the M operator is triggered again to define the boundary of the new dimension.

8. An electronic device, characterized in that, It includes at least one processor and a memory communicatively connected to the at least one processor; wherein: the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the complex system vulnerability prediction and control method based on the seven-operator as described in claim 1.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method for predicting and controlling the vulnerability of complex systems based on the seven-operator as described in claim 1.

10. A computer program product, characterized in that: The computer program product includes a computer program or instructions that, when executed by a processor, implement the method for predicting and controlling the vulnerability of complex systems based on the seven-operator as described in claim 1.

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