467 results about "Security validation" patented technology

A value chain network control tower system comprises a processor and memory configured to execute a know your model system that manages the complete lifecycle of Al models in enterprise environments. The know your model system performs model intake and registration actions including model documentation collection, registration procedures, metadata collection, input / output interface standardization, legal and licensing validation checks, and security validation. The system conducts comprehensive model evaluation and risk assessment actions by analyzing foundational properties, task performance, safety and risk management, alignment and compliance characteristics, operational metrics, and tooling transparency capabilities. The know your model system executes model deployment actions through automated environment validation, predeployment approval processes, and controlled production deployment with continuous monitoring.

The invention discloses a streaming knowledge injection and adversarial self-optimization large language model training method and system, and the method comprises the steps: collecting the newest knowledge of an authoritative information source in real time, converting the newest knowledge into a structured constraint rule through a semantic analyzer, and dynamically updating a knowledge base; based on the updated knowledge base, adopting a PPO algorithm to optimize a generator, actively constructing a high-risk adversarial sample, and forcing the model to expose security vulnerabilities; after user input and adversarial samples are input into the model, total loss is calculated through constraint detection, gradient updating is blocked if the total loss exceeds a threshold value, and otherwise, a multi-level safety verification stage is started; and finally, fusing a security verification result and the adversarial loss, updating a total loss function and cooperatively adjusting model parameters to form a continuous self-optimization training cycle. According to the method, compliance is guaranteed through streaming knowledge injection, vulnerabilities are actively mined in combination with adversarial training, verification precision is improved by means of multi-level detection and dynamic threshold adjustment, and safety and reliability of a large language model in a complex scene are enhanced.

The invention discloses an automatic driving safety key simulation scene generation method based on adversarial generation and co-evolution. The method comprises the following steps: receiving a basic traffic scene described by a natural language, generating an antagonistic element scene containing security threats by using a large language model in combination with a traffic safety knowledge base, and analyzing the antagonistic element scene into an executable scene script; constructing a multi-agent confrontation collaboration diagram based on the meta-scene, and recognizing a key background vehicle through a cross-timing attention mechanism in combination with a time mask and time decay mechanism; and performing disturbance optimization on the key background vehicle trajectory to generate an automatic driving test scene. According to the method, a scientific and systematic solution with engineering operability is provided for safety verification of the automatic driving system when the automatic driving system faces real traffic challenges such as multi-source intervention and dynamic collaborative threat, and the method has wide adaptation capability and important industrial popularization value.

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant integrated management system, which comprises a data acquisition module for acquiring voltage fluctuation characteristics and power climbing rate parameters of energy equipment in real time; the equipment fingerprint database construction module generates an equipment fingerprint code comprising a dynamic response time-lag coefficient and an adjustment margin quantized value; the dynamic clustering module is used for dividing simulation sub-models of regional topology constraints based on the geographic position of the equipment and a power regulation threshold value; the multi-time scale prediction module is used for generating source load prediction data by fusing meteorological parameters through a space-time diagram convolutional network; the digital twin simulation module outputs an energy storage charging and discharging threshold value and a demand response priority strategy; and the cross-domain collaboration module adopts a double-chain block chain architecture to realize security verification and data integrity binding of federated learning feature parameters. According to the invention, through equipment characteristic digital modeling, regional co-simulation and multi-energy flow coupling regulation and control, the resource aggregation precision and response real-time performance in a new energy strong fluctuation scene are improved.

The invention relates to the technical field of data disaster recovery, and provides a disaster recovery drill and disaster switching method and system, and the method comprises the steps: automatically recognizing multi-source heterogeneous database assets through a rule engine and a machine learning algorithm, and constructing a disaster recovery topological graph; a disaster recovery strategy is generated and optimized by adopting an intelligent algorithm based on the disaster recovery topological graph, and the disaster recovery strategy is dynamically adjusted in combination with real-time service requirements; fusing the historical disaster recovery data and the real-time monitoring data to reconstruct a disaster recovery strategy; executing data are collected in real time in the drilling process, and a disaster recovery strategy is optimized through a closed-loop feedback mechanism; monitoring a whole process state through a visual interface and displaying key indexes; based on the multi-dimensional evaluation model, quantitatively analyzing the drilling result, and determining a target disaster recovery strategy; and a security verification mechanism is adopted to authorize the key operation, a disaster recovery scene scheme is matched according to the fault type, and an automatic execution unit is controlled to complete disaster switching based on the target disaster recovery strategy. According to the invention, the disaster recovery switching accuracy in a complex scene is improved.

A security verification method, apparatus, medium, device and product for a large model service relate to the technical field of computers, receive an encryption service request, acquire a private key of a client from a key management service running in a trusted execution environment through an encryption and decryption service running in the trusted execution environment, decrypt the encryption service request based on the private key, and verify the security of the encryption service request. The method comprises the following steps: decrypting a service request of a user, sending the decrypted service request to a large model service to obtain a reasoning result of the large model service, encrypting the reasoning result through a public key of a client, and returning the encrypted reasoning result to the client, so that the service request of the user can be visible in a plaintext in a trusted execution environment; the security of the user data is greatly ensured, and the problem of user data leakage can be avoided through the public and private key pair of the user dimension. In addition, the key management service and the encryption and decryption service both run in the trusted execution environment, so that attacks from an IaaS layer can be shielded.

The invention discloses a manufacturing task autonomous negotiation and execution method based on a large language model agent, and the method comprises the steps: constructing a production scheduling agent, an equipment management agent, a material distribution agent and a quality control agent, analyzing a natural language task instruction through the production scheduling agent, and decomposing the natural language task instruction into subtasks; each agent calculates a utility value based on the load rate, the resource matching degree, the estimated completion time and the historical success rate, and performs structured negotiation to achieve a task allocation consensus; a prediction-check-rollback architecture is adopted to generate an action instruction sequence, the sequence is compiled into a time Petri network transition sequence, and reachability verification is carried out based on hard security constraints; production environment data is collected in real time to trigger anomaly detection and re-negotiation, and a formalized security verification and causal anti-factual reasoning parameter updating mechanism is introduced. According to the method, unstructured instruction understanding, autonomous task planning, multi-agent collaborative decision and closed-loop optimization are realized, and the problems of real-time performance, safety and interpretability of a large language model in manufacturing control are solved.

The invention belongs to the technical field of power network inspection, and particularly relates to a power network security inspection system which comprises a multi-source sensing module, an edge calculation module, a zero-trust security verification module, a core analysis module, an execution module and a feedback optimization module. Through an edge-cloud collaborative architecture, sensors, network flow and video data are fused in real time, the detection efficiency is improved, a physical equipment state and zero-trust dynamic authority control are introduced, 'physical-digital 'dual verification is realized, real threats and false alarms are marked, algorithm parameter adjustment is guided, and a feedback optimization module continuously reduces the false alarm rate through a closed loop mechanism; full-dimension safety protection of the power network is realized through modular design, dynamic branch processing and a closed-loop optimization mechanism; the threat level and the risk assessment result are dynamically adjusted through the environmental risk correction factor, the risk misjudgment rate is reduced, and the fault prediction accuracy in extreme weather is improved.

The invention relates to the technical field of medical RFQ accelerators, and particularly discloses a medical RFQ accelerator multi-safety redundancy linkage protection system and method, and the method comprises the steps: collecting the working state image data of an accelerator, and generating a three-dimensional fusion feature map containing heat distribution, electromagnetic radiation intensity and mechanical vibration; a multi-dimensional safety index evaluation vector is output by using a multi-mode Transform model; constructing a dynamic safety boundary prediction model, and generating a real-time safety threshold curve of each subsystem according to the evaluation vector; performing parameter optimization by adopting a federated learning framework; constructing a non-tampering security event traceability chain by using a block chain; constructing a multi-stage protection strategy of the medical RFQ accelerator; and verifying the redundancy control channel through a cross-layer security verification mechanism, and finally executing a protection action. According to the invention, multiple safety redundancy protection of the medical RFQ accelerator is realized, the risk of false shutdown is reduced, and the requirement of high reliability of clinical radiotherapy is met.

The invention discloses an off-line virtual laboratory and digital intelligent application iteration method and system based on an intelligent agent. The method comprises the following steps: constructing an off-line virtual laboratory consistent with a site in a non-production environment, and keeping the consistency of data and configuration through a synchronization mechanism; performing automatic scheduling on working condition arrangement, simulation deduction and rule checking by utilizing an intelligent agent; carrying out process deduction by adopting a mode of combining data driving and similar working condition retrieval, and completing index calculation and safety verification; and full-life-cycle management is carried out on the digital intelligent application, and cross-site rapid copying is realized through template packaging. According to the system, low-risk and high-efficiency iterative verification and popularization of digital intelligent application can be realized on the premise of not interfering field production, and the safety and engineering level of industrial intelligence landing are remarkably improved.

The invention relates to the technical field of cross-platform collaboration of an ERP (Enterprise Resource Planning) system, in particular to an ERP operation method for realizing cross-platform collaboration, which comprises a data conversion module, a protocol adaptation module, a task scheduling module and a resource allocation module. The data conversion module unifies heterogeneous data formats, the protocol adaptation module analyzes communication protocols, the task scheduling module dynamically adjusts task priorities, and the resource allocation module coordinates calculation and storage resources. According to the method, through improvements of a weight distribution algorithm, dynamic mapping updating, security verification, load balancing and the like, data integration and task collaboration among multiple platforms are optimized. The flexibility and adaptability of the system can be improved, the technical threshold is reduced, and the efficient and intelligent operation requirement of enterprises is met.

The invention discloses a personnel real-name authentication method based on multi-modal fusion, and relates to the technical field of personnel identity authentication, and the method comprises the steps: forming a multi-environment scene through dividing illumination and noise intervals, synchronously collecting face, fingerprint and voice data, and binding environment labels; respectively extracting core features of each modal to form a structured feature packet; clustering by adopting a K-means algorithm of quantum particle swarm optimization to generate a multi-modal normal feature baseline cluster library with environment labels and weights; and calculating the attribution degree of the feature and the baseline cluster during real-time authentication, judging whether the feature is normal or abnormal, and triggering safety verification when the feature is abnormal. According to the invention, the authentication environment adaptability, accuracy and security are improved, and the authentication security and the user experience are balanced.

The invention discloses a cloud migration security verification method, and relates to the technical field of information, and the method comprises the steps: inputting a dynamic verification result into a pre-trained anomaly detection model, carrying out the automatic analysis of migration data and the dynamic verification result, generating a re-verification request when an anomaly is detected, and triggering a source end to output the corresponding batch of metadata again, otherwise, generating an increment verification proof; the increment verification proof is analyzed and verified in the audit account book, a consistency report is generated and intact, missing and conflicting segments are marked, rollback and supplementary transmission are triggered and verification is carried out again when conflicts are found, and a final chain root is generated when no conflicts exist; writing the final chain root into an audit account book, generating a final verification certificate, carrying out credible erasure on the migration data, generating an erasure certificate, and combining the final verification certificate with the erasure certificate to generate a security verification result. According to the method, each batch of data is ensured to be subjected to complete verification before archiving, so that the security, data consistency and credibility of cloud migration are improved.

The invention relates to the technical field of new energy electric power construction and operation and maintenance, in particular to a new energy equipment virtual maintenance system and method supporting fault drilling. The system comprises five core modules: an asset and twinning modeling module constructs an electrical-mechanical-thermal multi-physics field coupling digital twinning model of a wind generating set, a photovoltaic inverter and an energy storage battery cluster, and drives a fault drilling engine; the fault drilling engine triggers a fault scene based on preset event-condition-action logic and a job ticket operation state, and activates a fault evolution chain through violation operation; the job ticket and process arrangement module simulates the whole maintenance process and checks security constraints in real time; the safety interlocking and protection simulation module verifies the operation in real time according to the relay protection constant value, triggers a protection action during violation and feeds back the protection action to the fault engine; and the virtual-real fusion interaction module synchronizes a virtual instruction to an engine and an external device through a communication protocol, so that the consistency of virtual-real operation and protection action is ensured, and co-simulation and security verification of the whole system are realized.

Various aspects of the present disclosure relate to a network repository function (NRF) that receives a first signaling as a network function (NF) request from a NF, the NF request including a NF type, a NF identifier (ID), and NF security state information. The NRF verifies a NF security state based on the NF ID and the NF security state information. The NRF transmits a second signaling as a NF response, where the NF response includes a security verification of the NF request.

The invention discloses an automatic test system of a power driving chip, and particularly relates to the technical field of automatic test, which comprises a dynamic test control module, a multi-level parallel test module, an abnormal power detection module and a safety verification diagnosis module. Based on cooperative work of a programmable electronic load and a digital signal processor, the dynamic response capability of a chip under different working conditions is verified, abnormal power data under an extreme load condition is generated, electrical parameters and thermal parameters of the chip are synchronously collected, the relevance between abnormal power performance and temperature is analyzed, and the abnormal power performance is analyzed. Temperature rise differences caused by power-off protection and electric leakage are distinguished, abnormal power waveforms are extracted and classified so as to distinguish power-off protection and electric leakage events, the influence degree of environmental charge interference is quantified, finally, response effectiveness of a chip protection circuit in a strong charge environment caused by electric leakage is simulated, and calibration is performed according to a collected correction value. Therefore, the automation degree of the system is improved, and the test robustness is enhanced.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a parameter security re-calibration and response method, device, equipment and medium, and the method comprises the steps: obtaining parameters of a basic model and a fine-tuned model, and comparing the parameters to obtain differential parameters; performing gradient sensitivity analysis on the differential parameters to generate an abnormal correlation map; detecting the fine-tuned model by using an abnormal association map to determine unsafe parameters; unsafe parameters are processed, safe re-calibration is completed, and basic model parameters are fused to form a re-calibration model; verifying the re-calibration model based on the scenarized security verification set and obtaining feedback; according to the feedback updating strategy, re-calibration is carried out again, and a security application model is generated; and processing the task request through the security application model to obtain a security response result. According to the method, unsafe parameters are identified and corrected through differential analysis and closed-loop recalibration, the safety and the compliance are enhanced while the model performance is kept, and reliable application in a complex scene is ensured.

The invention relates to the technical field of regulation and storage pool operation management, in particular to an interactive control method and system for urban regulation and storage pool operation management. The method comprises the following steps: acquiring geometric structure parameters of the storage tank; setting dynamic boundary parameters based on the geometric structure parameters; wherein the dynamic boundary parameters comprise a safe water storage upper limit, a suggested emptying water level and a forced emptying water level; constructing a three-dimensional interactive visual interface containing a space-time constraint relation according to the dynamic boundary parameters; and in response to an interactive working condition switching instruction input in the three-dimensional interactive visual interface, performing virtual execution verification, and after the virtual execution verification is passed, converting the equipment linkage rehearsal sequence into a control instruction and issuing the control instruction so as to realize operation management of the urban regulation and storage pool. By constructing the three-dimensional water level dynamic boundary mapping and equipment linkage virtual rehearsal mechanism, visual perception of working condition switching and water hammer resistance safety verification are achieved, and the problems that the operation opportunity is difficult to judge and the hidden danger of misoperation is caused are solved.

The invention relates to the technical field of artificial intelligence, in particular to an automatic security code generation system based on artificial intelligence, which comprises a demand understanding and security analysis unit, a code generation and optimization unit, a security verification and vulnerability detection unit and a feedback learning and knowledge updating unit. Through scene-based security level dynamic adaptation, reinforcement learning-driven security-performance balance and modular security component integration, it can be ensured that the generated code meets the service scene security requirement, redundant logic can be eliminated, the execution efficiency is considered, the problem that security and performance are difficult to cooperate in traditional code generation is solved, and the code generation efficiency is improved. Sustainable evolution and prospective defense of system security capability are realized, code generation is upgraded from passive compliance to active defense iteration, and the security, efficiency and adaptability of code generation are remarkably improved.

The invention discloses an Internet of Vehicles heterogeneous signcryption communication system and method based on a double-layer block chain, relates to the technical field of Internet of Vehicles, and solves the technical problems that the Internet of Vehicles technology is insufficient in expansibility and poor in compatibility, safety and efficiency are difficult to consider, and use requirements are difficult to meet. The system comprises a private key generation center PKG, a secret key generation center KGC, an on-board unit OBU, a roadside unit RSU and a double-layer block chain device. The private key generation center PKG is used for generating and distributing a public key and private key pair of the identity signcryption IBC; the key generation center KGC is used for key updating and management of the certificateless signcryption CLC; the OBU is used for performing communication between the vehicle and other vehicles or the road side unit; the roadside unit RSU is used for communication support and data forwarding; and the double-layer block chain equipment is used for carrying out data storage and security verification in the communication system. According to the invention, the calculation overhead and communication cost of the system are obviously reduced, and safe, efficient and extensible authentication and data transmission in a high-dynamic and large-scale V2X environment is realized.

The invention discloses an intelligent backpack multifunctional control system and method based on AI voice, and relates to the technical field of intelligent wearable equipment and AI interaction. The method comprises the steps of collecting a user voice instruction and multi-dimensional state data of the intelligent backpack, performing AI semantic analysis on the voice instruction to obtain an instruction intention, and performing fusion processing on the multi-dimensional state data to obtain a comprehensive state of the backpack; generating a target control strategy based on the instruction intention, the comprehensive state of the backpack and the user portrait; and driving an execution unit of the intelligent backpack to complete corresponding function actions according to the target control strategy, and feeding back an execution state to a user in real time. The system comprises a voice interaction unit, a sensor unit, a control unit, an execution unit and other core modules. According to the invention, the voice recognition accuracy and the control precision are remarkably improved, dual safety verification and a dynamic energy consumption optimization mechanism are combined, the use safety and the cruising ability are enhanced, and the personalized and intelligent travel requirements of the user are comprehensively met.

The invention relates to the technical field of power system operation and control, in particular to a safety operation and maintenance scheduling device and method for a wind, light and storage integrated system, and the device comprises a field sensing unit which is used for collecting the real-time operation data of wind, light and storage equipment; the edge calculation and autonomous control unit is used for local data processing, short-time prediction and emergency control when communication is abnormal or a safety index is out of limit; the central optimization and safety management platform is used for fusing and standardizing the data, evaluating the safety state of the system based on a double-domain safety redundancy index, evaluating energy storage life consumption through a coupling model, generating a scheduling instruction considering economical efficiency, safety and life by adopting opportunity constraint optimization, and sending the scheduling instruction to the central optimization and safety management platform; security verification and stable execution of the instruction are guaranteed through shadow simulation, strategy progressive switching and a security instruction shielding module; and the man-machine interaction application unit is used for visual monitoring, strategy recommendation and history redisk.

The invention discloses a double-patch updating method, device, equipment, medium and product of a starting read-only memory, and relates to the technical field of chip updating, the starting read-only memory is located in a server management chip, and the server management chip is provided with a one-time programmable memory isolated from the starting read-only memory. The method for burning the preset shadow patch code by the one-time programmable memory comprises the following steps of: starting the read-only memory, and detecting whether the read-only memory is successfully started within a preset time period; if the starting is not successful, triggering a shadow patch process, and executing a preset shadow patch code in the one-time programmable memory; if starting succeeds, triggering an online patch updating process based on the patch updating request, and loading a target segment patch code; and loading the target segment patch code subjected to segment security verification to a random access memory of the server management chip so as to redirect the access request to a new patch code address in the random access memory. And patch updating in the read-only memory starting process is realized.

The invention discloses an automatic equipment fault diagnosis and intelligent repair system oriented to complex working conditions, and belongs to the technical field of intelligent operation and maintenance of automatic equipment. The edge computing node is internally provided with a spatial-temporal feature separation convolutional network ST-FSCN and is used for processing multi-modal sensing data in real time; the cloud decision center runs a dynamic reinforcement repair strategy library and comprises a deep Q network based on a physical model, a meta-learning strategy optimizer MAML-RL and a security verification engine; the execution feedback loop is connected with the equipment PLC through an industrial bus, adjusts the operation parameters of the equipment in real time, and feeds back the execution effect to the self-evolution digital twin system; and the self-evolution digital twin system comprises a high-fidelity simulation model and a lightweight real-time model, and adopts a dual-channel updating mechanism. The method has the advantages that the composite fault recognition accuracy is improved, the average repair decision time is shortened, and accidental shutdown of equipment is reduced.

The invention provides a multi-language-oriented source code automatic verification method and system, and relates to the technical field of computers.The method comprises the steps that multi-language source codes are obtained; compiling the multi-language source code through an LLVM compiler to generate an intermediate file; the intermediate file is optimized; analyzing the optimized intermediate file through an IR analyzer to generate a symbolic execution model; performing simulation execution operation on the symbolic execution model; and performing security verification on the symbolic execution model after simulation execution operation to obtain a verification report. Unified verification of multi-language source codes can be achieved, the verification environment building and development cost is greatly reduced, and the overall verification efficiency is improved; and the analysis capability of the intermediate file can be enhanced, the logic constraint relationship in the code can be accurately captured, the comprehensiveness and reliability of the verification work can be practically guaranteed, and diversified software security verification requirements can be met.

The invention discloses a digital payment identity security verification method, system and device based on a cloud platform and a medium, and belongs to the technical field of information security, and the method comprises the steps: obtaining digital payment request data, and determining user identity information and payment environment information in the digital payment request data; respectively generating a user identity feature vector and a payment environment feature vector according to the user identity information and the payment environment information; inputting the user identity feature vector and the payment environment feature vector into a verification model of a cloud platform to obtain an identity verification score and an environment risk score; and determining an identity verification result of the digital payment request according to a combination relationship between the identity verification score and the environmental risk score. According to the method, the user identity features and the payment environment features are separated and then subjected to fusion judgment, multi-dimensional identity verification is achieved, a relative judgment mechanism based on historical cases is adopted to replace fixed threshold judgment, and the adaptability and accuracy of a verification system to different payment scenes are improved.

The invention relates to the technical field of power system safety monitoring, and discloses a power monitoring safety protection control system and method, and the system comprises a main station safety controller, a terminal safety verification unit, and a safety verification channel. The master station safety controller responds to a control instruction of the monitoring master station, carries out prospective simulation on the instruction, and generates a verification data packet containing a physical state expected vector; the terminal security verification unit receives the original control instruction and the verification data packet, and performs collaborative arbitration; the collaborative arbitration process comprises the following steps: the terminal security verification unit performs localized consequence simulation based on a built-in local physical micro-model to generate a local prediction state vector; comparing the vector with a physical state expected vector issued by the master station; and according to the comparison result, the terminal security verification unit generates and executes permission or blocking decisions for the control instruction. According to the invention, through cooperative verification of the physical layers of the master station and the terminal, malicious control instructions having physical harm to the power system are effectively protected.

The invention discloses an arbitration-based automatic driving decision-making method, system and device and a storage medium. The method comprises the steps of heterogeneous parallel sensing and planning calculation, and synchronous starting of two independent calculation assembly lines; performing unified representation and space-time alignment, and performing standardization processing on output of the two calculation assembly lines; certified security arbitration and decision making are carried out, and security verification and utility evaluation are carried out on each candidate track output by the main brain; performing control execution and global health monitoring, and sending the selected track to a vehicle controller for execution; and performing off-line closed-loop self-evolution, retraining and optimizing the model, and updating and deploying the model to a vehicle end system through OTA. According to the method, a heterogeneous double-brain system integrating an end-to-end AI model and a traditional rule model is constructed, and a certifiable safety arbitration mechanism with strict formalized verification capability is introduced, so that the absolute safety, dynamic reliability and continuous self-evolution of an automatic driving decision are ensured while the AI performance advantages are fully played.

The embodiment of the invention provides an energy storage data processing method and system based on all-modal standardization and edge intelligence, and the method comprises the steps: collecting multi-source heterogeneous data of an energy storage system, carrying out the standardization processing, generating a unified data stream, and carrying out the encryption and anti-interference processing; carrying out decryption and security verification on the encrypted data; performing preprocessing and feature extraction on the verified data; performing time alignment processing on the preprocessed feature data, and performing hybrid fusion to obtain cross-modal fusion features; inputting the cross-modal fusion features into a lightweight artificial intelligence model for real-time reasoning, and outputting a fault risk probability; performing risk grading based on the fault risk probability, and generating a corresponding control instruction; and executing the control instruction, and performing feedback verification on an execution effect. According to the technical scheme provided by the invention, the availability, the safety and the intelligent level of the energy storage data can be remarkably improved, and a key technical support is provided for efficient operation of a novel power system.

The embodiment of the invention relates to the technical field of visual feedback, and provides a self-adaptive visual feedback system and method for ensuring material forming quality, the system comprises a front-end framework and a rear-end framework which work cooperatively, the front-end framework comprises a sensing module, a feature extraction module, an error compensation module and an execution control module, the back-end architecture comprises a decision module, a twin simulation module and an evolution module; the system further comprises a quality analysis module. According to the embodiment of the invention, environmental interference is overcome through active vision and event camera collaborative perception, long-term precision is maintained through online calibration compensation, quality quantitative evaluation is realized by combining three-dimensional shape real-time reconstruction and a deep learning method, a digital twin technology is introduced to carry out strategy security verification, and continuous evolution of the system is realized by means of a federated learning architecture. The method is not only suitable for the welding process, but also can effectively meet the high-quality control requirements of various material forming processes such as spraying, cladding and surfacing.