1612 results about "Rapid response" patented technology

The invention provides a perception collaborative decision-making method and system based on multi-modal heterogeneous data fusion, and relates to the technical field of artificial intelligence, and the method comprises the steps: inputting a global environment situation perception graph into a pre-trained multi-target collaborative decision-making model; the multi-target collaborative decision-making model forms a multi-target decision-making feature set by analyzing the resource entities and the incidence relation in the graph; based on the multi-target decision feature set, decision optimization is carried out to obtain a comprehensive collaborative scheduling scheme; performing instruction analysis and packaging on the comprehensive collaborative scheduling scheme to obtain an executable instruction sequence; and issuing the executable instruction sequence to a corresponding decision node and a control terminal in parallel through a distributed communication architecture to complete real-time scheduling of resources and collaborative issuing of control instructions. According to the invention, by constructing a linkage mechanism of multi-modal data fusion, dynamic environment perception and collaborative decision execution, intelligent perception and quick response to a complex environment are realized.

The invention provides a method and a system for intelligently generating and dynamically adjusting a public emergency plan, which are oriented to the field of public safety emergency. The method comprises the following steps: fusing multi-source heterogeneous data to construct an extensible domain knowledge graph, establishing a standardized emergency instruction library, and carrying out multi-dimensional information labeling; automatic extraction of disaster elements is realized based on an entity recognition model of deep learning; analyzing association rules among the emergency entities through a semantic relationship mining technology; key information such as a disaster chain and resource distribution is rapidly obtained by using a map reasoning mechanism; carrying out cross-department resource collaborative allocation by adopting a multi-objective optimization algorithm to generate an optimal disposal scheme; a high-dynamic adjustment mechanism is constructed, and an emergency plan is dynamically optimized based on situation evolution prediction and real-time monitoring data; and finally, automatic generation and versioning management of the plan are realized. Through multi-mechanism cooperation of knowledge modeling, intelligent element analysis, semantic reasoning, dynamic optimization and predictive adjustment, the emergency plan generation efficiency, situation adaptability and resource allocation rationality are remarkably improved, and support is provided for quick response and scientific decision under emergencies.

The invention relates to the technical field of unmanned aerial vehicle scheduling, and discloses an emergency scene unmanned aerial vehicle task allocation method based on multi-objective optimization, and the method comprises the steps: obtaining a task parameter set of an unmanned aerial vehicle in real time through a standardized task interface, and obtaining a state data set of the unmanned aerial vehicle in real time through an unmanned aerial vehicle management platform; secondly, calculating an optimal task allocation scheme by adopting a multi-target particle swarm optimization algorithm and an improved PSO algorithm, and dynamically adjusting sudden emergency tasks and priority changes in combination with a task preemption mechanism; three task allocation plans including a time optimal scheme, a resource optimal scheme and a parameter optimal scheme are provided to ensure that tasks can be completed in the shortest time or executed at the lowest cost, and the final scheme is provided for a command center for decision making. According to the invention, the intelligence and flexibility of unmanned aerial vehicle scheduling are improved, and rapid response and global optimal resource configuration of unmanned aerial vehicle scheduling in an emergency scene are realized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a target event processing method, device, equipment and medium based on language model collaboration.The method comprises the steps that a target event is monitored and recognized, multi-source data associated with the target event are collected, the multi-source data are fused to generate fused data, and the fused data are sent to a server; and inputting the fusion data into a pre-training language model to generate a decision scheme, performing decision coordination and matching based on the decision scheme, distributing a processing agent, executing a matched decision task by using the processing agent, and generating a decision result. According to the invention, through fusion of multi-source data and pre-training language model deep reasoning and combination of an intelligent agent dynamic cooperation mechanism, intelligent perception, rapid response and cooperative processing of complex events are realized, and the adaptive ability and processing efficiency of the system in response to unknown faults and complex tasks are improved.

The invention relates to an LLM-based power distribution network knowledge graph construction and fault intelligent diagnosis method. The method comprises the steps of constructing a knowledge system, constructing a normal operation state model and an anomaly detection threshold value, performing fault diagnosis through an LSTM-BP cascade network, and performing fault classification. The abnormal parameters and the fault classification result are input into an LLM model, the fault type is judged, corresponding diagnosis information is obtained, and the system generates a scheduling instruction and an emergency decision suggestion according to the diagnosis information; and generating a detailed diagnosis report. According to the system, statistical analysis and deep learning algorithms are adopted for operation parameters such as voltage and current after preprocessing, and automatic detection and fault classification of abnormal parameters are realized in combination with a CNN recognition module; a preset rule base in the knowledge system is combined to accurately determine a fault type, generate a scheduling instruction and an emergency decision suggestion, and assist operation and maintenance personnel in rapid response and dynamic management; finally, a detailed diagnosis report is generated, thereby improving the fault handling efficiency and the guarantee capability of safe and stable operation of a power grid.

The invention discloses a PID temperature control system and method for a semiconductor etching machine, and relates to the field of temperature control, and the system comprises a coupling relation construction module, a temperature sensing confidence module, a decoupling compensation module, a coupling relation dynamic updating module, and a transient temperature change control module. According to the invention, by constructing the coupling relation matrix and the inverse matrix thereof, decoupling control of multi-area temperature control is realized, cross interference is effectively suppressed, and the temperature control precision is improved. And a multi-point temperature measurement fusion and anti-interference algorithm is introduced, so that the temperature measurement accuracy and the system robustness are enhanced. A response residual vector is associated with a control vector, and a coupling matrix is dynamically optimized, so that the system has self-learning and model self-adaption capabilities and adapts to working condition changes. And a transient temperature change quick response mechanism is set, so that the control priority can be improved and the PID parameters can be adjusted when the temperature changes suddenly, the response speed and the control stability of the system to abnormal temperature change are remarkably improved, and the thermal uniformity and the process consistency in the etching process are ensured.

The invention discloses an intelligent fire-fighting risk prediction and dynamic early warning method and system based on multi-source data fusion, and relates to the technical field of intelligent fire-fighting risk prediction, and the method comprises the steps: collecting multi-source fire-fighting data, and constructing a fire-fighting safety state multi-dimensional data matrix; constructing a risk prediction model based on the fire safety state multi-dimensional data matrix to carry out real-time risk scoring; and executing dynamic early warning through a risk prediction result. According to the method, through the multi-source data acquisition and fusion module, multi-type fire-fighting data are comprehensively acquired and standardized, a structured and multi-dimensional fire-fighting safety state matrix is constructed, and the problems of data dispersion and information splitting are solved. In combination with a risk collaborative prediction and scoring module, the risk level of each region is dynamically output based on multi-factor analysis, and the accuracy and foresight of risk prediction are improved. Through a dynamic early warning generation and linkage module, real-time grading early warning and linkage of a fire fighting system are realized, and rapid response and intelligent prevention and control of a high-risk area are realized.

The invention discloses a medical integrated module type electroacupuncture anesthesia therapeutic apparatus, and belongs to the technical field of medical apparatuses and instruments. A medical integrated module type electroacupuncture anesthesia therapeutic apparatus comprises a signal collecting and monitoring module, a main control module, a parameter self-adaptive adjusting module, a stimulation output module and a safety monitoring unit. The problems that existing current output parameters depend on manual adjustment and doctor experience, an objective quantitative basis is lacked, a real-time physiological monitoring and feedback mechanism is lacked in the treatment process, and current adjustment lags behind the state change of a patient are solved. Through a multi-parameter fusion decision algorithm and a closed-loop real-time feedback mechanism, 'experience-driven open-loop control 'of traditional electro-acupuncture equipment is upgraded to'data-driven closed-loop control', the subjectivity and hysteresis problems of manual adjustment are solved, and treatment safety and individual adaptability are greatly improved through high-precision monitoring and quick response.

The invention discloses a power distribution terminal equipment fault intelligent diagnosis method and system based on the Internet of Things. The method comprises the following steps: S1, collecting multiple items of operation data of a power distribution terminal to construct a time sequence sample; s2, extracting power disturbance characteristics based on a sliding window, generating a behavior coupling matrix and a topological connection matrix, and calculating node redundancy; s3, fusing the two types of relationships to construct a dynamic graph; s4, inputting a graph diffusion network, fusing a structure and a behavior diffusion result, and generating a node state feature vector; s5, inputting a multi-label classification model to identify the fault type and probability; s6, generating a response instruction in combination with the fault type and the redundancy; s7, response is executed, the relation matrix and the classification model are updated, and the diagnosis process is optimized in a closed-loop mode. According to the invention, accurate fault identification and quick response of the power distribution terminal are realized, and the intelligent operation and maintenance level of a power supply system is improved.

The invention relates to the technical field of micro-grid dispatching, and particularly provides an intelligent dispatching method and system for a photovoltaic-storage-diesel micro-grid, and the method comprises the steps: obtaining the operation data of an energy storage battery, the operation data of a diesel generator, and the global operation data of the micro-grid; evaluating the power response capability of the energy storage battery according to the operation data of the energy storage battery, and evaluating the quick response capability of the diesel generator according to the operation data of the diesel generator; analyzing the stability risk level of the micro-grid according to the global operation data, the power response capability and the quick response capability of the micro-grid; adjusting scheduling optimization target emphasis according to the stability risk level; emphatically acquiring an energy storage battery charging and discharging instruction, a diesel generator starting and stopping instruction and an output instruction according to the adjusted scheduling optimization target, and executing all the acquired instructions; according to the method, the problem that the system stability is sacrificed due to blind pursuit of cost minimization in a traditional strategy can be avoided.

The invention discloses a medical personnel scheduling table management method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: acquiring multi-dimensional portrait data and department resource map data of medical personnel to be scheduled in a department; based on the multi-dimensional portrait data and preset scheduling rules and shift demands, a basic scheduling table of a preset period is generated, and the basic scheduling table covers the conventional workload of a set proportion of the medical personnel; initializing a general scheduling table according to the basic scheduling table; based on the real-time medical demand data, the department resource map data and the current state of the general scheduling table, dynamic adjustment information of the general scheduling table is generated, and the dynamic adjustment information comprises a newly added scheduling instruction or an original shift change instruction; and updating the general scheduling table in real time according to the dynamic adjustment information. According to the invention, the rapid response and dynamic adjustment capability to emergencies and real-time medical requirements can be improved, and the problem that the medical staff scheduling table management flexibility and rationality are insufficient is solved.

The invention discloses a network attack active defense strategy optimization method based on deep reinforcement learning, and the method comprises the following steps: collecting multi-source data of a network environment, and carrying out the feature clipping and white list feature reservation; performing normalization and coding processing to generate a security situation vector; constructing a multi-index reward function, and generating an instant reward value and an event-level reward value; executing a double-closed-loop mechanism through an improved PPO model, and respectively outputting an instant strategy instruction and a long-term strategy parameter; performing multi-source evidence commissioning on the instant strategy instruction and the security situation vector, and judging a key evidence loss condition to obtain an execution token; inputting a risk budget pool to carry out resource quota checking, and executing anti-jitter and cooling control; and optimizing parameters of the multi-index reward function through a causal account book. According to the method, rapid response and continuous optimization of various attack behaviors can be realized, the defense effect and the resource utilization rate are considered, the false report and missing report rate is reduced, and the self-adaptability and stability of a network defense system are improved.

The invention discloses a self-adaptive adjustment method and system for twisting tension of a copper stranded wire. According to a state analysis result, a dynamic coupling adjustment mechanism is started, rapid response adjustment of tension is achieved by adjusting the matching relation between the rotating speed of a stranding cage and the linear speed of a traction wheel and linkage with the exciting current of a magnetic powder brake, tension data are rapidly collected through a sensor, and after filtering and abnormal value processing, the tension is obtained. The fluctuation frequency and amplitude are analyzed immediately, and adjustment is started once the fluctuation frequency and amplitude exceed a set range. During adjustment, the ratio of the rotating speed of the stranding cage to the speed of the traction wheel is adjusted, the current of the magnetic powder brake is linked, mechanical adjustment and electromagnetic braking are matched, and the response speed is much faster than that of traditional single-parameter adjustment. Therefore, the stress of the copper stranded wire in the stranding process is uniform, the wire diameter is not thinned or burrs are not formed on the surface due to sudden change of tension, the stranded wire pitch is uniform, the process requirement is met, and the influence on the stranded wire quality due to sudden change of the tension is avoided as much as possible.

The invention relates to the technical field of rail traffic equipment maintenance and artificial intelligence image recognition, in particular to a rail vehicle cloud edge cooperative detection method and system and inspection equipment. The method comprises the following steps: acquiring image data of an area to be detected, synchronously recording attitude information of shooting equipment, carrying out image quality scoring on the image data, if the image quality score reaches a preset threshold value, calling a local target detection model to carry out defect identification on an image, and outputting an identification result containing a target type, a position and confidence; if the confidence coefficient of the recognition result is lower than a set threshold value, the image data and the posture information are uploaded to a cloud server, a cloud high-precision recognition model is called for secondary recognition, and a cloud recognition result is obtained. The real-time requirement is met through quick response of the local model, the cloud model rechecks a low-confidence result, efficiency and precision are both considered, and the contradiction that in the prior art, manual judgment standards are different, and speed and precision are difficult to consider is solved.

The invention discloses a shield tunnel muck improvement parameter prediction method and system, and relates to the technical field of tunnel engineering construction.The method comprises the steps that a multi-modal sensor is arranged at a key part of a shield tunneling machine, and tunneling parameters, stratum parameters, multi-modal sensor signals, muck physical properties and modifier injection parameters are collected; then, extracting change characteristics, calculating mutation sensitive factors and carrying out working condition judgment; constructing a normal prediction sub-model library and a sudden change quick response model, calculating a stratum model adaptation index and performing model suitability judgment; establishing a working condition coupling prediction model by using a graph neural network, extracting nonlinear coupling characteristics, calculating a working condition coupling index and judging prediction stability; actual construction performance data are collected and compared with a model prediction result, a prediction correction index is calculated, deviation analysis is carried out, and a corresponding correction strategy is triggered. According to the method, intelligent prediction and dynamic correction of shield tunnel muck improvement parameters can be achieved, and the safety and stability of the construction process are improved.

The invention relates to the field of artificial intelligence and computer science, in particular to a causal structure and latent variable-based adaptive reinforcement learning reasoning migration method, which comprises the following steps of: constructing a causal world model fused with multi-modal observation and a decoupling latent variable space; establishing a hierarchical inference engine comprising an intuition layer, a conventional layer and a planning layer; pre-training a quick response and judicial planning dual-mode strategy and generating an interpretable fuzzy rule base; performing calculation level coarse tuning based on task identification and causal complexity; evaluating the real-time state criticality through an adaptive neural fuzzy system and dynamically switching a decision mode; after the action is executed, the threshold and the rule are subjected to closed-loop optimization, and cross-environment efficient migration is realized by utilizing a causal modularization characteristic. According to the technical scheme, consumption of computing resources is remarkably reduced on the premise that decision precision and safety are guaranteed, and the response speed and cross-scene adaptive capacity of a system on edge equipment are improved.

The invention discloses a factory equipment predictive maintenance scheme optimization method based on reinforcement learning, and relates to the technical field of equipment maintenance scheme prediction, and the method comprises the steps: carrying out the fusion through employing a space-time attention mechanism based on an equipment health index, equipment operation duration and a current load level, generating an equipment state vector, constructing a reinforcement learning framework, and carrying out the optimization of a factory equipment predictive maintenance scheme. A multi-target reward function is embedded in the reinforcement learning framework, a PPO algorithm is adopted for optimization, a reinforcement learning model is generated, the reinforcement learning model is trained by using the equipment state vector, the trained reinforcement learning model is obtained, the trained reinforcement learning model is deployed to an actual industrial control unit, the latest equipment state vector is received in real time, and the final equipment state vector is obtained. And obtaining an optimal maintenance strategy. And converting the optimal maintenance strategy into a specific maintenance instruction and executing the specific maintenance instruction. Through the factory equipment predictive maintenance method, rapid response to emergencies is facilitated, greater loss is avoided, resource allocation is optimized, and enterprise competitiveness is enhanced.

The invention discloses an artificial intelligence city emergency management system based on space-time prediction, which belongs to the technical field of city emergency management and comprises a data acquisition unit, a data preprocessing unit, a data processing unit, a model optimization unit, an emergency decision-making unit and an intelligent decision-making unit. Large-scale information from various sensors, monitoring systems and historical data is analyzed and processed by adopting a machine learning algorithm, potential risk modes and trends are identified, the information can be quickly screened and integrated by adopting a big data analysis technology, the real-time performance and accuracy of key data are ensured, and the real-time performance of the system is improved. The whole system generates early warning information by using a prediction result, and transmits the information to related departments in time through the emergency decision unit, so that rapid response and effective processing of emergencies are realized, the emergency response speed and accuracy can be improved, an emergency strategy can be flexibly adjusted according to real-time conditions, and the safety of the system is improved. And the requirements of cities for coping with complex and changeable conditions are met.

The invention discloses an intelligent production scheduling management system for a hardware mold, relates to the technical field of intelligent manufacturing control, and is used for solving the problem of slow scheduling reconstruction under disturbance. According to the method, rapid response and local adjustment of resource disturbance events such as equipment faults and personnel absence are realized by constructing a disturbance identification and scheduling reconstruction mechanism, the system acquires equipment utilization and scheduling information, generates a joint time sequence, calculates a disturbance sensitivity index and constructs a task resource influence map, and the task resource influence map is subjected to resource scheduling. Positioning a disturbed task and a critical path; a scheduling adjustment strategy in a minimum disturbance range is generated in combination with task priority factors, equipment load balance coefficients and scheduling cost, scheduling confusion caused by global rearrangement is avoided, a system is linked with mold allocation and task queue control, a closed-loop scheduling execution process is formed, and the scheduling efficiency is improved. And the production scheduling stability, the self-adaptive capability and the task completion efficiency can be improved in a complex manufacturing scene.

The invention discloses an intelligent energy-saving control method for an air compression system based on data driving. The intelligent energy-saving control method is suitable for an industrial air compression station with multiple parallel units. According to the method, operating parameters of all air compressors and pressure and flow data of a header pipe are collected in real time, an energy efficiency model of the output flow and the gas-electricity ratio of all frequency converters is established, and the future flow requirement of the header pipe is dynamically predicted in combination with the characteristics of a fixed frequency machine. Based on a prediction result and a single-machine energy efficiency model, the basic load and the fluctuation load are distributed according to the overall energy efficiency optimization principle of the system, the start-stop sequence and the rotating speed of each unit are determined, and stable main pipe pressure, continuous gas supply, rapid response and good operation flexibility are achieved. By means of the method, the energy efficiency difference of all the units can be fully utilized, energy consumption fluctuation and equipment abrasion caused by frequent starting and stopping are reduced, and the overall energy utilization rate of the air compression system and the reliability of the industrial production process are improved.

The invention discloses an intelligent fire detection and quick response method based on image recognition. The method comprises the following steps: S1, synchronously acquiring and preprocessing multi-source image data; s2, carrying out multi-modal feature fusion and early fire identification; s3, intelligent grading response based on deep reinforcement learning: constructing a fire behavior grade dynamic evaluation network, inputting fused multi-modal features, outputting fire behavior grade probability distribution and a crisis index, judging a fire behavior grade according to a dynamic threshold value, and triggering a predefined grading response strategy; s4, performing cross-scene adaptive transfer learning; and S5, carrying out system self-diagnosis and dynamic optimization. According to the invention, through time-space synchronous acquisition and deep fusion of visible light, infrared and smoke multi-mode data, vision, thermal radiation and smoke characteristics of flames are integrated, and common interferences such as real fire behavior and lamplight, light reflection, a moving heat source, water mist and dust are effectively distinguished by using an improved lightweight CNN, a self-adaptive background temperature model and an environment airflow model.

The invention relates to an electric power communication optical fiber fault monitoring and visualization method and system, and belongs to the technical field of optical fiber communication monitoring, and the method comprises the following steps: carrying out the real-time monitoring of an electric power communication optical fiber through a non-intrusive monitoring system, collecting an optical power signal in real time, and constructing an optical fiber resource database; when the non-intrusive monitoring system detects that optical power attenuation exceeds a preset threshold value, an optical time domain reflectometer OTDR is started to perform intrusive detection on an abnormal optical fiber, and an OTDR echo curve is obtained; extracting a composite feature vector of an OTDR echo curve, and identifying a fault type through a multi-stage fusion algorithm in combination with historical fault record data; according to the OTDR detection point coordinates, the optical path distance and the geographic information data, the position of a fault point is calculated, and the optical fiber topology, the position of the fault point and the fault type of the fault point are displayed in a visual interface. According to the invention, accurate fault identification and quick response are realized, and the operation and maintenance efficiency is improved.

The invention relates to the technical field of industrial network control, in particular to a side-cloud collaborative software-defined industrial control system, which comprises a side control action configuration module, a multi-source signal collection module, a state mutation recognition module, a cloud scheduling module and a collaborative control generation module. In the invention, through decoupling control action configuration, multi-channel signal acquisition, data label construction, sudden change state identification and a remote scheduling execution process and realizing standardized processing, an edge control basic instruction structure body can be quickly constructed based on an equipment state and a unified time sequence data label mechanism is established; in combination with the dynamic sensing ability of a sudden change state and a feedback point identification mode, efficient discrimination and rapid classification of key control points can be realized, task chain execution can be rapidly mapped to a key path and real-time regulation and control are implemented, rapid response to industrial field multi-source change and accurate issuing of a control instruction are realized, and the task chain execution efficiency is improved. And the real-time performance and the collaboration of the control process are greatly improved.

The invention provides a heterogeneous building air conditioner group quick response control method based on physical information deep learning, and the method comprises the steps: 1, obtaining a real-time discrete step scheduling instruction issued by a power grid, obtaining the thermodynamic parameters and equipment inertia time constants of all building units in a heterogeneous building group, obtaining the outdoor temperature, and carrying out the real-time discrete step scheduling instruction; the solar radiation intensity and the indoor air temperature of each building unit; step 2, extracting spatial-temporal characteristics of each building unit based on a multi-head self-attention mechanism, and generating initial distribution target power of the air conditioner; 3, inputting the initial allocation target power into a pre-trained cascaded physical information neural network (PINN), and predicting power and temperature evolution tracks in a future scheduling period; 4, calculating an out-of-limit risk index of each unit, judging whether to execute a quantile risk migration strategy or not, and generating a final distribution target power of the air conditioner; and 5, calculating the transient climbing deviation between the actual aggregation power and the target power of the power grid, and carrying out deviation compensation.

The invention relates to the technical field of three-dimensional modeling, in particular to an on-orbit three-dimensional modeling method and system. The method comprises the following steps: mapping a task demand input by a user into a task specification object; carrying out mixed retrieval on the task template library, carrying out atlas consistency verification on the retrieved task templates, and sorting candidate templates after the verification is passed; expanding the high-score candidate template into an executable high-score operator sequence to serve as a task chain to be output; mapping each operator in the task chain into an MCP calling node to obtain an MCP calling chain; and calling a data processing tool and a three-dimensional modeling tool to generate a preliminary three-dimensional model, then performing incremental correction and local optimization through a dynamic feedback updating mechanism, and downloading the updated incremental three-dimensional data to a ground data center. Automatic task analysis is achieved, and manual intervention is not needed. The method has the lightweight modeling capability, knowledge-driven complementation can be realized, and a collaborative mode of on-orbit quick response and ground fine reconstruction is realized.

The invention relates to the technical field of inversion systems, in particular to a coastal wetland forest ecosystem biomass and carbon sink rapid inversion system. According to the technical scheme, the system comprises a data acquisition and preprocessing module, an inversion module based on machine learning, a spatial heterogeneity adaptive module, a dynamic monitoring and updating module and a result visualization and output module. According to the method, a data basis is provided for accurate inversion through multi-source data fusion, and meanwhile, an inversion model combining the generative adversarial network and the long-short-term memory network realizes combination of a physical model and deep learning, so that the interpretability of the physical model is reserved, and the model performance is improved by utilizing the strong fitting capability of the deep learning; according to the invention, the adaptability of the system in a complex coastal wetland environment is improved through the spatial heterogeneity adaptive module, and the reliability of an inversion result is improved; according to the invention, real-time dynamic monitoring of biomass and carbon sink is realized through the dynamic monitoring and updating module, and rapid response of ecological system change is supported.

The invention belongs to the technical field of mechanical arm control, relates to a bionic mechanical arm elbow joint adjusting angular velocity control method, and solves the problem that an existing method is difficult to meet the collaborative requirements of smooth starting, smooth transition and quick response in bionic actions. Acquiring starting and target positions to generate a motion trail; deducing an expected angle sequence based on forward and reverse kinematics, calculating a target angular velocity, and collecting current angle and angular velocity information in real time; an angle error signal is constructed, an angular velocity control instruction is generated by using a position type PID controller, and PID parameters are dynamically set through an improved algorithm based on state optimization; and a control instruction is input into the elbow joint driving unit, and dynamic adjustment and closed-loop control of the angular speed are achieved. The effect of the method is verified through experiments, and the experiments prove that the precision, robustness and dynamic response capability of angular velocity control of the bionic mechanical arm can be remarkably improved through the method.

The invention relates to the technical field of numerical control machine tool monitoring, and discloses a numerical control machine tool intelligent monitoring system based on digital twinning. A digital twin modeling module of the system obtains geometric parameters, physical attributes and historical operation data of a numerical control machine tool in real time, and constructs a dynamically updated virtual machine tool model; the working condition sensing module collects vibration, temperature and current data through a multi-source sensor, and generates a running state feature set through time sequence alignment processing. The exception response module is used for matching an exception mode library according to the feature set and outputting an exception type identifier and an influence range parameter; the decision optimization module generates an optimization instruction set containing maintenance priority ranking based on the parameters; and the strategy iteration module monitors an instruction execution result, calculates a deviation value between an actual effect and a virtual model prediction effect, and dynamically updates the abnormal mode library. According to the system, accurate sensing of the operation state of the numerical control machine tool, quick response to abnormity and dynamic optimization of a maintenance strategy are realized, and the intelligent level of monitoring is improved.

The invention belongs to the technical field of automobile electronic safety, and particularly relates to an automobile keyless entry and start integrated system based on Bluetooth recognition, which comprises an awakening module, a connection management module, an entry authorization module, a start authorization module and a safety audit module, and is used for awakening an application scene switched to an active authentication mode based on a sleep mode. The problem of continuous high-power-consumption operation of Bluetooth is avoided, meanwhile, quick response is ensured when a user approaches a vehicle, and progressive protection for a Bluetooth recognition scene is formed through multiple security links of physical proximity verification and bidirectional encryption authentication. A vehicle entering authorization instruction is sequentially generated and sent to a vehicle body domain controller, and a vehicle starting authorization instruction is sequentially generated and sent to a vehicle power supply management unit, so that the convenience of Bluetooth keyless entry is reserved, the balance between non-inductive operation and absolute safety is realized, and closed-loop optimization is executed in combination with complete authentication failure auditing, so that the system has a self-iteration capability.

The invention relates to the technical field of electrical safety, and discloses a multistage cooperative fault detection method and system based on an intelligent air switch, and the method comprises the steps: intelligent air switch terminals disposed at all levels of a power distribution network are used for synchronously collecting a multi-physical-quantity time sequence signal; the intelligent air switch terminal calculates the dynamic correlation entropy of the collected current signal and executes local fault diagnosis according to the dynamic correlation entropy; the terminals carry out cooperative communication, and dynamic correlation entropy is collected to construct an entropy migration spectrum so as to carry out cooperative traceability on faults; and finally, integrating local diagnosis and collaborative traceability results, and executing corresponding hierarchical collaborative protection actions. The system comprises an acquisition module; an entropy calculation module; a local diagnosis module; a communication module; a collaborative traceability module; and a protection execution module. According to the invention, through multi-stage cooperative fault detection of the intelligent air switch, accurate identification, rapid response and state early warning of hidden faults are realized, and the safety and reliability of a power distribution system are improved.