30 results about "Steady state distribution" patented technology

The invention discloses an unmanned aerial vehicle cluster patrol path decision-making method under resource constraint, and relates to the technical field of unmanned aerial vehicle path planning, and the method comprises the steps: carrying out the discretization of an actual to-be-patrolled physical position, and constructing an undirected topological graph; generating steady-state distribution of each patrol node according to topological constraints and importance degrees of the nodes; generating a plurality of transfer matrixes with the same steady-state distribution and different transfer characteristics according to a multi-stage entropy driving random matrix optimization algorithm; initializing the position of a navigator according to the transfer matrix and determining a selected path; according to the reference path of the navigator, the self-adaptive active positioning decision is realized under the positioning constraint to ensure the path tracking effect; according to path selection and tracking of the navigator, the follower and the navigator form a humanoid marshalling cluster through a reward function; and according to the multi-state transfer matrix and the humanoid marshalling, automatically switching to the next transfer matrix when a transfer frequency threshold value is reached, and realizing unmanned aerial vehicle cluster intelligent patrol path decision under resource constraint.

The invention discloses an unmanned aerial vehicle random patrol strategy system evaluation method in a confrontation environment. The method comprises the steps of setting an unmanned aerial vehicle intelligent patrol attack and defense confrontation system and a target steady-state distribution model of the unmanned aerial vehicle intelligent patrol attack and defense confrontation system; determining the average strategy entropy of the patrol person; determining conditional probability differences of the patrol persons; determining a transfer strategy dispersion degree of the patrol person; a patrol strategy comprehensive structure difference index is determined; determining a single-time-point key state average relative error; determining the average relative error of the time average key state; and determining a time-weighted key state average relative error. According to the method, the comprehensive structure difference index of the difference between the single-state transition matrix and different matrixes is quantified, and a balanced framework is provided to evaluate the overall unpredictability of the strategy. A multi-dimensional evaluation system based on key state and time weighting is designed, and time importance is directly fused into error calculation through a key state screening mechanism.

The invention relates to the field of fire risk assessment, and discloses a Markov chain model-based fire risk assessment method, which comprises the following steps of: defining a fire state set S, constructing a fire state transition matrix P according to historical fire data, and calculating steady-state distribution # imgabs0 # of each fire state by using the fire state transition matrix P, according to the distribution # imgabs1 # of the current fire state, the fire state distribution # imgabs2 # of the next moment is predicted, the fire state changes of multiple moments in the future are obtained after multiple times of repeated prediction, the expected value # imgabs4 # of the fire risk is defined for each fire state # imgabs3 #, the total expected value R of the fire risk is calculated, the future fire occurrence situation is simulated for multiple times through the Monte Carlo method, and the fire state changes of multiple moments in the future are obtained. Evaluating a future risk scene; and obtaining probability distribution of states at different moments in the future according to a fire occurrence condition simulation result, and providing a basis for a fireproof plan. Real-time assessment of fire risks is realized through dynamic modeling, accuracy and scientificity of assessment are improved, and fire prevention and emergency management decisions are effectively supported.

The embodiment of the invention provides an intelligent scheduling method for an electric vehicle charging station, and belongs to the technical field of data processing, and the method specifically comprises the steps: constructing a multi-dimensional time sequence feature set, and generating a training set with a scheduling label; training a deep learning model based on a gating circulation unit by using the training set, and performing rolling prediction on the arrival rate and the service rate of the vehicle by using the trained deep learning model; constructing a multi-state Markov queuing model based on scheduling control input, calculating steady-state distribution and calculating performance indexes according to the steady-state distribution; constructing a charging port scheduling optimization model based on a model prediction control method and the performance indexes, constructing an objective function, and outputting a rolling optimization strategy according to the objective function; and executing a rolling optimization strategy, adjusting the number of the AC / DC charging piles, generating indication information, and feeding back an operation result to the charging port scheduling optimization model to realize closed-loop correction. According to the scheme, the resource utilization rate is improved, the user waiting time is shortened, and the system stability is enhanced.

The invention discloses an unmanned aerial vehicle patrol path decision-making method in a complex environment, and relates to the technical field of unmanned aerial vehicle path planning, and the method comprises the steps: taking each to-be-patrolled position as a node to construct an undirected topological graph; generating steady-state distribution of each node patrolled by the unmanned aerial vehicle according to topological constraints and node importance of the undirected topological graph; generating a plurality of candidate state transition matrixes with steady state distribution; performing dynamic environment evaluation on obstacles of paths corresponding to the candidate state transition matrixes so as to determine selectable speeds and directions of the unmanned aerial vehicle on the selected paths; dynamically adjusting the weight of the decision-making network according to the dynamic environment evaluation result, and generating a motion execution command by using the decision-making network after weight adjustment; and controlling the unmanned aerial vehicle to move among the to-be-patrolled positions according to the motion command. According to the invention, dynamic environment evaluation, path planning, decision network weight adjustment and motion command generation are carried out on the obstacle, so that efficient intelligent decision making of the unmanned aerial vehicle in a complex environment can be realized.

The application discloses a kind of complex environment under unmanned plane patrol path decision method, it is related to unmanned plane path planning technical field, method includes: with each to be patrolled position as node to construct undirected topological graph;According to the topological constraint of undirected topological graph and node importance generates the steady state distribution of each node patrolled by unmanned plane;Generate multiple candidate state transition matrix with steady state distribution;Dynamic environment evaluation is carried out to the obstacle of each candidate state transition matrix corresponding path, to determine the selectable speed and direction of unmanned plane on selected path;According to the weight of decision network dynamic adjustment according to dynamic environment evaluation result, and then generate execution motion command using the decision network after weight adjustment;According to motion command, unmanned plane is controlled and moves between each to be patrolled position.The application can realize the intelligent decision of unmanned plane under complex environment efficiently by dynamic environment evaluation, planning path, adjusting the weight of decision network, generating motion command.

The present application discloses a method for making patrol path decisions of a swarm of unmanned aerial vehicles (UAVs) under resource constraints, which relates to the technical field of UAV path planning. The method comprises: discretizing the actual physical locations to be patrolled to construct an undirected topological graph; generating a steady-state distribution of each patrol node according to the topological constraints and the importance of the nodes; generating a plurality of transfer matrices with the same steady-state distribution but different transfer characteristics according to a multi-stage entropy-driven random matrix optimization algorithm; initializing the position of a navigator and determining the path selected by it according to the transfer matrix; implementing adaptive active positioning decisions under positioning constraints according to the navigator's reference path to ensure path tracking effects; according to the navigator's path selection and tracking, the followers form a humanoid grouping cluster with the navigator through a reward function; according to the multi-state transfer matrix and the humanoid grouping, automatically switching to the next transfer matrix when a transfer number threshold is reached, thereby realizing intelligent patrol path decisions of the UAV swarm under resource constraints.

The invention relates to the technical field of airflow optimization, in particular to an airflow optimization method of a shell drying system. The method comprises the following steps that environmental parameters in a shell drying cabin are obtained through experimental testing, and data analysis is conducted on the environmental parameters through standard deviation and variable coefficients; establishing a CFD simulation model based on a Navier-Stokes equation, and setting and initializing the CFD simulation model to identify steady-state distribution data of wind speed, temperature and humidity on the surface of the shell; based on a CFD simulation model, a local airflow insufficient area is determined, and the number of fans and the air supply angle are optimized; a brand-new dry environment wind field vector airflow organization and adjustment device, a centralized rotating wheel dehumidification system and an automatic air volume control strategy are introduced; and the optimized shell drying system is subjected to experimental verification again. According to the airflow optimization method of the shell drying system, the wind field vector airflow organization and adjustment device and the centralized rotating wheel dehumidification system are combined with an automatic air volume control strategy, and accurate control over airflow and humidity is achieved.

The invention provides a multi-user joint steady-state distribution tensor determination method and related equipment, which effectively protect the privacy of data. The method comprises the following steps: constructing a user frequency tensor for each user in a plurality of users, and generating a noise tensor; determining a synthetic tensor with a differential privacy protection characteristic according to the noise tensor and the user frequency tensor; performing normalization processing on the user frequency tensor and the synthetic tensor; constructing a DP-2M Markov model based on the normalized user frequency tensor and the synthesized tensor, and solving the DP-2M Markov model to obtain an SJE of each user and a DP-SJE of each user; determining a space-time distance and a semantic distance between the SJE and the DP-SJE; the DP-SJE of each user is optimized according to the space-time distance and the semantic distance; and sending the synthetic tensor and the optimized DP-SJE to a cloud device, so that the cloud device determines a user influence coefficient according to the synthetic tensor, and determines a multi-user DP-SJE according to the user influence coefficient and the optimized DP-SJE.

This application discloses an unsupervised cross-modal hash retrieval method based on steady-state distribution and clustering. The method constructs an alignment loss function based on a similarity matrix, a first encoding hash code, and a second encoding hash code; obtains a first soft assignment corresponding to the first encoding hash code and a second soft assignment corresponding to the second encoding hash code through a pseudo-classifier, and constructs a cluster-level contrast loss function based on the first soft assignment and the second soft assignment; fuses the first encoding hash code and the second encoding hash code to obtain a fused encoding hash code; constructs a steady-state loss function and a quantization loss function based on the first encoding hash code, the second encoding hash code, and the fused encoding hash code; constructs a total loss function by combining the alignment loss function, the cluster-level contrast loss function, the steady-state loss function, and the quantization loss function; and determines the trained unsupervised cross-modal hash retrieval model based on the convergence of the total loss function. This application can improve the accuracy of cross-modal retrieval.

The invention provides an AI contract tamper-proofing method based on an OCR + large language model, belongs to the technical field of large language models, and improves image quality by performing multi-scale adaptive enhancement and super-resolution reconstruction on a contract scanning image. A semantic bridging fusion model is adopted to deeply fuse an OCR recognition result and an enhanced image under a sparse coding framework to realize context correction of low-confidence characters, semantic normalization is performed on a corrected text, and a term semantic vector sequence is established; the steady-state distribution field of the semantic concentration is calculated based on the fluid dynamic diffusion equation, the concentration gradient matrix is extracted to serve as the text fingerprint, contract tampering detection with robustness to the OCR recognition error is achieved through fingerprint similarity comparison, and the technical problem that the false alarm rate is high due to the fact that contract tampering detection is sensitive to the OCR recognition error is solved.

This invention discloses a real-time monitoring and optimization system for intelligent factory production processes, belonging to the field of intelligent factory production management. Specifically, it includes: acquiring process parameters and production action sequence data of the pipe fitting forming process using the Industrial Internet of Things (IIoT) and transmitting them to a data processing center; constructing a motion waste analysis model based on Markov chain theory and the motion sequence data; identifying motion waste by calculating state transition probabilities and steady-state distributions and comparing them with standard motion flows; and feeding the results back to the production management system; combining process parameters and motion waste identification results, the production management system uses queuing theory and operations research to construct a dynamic bottleneck station identification model, calculating the probability of each station becoming a bottleneck in real time, and determining the dynamic bottleneck station based on the amount of work-in-process inventory and production efficiency; and adjusting the production cycle based on the dynamic bottleneck station identification results to achieve real-time monitoring and optimization of the production process, thereby improving production efficiency and quality.

The invention discloses an unmanned aerial vehicle communication multi-priority queue modeling and performance analysis method, and the method comprises the steps: building an extensible state model through a WiMarkov chain and a hypercube transfer unit, and achieving the low-delay access of different priorities through a threshold and a window in combination with a weighted COS statistics and fixed backoff mechanism; semi-analytical expressions of the success rate and the time delay are obtained through steady-state distribution and generation function approximation, and parameters are learned through optimization constraints. Simulation shows that the low-load success rate is larger than or equal to 99%, the high-priority delay is still kept at the millisecond magnitude during high load, and the method is suitable for an unmanned aerial vehicle network communication system with the strict real-time requirement.

This invention relates to the field of fire safety inspection technology and discloses a method for generating county-level fire safety evaluation standards based on artificial intelligence. The method includes receiving a raw county-level fire data set containing fire records from multiple dimensions; performing multiple rounds of cross-validation within the set to filter for a stable set of elements; inputting this set into a dynamic semantic network that simulates element dependencies and transmission relationships using nodes and associated edges; injecting real-time county-level environmental state parameters to trigger node state migration; and reconstructing the text-based fire safety evaluation standard clauses according to the final steady-state distribution. By obtaining stable elements through multiple rounds of cross-validation, and generating environmentally adaptable clauses through the dynamic semantic network combined with real-time parameters, the method achieves stable standard framework elements and dynamic response clauses to the environment.

This invention discloses a patrol path decision-making method for unmanned systems in adversarial environments, belonging to the field of autonomous unmanned systems and intelligent decision-making technology. The method includes: acquiring an undirected patrol topology graph of the physical environment to be patrolled; calculating the steady-state distribution of the target based on the value parameters, attack time parameters, and degree of each node; and defining a patrol strategy unpredictability index weighted by intra-strategy entropy and inter-strategy entropy. PSUI To address the uncertainty of strategies in terms of microscopic randomness and macroscopic diversity, a simulated annealing dual-entropy balance optimization algorithm is employed to maximize... PSUI To achieve the goal, a set of state transition matrix strategies is generated under the constraints of topological adjacency, row normalization, and target steady-state distribution. The unmanned system is then controlled to perform spatiotemporally decoupled patrols through random matrix switching and probabilistic transitions within the matrix. This invention unifies patrol strategy evaluation and optimization, significantly improves the unpredictability of patrol paths, and enhances the protection capabilities for critical infrastructure.

The application relates to the technical field of fault analysis, and discloses a random fault injection method and an injection system for a train air brake system, which comprises the following steps: a simulation model of a target air brake system is constructed; all historical fault information is classified to obtain fault degree information and fault type information, and a fault degree state transition diagram and a fault type state transition diagram of the target air brake system are drawn; a fault type Markov chain is constructed according to the fault type state transition diagram, and a fault degree Markov chain is constructed according to the fault degree state transition diagram; a state transition probability matrix is determined, and a steady-state distribution is solved; a probability distribution of all fault information is set according to the steady-state distribution, and all fault information is randomly extracted in layers based on the probability distribution; and the extracted fault information is injected into the simulation model; the application solves the problem that the existing fault injection method cannot conveniently and effectively perform random fault injection on the train air brake system.

The invention relates to the technical field of petroleum development, and discloses a rock core water injection mode determination method, device and equipment and a storage medium, and the method comprises the following steps: respectively carrying out steady-state water injection and unsteady-state water injection on an experimental rock core under preset experimental parameters; respectively performing CT scanning on the steady-state water injection process and the non-steady-state water injection process to obtain a steady-state distribution result of an internal fluid of the experimental rock core in a steady-state water injection mode and a non-steady-state distribution result of the internal fluid in a non-steady-state water injection mode; comparing a steady-state distribution result with an unsteady-state distribution result; determining a water injection mode suitable for the actual core in the mine field according to a comparison result. According to the method, the water injection mode suitable for the actual rock core in the mine field is determined by comparing the steady-state distribution result and the non-steady-state distribution result of the experimental rock core, the application conditions of the actual rock core in the mine field can be determined, the situation that an inapplicable water injection mode is adopted in the mine field is avoided, and the oil recovery rate is effectively increased.

The application discloses a kind of unmanned system patrol path decision-making method under hostile environment, belong to autonomous unmanned system and intelligent decision-making technical field.The method includes: obtaining the undirected patrol topology graph of the physical environment to be patrolled, target steady-state distribution is calculated based on the value parameter of each node, attack time parameter and degree;Define the patrol strategy unpredictability index formed by the weight of strategy internal entropy and strategy entropy PSUI , to quantify the uncertainty of strategy in micro-randomness and macro-diversity;Using simulated annealing double-entropy balance optimization algorithm, to maximize PSUI As target, generate state transition matrix strategy set under the constraints of meeting topology adjacency, line normalization and target steady-state distribution;Control unmanned system to carry out space-time decoupling patrol by random switching matrix and probabilistic transition in matrix.The application realizes the unity of patrol strategy evaluation and optimization, significantly improves the unpredictability of patrol path, and enhances the protection ability to key infrastructure.

The invention relates to a multi-state conversion runoff prediction method based on structural fracture recognition, and is suitable for the technical field of hydrology and water resource prediction. The method comprises the following steps: acquiring a historical runoff sequence and performing stationarity test; the global residual sum of squares is minimized under the piecewise linear regression framework; establishing a mean value model for the stationary sequence, and calculating a one-step prediction residual sequence as a disturbance term estimation value; taking the residual error as input, establishing a first-order Markov state transition structure, and jointly estimating a state transition probability matrix of the state transition structure and fluctuation parameters of each hidden state; setting an initial state probability vector as steady-state distribution or uniform distribution of the state transition probability matrix, and obtaining a smooth state probability at each moment through backward recursion by using full sample information; and generating one-step rolling condition prediction under each state, weighting according to a state prediction probability to obtain a rolling point prediction result of the runoff, executing inverse transformation according to recorded difference and transformation parameters, and recovering to an original physical quantity scale.

The present application relates to the technical field of airflow optimization, in particular to an airflow optimization method for a shell drying system. The method comprises the following steps: obtaining environmental parameters in a shell drying cabin through experimental testing, and performing data analysis on the environmental parameters using standard deviation and coefficient of variation; establishing a CFD simulation model based on Navier-Stokes equation, setting initial CFD simulation model to identify steady-state distribution data of shell surface wind speed, temperature and humidity; determining local airflow insufficient area based on the CFD simulation model, optimizing fan quantity and air supply angle; introducing a brand-new drying environment wind field vector airflow organization and adjustment device, a centralized rotary dehumidification system and an automatic air volume control strategy; and performing experimental verification on the optimized shell drying system again. The airflow optimization method for the shell drying system realizes accurate control of airflow and humidity by combining the wind field vector airflow organization and adjustment device and the centralized rotary dehumidification system with the automatic air volume control strategy.

The invention belongs to the technical field of power grid frequency analysis, and particularly relates to a once-through boiler frequency modulation simulation method and device considering heat distribution and storage characteristics. The method comprises the steps that the steady-state distribution condition of physical property parameters along the height of a hearth is calculated based on heat load distribution nonuniformity, and a dynamic heat exchange model is constructed; aiming at the dynamic heat exchange model, building a thermodynamic system dynamic simulation model of the boiler; based on a thermodynamic system dynamic simulation model, the method is embedded into a traditional primary frequency modulation model of a power system for simulation verification and comparison. According to the method, the boiler model is built by using the heat flow method, the mismatching relation between complex fluctuation and large delay of a power plant boiler thermodynamic system in power grid frequency modulation is revealed, and under the working condition of long-term power shortage or low load, the frequency modulation capability prediction precision is higher, and the prediction accuracy is higher. And optimization of a power grid frequency modulation scheduling instruction and stability control of the power grid frequency under high-proportion new energy grid connection are facilitated.

The invention discloses an extracorporeal membrane oxygenation pipeline pressure real-time monitoring method, and relates to the technical field of extracorporeal membrane oxygenation pipelines, and the method comprises the following steps: extracting steady-state distribution segments with stable pressure and balanced amplitude from a flow direction response distribution cluster under the condition that the pipeline is determined to be externally compressed but not completely closed, and determining the flow direction response distribution cluster; the steady-state distribution segment is subjected to fluctuation consistency turn-back analysis, and the pressure signal abnormal stability phenomenon under the condition that the pipeline is externally pressed but not completely closed is determined; on the basis of determining the abnormal stability phenomenon of the pressure signal, a contrast relation between a structural steady-state block and a flowing vector direction path is established, and whether the pipeline is in a limited operation state or not is judged by comparing vector direction deviation with change characteristics of the steady-state block. According to the invention, the problem that the pipeline is limited and difficult to identify under the illusion that the pressure is stable is solved, and accurate monitoring and dynamic regulation and control of the operation state of the extracorporeal membrane oxygenation pipeline are realized.

The present invention relates to a circuit system health prediction method based on low-frequency noise and deep learning, which includes the following steps: step 1: assigning stability importance to the circuit system; step 2: extracting low-frequency noise and obtaining steady-state distribution characteristic parameters of the low-frequency noise of the components in the time domain analysis; step 3: processing low-frequency noise signal data and establishing a neural network model; step 4: completing the training of the neural network model and obtaining the prediction result of the circuit life. The present invention realizes circuit health analysis by establishing a deep learning prediction model based on the analysis of the low-frequency noise of the components. It can adapt to circuit systems with different failure criteria and is suitable for accurate diagnosis in the engineering field. Compared with the traditional aging test method based on modeling of a large number of test results, it has obvious advantages; it does not require overstressing during the test process, realizes non-destructive testing of the circuit, and realizes functional prediction of the circuit system timing based on health diagnosis.

The application provides a function generalization and attractor determination method for a gene regulation network, for each target gene in the gene regulation network, a Klimt correlation coefficient between the target gene and all regulation genes corresponding to the target gene is calculated, and a regulation gene with the highest Klimt correlation coefficient value is selected as a guide to set other unobserved values in a truth table of the gene regulation network; and based on the truth table of the gene regulation network, an attractor is obtained by traversing each state of the gene. The application uses the Klimt correlation coefficient to calculate the correlation between genes, effectively improves the problem of low accuracy of the existing algorithm, and through a large number of experimental comparisons between the algorithm using the Klimt correlation coefficient and the algorithm proposed by the prior art, it is proved that the method of the application reduces the average sensitivity error and the steady-state distribution distance, and further realizes the effect of more accurate prediction of the truth table.

The present invention discloses a method for quantum bit quantum flip warning and threshold setting of a quantum computer, which sets an initial threshold for quantum bit flip of the quantum computer based on a quantum flip prediction model; uses a threshold prediction model to dynamically set the quantum flip threshold to achieve quantum flip warning and replacement of the initial threshold; when the threshold prediction model has used up all the quantum flip thresholds in the historical database that conform to the thresholds predicted by the quantum flip prediction model, a threshold adjustment model is constructed to generate a new dynamic threshold to replace the current threshold. The present invention uses a Bayesian threshold prediction model to adjust the monitoring threshold to solve the problem that the method of steady-state distribution of the quantum flip prediction model based on the Markov chain cannot perform calculations on the continuous-time Markov chain, resulting in prediction distortion of the network warning model.

The invention discloses an unsupervised cross-modal Hash retrieval method based on steady-state distribution and clustering, and the method comprises the steps: constructing an alignment loss function according to a similarity matrix, a first coding Hash code and a second coding Hash code; obtaining a first soft assignment value corresponding to the first coding hash code and a second soft assignment value corresponding to the second coding hash code through a pseudo classifier, and constructing a cluster-level comparison loss function according to the first soft assignment value and the second soft assignment value; fusing the first coded Hash code and the second coded Hash code to obtain a fused coded Hash code; according to the first coding Hash code, the second coding Hash code and the fusion coding Hash code, constructing a steady-state loss function, and constructing a quantization loss function; combining the alignment loss function, the cluster-level comparison loss function, the steady-state loss function and the quantization loss function to construct a total loss function; and determining a trained unsupervised cross-modal hash retrieval model according to the total loss function convergence. According to the method and the device, the accuracy of cross-modal retrieval can be improved.

The invention discloses a method for modeling and evaluating reliability of an intelligent diagnosis model, which comprises the following steps of: firstly, regarding an output sequence of the intelligent diagnosis model as a Markov chain, defining a correct classification state and an error state according to a classification result, and calculating a corresponding state transition space and a transition probability of each state; and the transition probability matrix can accurately reflect the classification performance of the model. Furthermore, the long-term stability probability of each state is determined by solving the steady-state distribution equation, the steady-state probability of the correct classification state is defined as the reliability index of the model, and the long-term classification stability of the model is quantitatively evaluated. And finally, calculating the steady-state distribution, so that the method can update the reliability index in real time in the model operation process to adapt to the dynamic change of different tasks and data sets. The invention provides an accurate and efficient intelligent diagnosis model reliability evaluation means, model design optimization and parameter adjustment are guided, and the stability of the diagnosis model in practical engineering application is effectively improved.

The application relates to a training method of a coherent Ising machine and related equipment. The training method comprises the following steps: injecting noise into the coherent Ising machine, so that, under the joint action of physical parameters and the noise, the continuous amplitude vector of the light pulse generated by the coherent Ising machine evolves to a steady-state distribution state subject to the Gibbs distribution according to the Langevin dynamics evolution rule, the physical parameters include pumping parameters, saturation parameters, coupling coefficients, bias field parameters and gains; after the continuous amplitude vector collected under the current steady-state distribution is taken as negative-phase amplitude data, one or more of the physical parameters are updated based on the update amount obtained from the deviation between the negative-phase amplitude data and positive-phase amplitude data, so that the continuous amplitude vector reaches a target steady-state distribution. The application solves the problem that in the traditional CIM machine, the parameters are mostly fixed or empirically adjusted, and the steady-state sample distribution of the light pulse changes greatly due to environmental drift and device errors.

The invention discloses a real-time monitoring and optimizing system for the production process of an intelligent factory, and belongs to the field of production management of the intelligent factory, and the real-time monitoring and optimizing system specifically comprises the following steps: obtaining technological parameters and production action sequence data of a pipe fitting forming process by using an industrial Internet of Things, and transmitting the technological parameters and the production action sequence data to a data processing center; on the basis of the Markov chain theory, an action waste analysis model is built according to action sequence data, action waste is recognized by calculating the state transition probability and steady state distribution and comparing with a standard action process, and a result is fed back to a production management system; the production management system constructs a dynamic bottleneck station identification model by using the queuing theory and the operational research in combination with the process parameters and the action waste identification result, calculates the probability that each station becomes a bottleneck in real time, and judges the dynamic bottleneck station in combination with the stacking number of products being processed and the production efficiency; the production takt is adjusted according to the dynamic bottleneck station recognition result, real-time monitoring and optimization of the production process are achieved, and the production efficiency and quality are improved.