6540 results about "Functional module" patented technology

The invention belongs to the field of network security, and discloses a network security evaluation system and method based on a dynamic attack and defense game model, and the evaluation system comprises four functional modules of attack and defense game construction, risk prediction, dynamic defense optimization and security situation awareness. According to the method, the idea of dynamic attack and defense game is introduced, multi-level and multi-scene attack and defense simulation is carried out according to historical states, strategy data and real-time data, and the optimal attack path and attack strategy are calculated and analyzed through a game equilibrium solution; according to the method, network resources and network security resources are dynamically adjusted by relying on reinforcement learning and deep learning technologies and a defense rule base generated by pre-training, network configuration is optimized according to a real-time evaluation result, and waste of system resources is reduced. By implementing the system and the method provided by the invention, the security of the target network can be dynamically evaluated, the security defense strategy of the network can be adjusted in real time, and unknown attacks such as advanced persistent threat (APT) and zero-day attack can be effectively resisted.

The invention relates to the technical field of smart home, and discloses an automatic coordinated regulation and control system and method for smart home equipment, and the system integrates a plurality of functional modules such as equipment access, environment perception and user behavior analysis. The equipment access module supports access of multiple protocols and endows the equipment with a unique identifier; the environment sensing module collects environment parameters in real time and compares the environment parameters with thresholds. The user behavior analysis module generates a user scene preference model by means of an algorithm, the cooperation strategy generation module synthesizes multiple factors to formulate a regulation and control strategy, and the dynamic priority management module allocates weights according to equipment and scene conditions. And the equipment control module executes the instruction and feeds back the state, and the abnormity detection module monitors the equipment state and triggers a strategy regeneration mechanism when abnormity occurs. The data storage module securely stores various data, and the user interaction module efficiently analyzes instructions. According to the invention, many problems of an existing smart home system are solved, automatic coordinated regulation and control of equipment can be realized, and user interaction experience is optimized.

The invention relates to the technical field of server upgrading, and discloses a server firmware remote upgrading method and system, and the method comprises the steps: a management node obtains a device identifier list and target firmware version information of a to-be-upgraded server, and uses a distributed task scheduling algorithm to divide tasks; transmitting an upgrading instruction and a data packet through an encryption channel, and generating an incremental packet by using a differential upgrading technology; data transmission is guaranteed through heartbeat detection and breakpoint continuous transmission; after pre-verification of the server-side security sandbox, upgrading and isolating tasks in an idle period; verifying by adopting a gray release strategy, and automatically rolling back if the verification fails; and storing an upgrade state and a log by means of a block chain network. The system comprises corresponding function modules. According to the method, the upgrading efficiency, safety and reliability are improved, the influence on services is reduced, data cannot be tampered and can be traced, the problems existing in a traditional upgrading mode are effectively solved, and the method is suitable for various server firmware remote upgrading scenes.

The invention relates to a health monitoring and fault prediction technology of intelligent equipment, in particular to an equipment fault prediction method based on health degree analysis of the intelligent equipment. According to the method, multiple sensors are embedded in the intelligent device, data of the operation state of the device are continuously collected, and primary processing and abnormal value elimination are carried out through an edge computing node. The processed data is transmitted to a cloud server, and machine learning models such as a deep neural network, a Bayesian classifier and a support vector machine are utilized to analyze the equipment health degree trend, and a potential fault mode is identified. And intelligently generating a fault prediction report according to an analysis result, and notifying an operation and maintenance team to take measures through an operation and maintenance management system. In addition, the system further comprises a historical fault database, a scoring system, an online review group and other functional modules, the accuracy and reliability of prediction are ensured, and the equipment maintenance efficiency and safety are improved.

The invention provides an IMU (inertial measurement unit)-assisted depth SLAM (simultaneous localization and mapping) method and system fusing language-vision multi-mode perception, and the system comprises functional modules such as initial calibration and semantic map initialization, pre-integration prediction and key frame judgment, dense point cloud reconstruction and relative pose estimation, semantic embedding extraction, semantic guidance loopback detection and semantic three-dimensional map incremental updating. IMU motion priori, depth geometric constraint and language model semantic factors are subjected to combined modeling through a graph optimization framework, and high-precision positioning and labeled map construction in a complex dynamic environment are achieved. Compared with the prior art which only depends on geometric or inertial information, the method has the advantages that the loop-back mismatching rate is reduced, the closed-loop convergence efficiency and the long-time relocation robustness are improved, and a semantic interface is provided for upper-layer tasks such as natural language navigation and target retrieval. The method can be widely applied to the fields of service robots, security inspection, intelligent driving, post-disaster search and rescue and the like.

A prefabricated building production line and a prefabricated building structure. The prefabricated building production line comprises an assembly conveyor line, a functional module conveyor line, a transfer mechanical arm, an assembly mechanical arm, and a control platform; an assembly area, a testing area, and an inspection area are arranged in order along a transport direction of the assembly conveyor line; the assembly conveyor line is used for conveying a building unit main body to be assembled; a plurality of functional module conveyor branch lines are arranged at the side of the functional module conveyor line away from the assembly conveyor line; the functional module conveyor line and the functional module conveyor branch lines are used for transporting a functional module; the transfer mechanical arm is used for carrying a functional module and the building unit main body to be assembled; and the assembly mechanical arm is used for installing the functional module on the building unit main body to be assembled. The prefabricated building structure comprises: a lifting device for prefabricated building installation, an prefabricated building ventilation structure, a prefabricated building automated production fixture, etc.

The invention relates to the technical field of food storage management, in particular to a food storage management system, which comprises a data acquisition layer, is composed of a plurality of devices and is used for acquiring food, environment and user operation data; the data transmission layer transmits data by using wired and wireless network technologies; the data storage layer stores various types of data in combination with a relational database and a non-relational database; the data processing layer comprises a plurality of functional modules, such as an intelligent warehousing management module, and is used for realizing automatic input verification and batch management of food information; the intelligent ex-warehouse management module is used for optimizing ex-warehouse sequence and tracking food directions; the expiration and waste management module is used for carrying out expiration reminding and surplus value evaluation; the data analysis and purchase optimization module is used for predicting demands and analyzing loss; and the dish and inventory association module is used for matching the dish formula and the inventory and predicting the sales allocation inventory. The system improves the management efficiency, reduces the waste, optimizes the purchase decision, enhances the dish and inventory association management, guarantees the system safety, and provides data support to assist the decision.

The invention provides a compliance auditing method and system for multi-modal content. The method comprises the following steps: performing feature extraction on unstructured to-be-audited multi-modal content to obtain a structured feature vector; performing image-text semantic association on the text semantic feature vector and the image visual feature vector to obtain a fusion feature vector involving image-text semantic contradiction; constructing a domain knowledge graph based on the compliance guidance data of the domain to which the to-be-audited multi-modal content belongs; inputting the fusion feature vector into a domain knowledge graph, and performing compliance rule retrieval by adopting a sub-graph matching algorithm to determine a violation type corresponding to the fusion feature vector and a violated compliance term; and generating an interactive compliance audit report. The system comprises functional modules for realizing the steps in a one-to-one correspondence manner. According to the technical scheme, the problem that cross-modal semantic analysis of an existing multi-modal content compliance auditing method is not accurate can be solved.

The invention discloses a PCB intelligent optimization layout wiring method and device, equipment and a storage medium, and relates to the technical field of electronic manufacturing, and the method comprises the steps: obtaining the initial layout of a printed circuit board; on the basis of a first optimization algorithm, automatically adjusting the layout position of the initial layout according to a functional module area and writing design requirements to obtain an adjusted layout; optimizing the heat dissipation structure of the adjusted layout through thermal analysis to obtain a reference layout; and wiring the reference layout based on a second optimization algorithm to complete the layout wiring of the printed circuit board. According to the invention, the layout and wiring efficiency and accuracy of the printed circuit board can be improved.

The invention discloses an unmanned aerial vehicle flight control system vulnerability detection method based on data flow analysis and LLM, and belongs to the technical field of intelligent software testing. Comprising the following steps: extracting a code function module associated with user operation in an unmanned aerial vehicle flight control system through a data flow analysis method, and establishing an operation-code mapping relation library; generating a structured natural language semantic description for each function module code by adopting a large language model LLM, and forming a multi-dimensional semantic feature vector; based on correlation analysis of multi-module semantic features, a combined test scene is constructed, and a natural language test case is generated; the natural language test case is converted into an executable test code through reverse semantic mapping, and coding reconstruction of test logic is completed; and executing a test code and capturing a runtime log in an unmanned aerial vehicle simulation environment, and performing vulnerability feature extraction and root cause positioning by using a large language model. According to the method, the efficiency is improved, and meanwhile, the deep coverage test of a complex interaction scene is supported.

The invention relates to the technical field of chip design, in particular to a system-on-chip design method and system and a medium. The method comprises the following steps that a chip design target and basic parameters are collected, a constraint condition set is generated through demand constraint analysis, the basic parameters of a system are subjected to parallel recombination, functional prototype data are generated, the functional prototype data are used for chip behavior design, functional unit groups are generated, a network architecture is reconstructed to form a functional module network, and the functional module network is used for chip behavior design. And carrying out clock domain mapping and competition compensation processing based on the network to obtain a function scheduling sequence, carrying out resource matrix remapping on the basic parameters according to the function scheduling sequence, generating hardware abstract data, carrying out chip layout simulation, and finally generating a simulation chip blueprint. According to the system-on-chip design method, the more efficient system-on-chip design method is achieved.

The invention relates to the technical field of computer processing, in particular to a software architecture code generation method and system based on a large language model, and provides the following scheme: obtaining demand information input by a user, analyzing demands by using a natural language processing technology, and extracting functional elements; performing classification processing on the functional elements through a clustering algorithm to generate a functional module group; mapping the function module group to a system architecture layer, and generating an architecture diagram represented by UML (Unified Modeling Language); and generating a corresponding source code according to the UML graph, and optimizing the source code. The optimization strategy comprises static analysis of format problems and potential vulnerabilities in identification codes and performance optimization of complex functions. Through combination of the large language model and the automatic code generation and optimization technology, the software development efficiency is improved, manual intervention is reduced, and the correctness and maintainability of the generated code are ensured.

The invention relates to the technical field of agricultural information management, and particularly discloses an agricultural information management system and method based on a big data platform, and the method comprises the steps: firstly deploying a multi-source data collection module at an edge calculation node, and obtaining and standardizing the soil moisture content, meteorological environment and equipment operation data in real time; secondly, constructing a local dynamic irrigation strategy model, and realizing multi-objective optimization through a reinforcement learning algorithm; establishing a federated learning framework at the cloud, dynamically distributing node weights by adopting an attention mechanism, and realizing model aggregation of privacy protection in combination with secure multi-party computing; an optimal irrigation instruction is generated through a multi-source data fusion engine, and a three-level response exception handling mechanism is established; and finally, a closed-loop feedback system containing short-term incremental learning and long-term architecture optimization is formed. The corresponding management system comprises six functional modules, namely a data acquisition module, a local modeling module, a federated learning module, a real-time decision-making module, an abnormal monitoring module and a closed-loop optimization module.

The invention provides a vehicle-mounted display terminal man-machine interface interaction experience optimization method and system, and relates to the technical field of vehicle-mounted man-machine interaction, and the method comprises the steps: obtaining eyeball watching, hand operation and head rotation data of a driver, and carrying out the fusion to generate an attention distribution thermodynamic diagram; constructing a dynamic scene recognition model in combination with the vehicle running state and the environment information; and calculating a function module optimization index based on the thermodynamic diagram and scene prediction, and dynamically adjusting the position, the response area and the display level of the function module. According to the invention, the interaction efficiency of drivers can be improved, the operation distraction risk is reduced, and the driving safety is enhanced.

The invention discloses an end-side cloud collaborative intelligent fire-fighting fire monitoring system based on image fusion, and relates to the technical field of intelligent fire-fighting, the system is composed of a plurality of functional modules, and the system comprises a multi-modal image fusion module which generates a dynamic scanning priority map based on prior data, distinguishes a natural heat source from an abnormal fire by using a dual-light fusion algorithm, and sends an image fusion result to a cloud server; a scanning area is divided according to the thermal risk grade, and the thermal imaging resolution is dynamically adjusted; the distributed edge computing module is used for carrying out space-time synchronization on cross-modal data through a multi-modal feature alignment network, and carrying out dynamic allocation on a CUDA core and CPU resources through adaptive computing scheduling; an improved artificial bee colony algorithm is adopted, the bandwidth of the multi-sensor data flow is dynamically allocated through a time-sharing multiplexing protocol, and three-dimensional path planning is carried out; and the end-side cloud collaborative decision module constructs a federated learning driven model sharing network, and each edge node trains a lightweight YOLOv5s pruning model based on local data.

The invention discloses a data access permission control method and system based on a block chain, and relates to the technical field of block chains, the system is composed of a plurality of function modules, and the system comprises a hierarchical permission module which adopts a role hierarchical strategy and defines the access granularity of a role through an intelligent contract; the dynamic permission adjustment module is used for dynamically adjusting the permission range by analyzing user behaviors in real time through machine learning according to the principle of following the minimum permission in the permission distribution process; attribute encryption and zero-knowledge proof are introduced, an access key is dynamically generated according to user attributes, and sensitive information is hidden in the verification process; the multi-chain collaboration module is used for coding an intelligent contract through an authority rule, automatically executing access request verification, storing encrypted data through a private chain, managing an index and an access strategy through an alliance chain, isolating a storage domain from an access domain, and carrying out process traceability on a data request through a cross-chain verification mechanism; and the key management module is used for generating a true random number private key by adopting a hardware security unit.

The invention relates to the technical field of process management, and discloses a lightweight process management method, which comprises the following steps of: defining a business process and analyzing the business process through a process engine; a graphical process design interface is provided, and a user can dynamically configure business process nodes through dragging and clicking operations; the business process is disassembled into a plurality of task nodes, and each task node is packaged into an independent function module through modularization design; the task nodes are combined according to needs and executed in parallel on different processors, and efficient execution of the task nodes is ensured; the execution state of the business process is monitored in real time, and potential problems are found and processed in time through log recording and an exception processing mechanism. According to the technical scheme of supporting dynamic adjustment of the task nodes and optimizing parameters based on the historical abnormal mode, execution parameters of the task nodes are dynamically corrected, association logic between the nodes is adjusted in real time, and the effects that process management is flexible and adapts to complex service scenes, and operation stability is improved are achieved.

The invention relates to an intelligent agent system optimization method and device based on intelligent fault analysis and cross-generation knowledge inheritance, and belongs to the technical field of artificial intelligence. According to the method, interaction abnormal signals are captured in real time by deploying a lightweight log probe, and a tool benefit prediction model based on reinforcement learning is constructed to automatically generate an improvement proposal when the failure rate exceeds a threshold value; an agent genealogy map is established to realize automatic inheritance of a new agent on core memory and abandonment of failure knowledge, and a disastrous forgetting blocker is deployed to dynamically extract a functional module from a genealogy to deal with key capability degradation. Aiming at the problems of fault response lag, knowledge inheritance fracture, key capability degradation and the like in an intelligent agent system iteration process, the invention creatively provides a cooperation mechanism of an intelligent fault analysis layer and a cross-generation knowledge inheritance network, and the fault self-healing capability, version stability and service continuity guarantee level of the system are remarkably improved.

The invention discloses a multi-modal large model optimization method and system based on defect detection and analysis. The method comprises the following steps: S1, collecting a multi-modal original sample with power business scene characteristics; s2, performing standardization preprocessing on the original data to obtain a fine-tuned standard data set; and S3, establishing relational mapping among the labels of the training data set to form a semantic relation graph of the labels, and extracting a topological structure relation of the semantic relation graph to generate the power text knowledge base. S4, various task function modules of the computer vision large model are trained, so that the computer vision large model has various tasks for processing the electric power scene. And S5, fusing the trained Clip semantic large model and the trained computer vision large model into a multi-modal large model by using a Transform structure. According to the method, the multi-modal large model subjected to fine tuning through the power text knowledge base not only maintains the specific analysis and reasoning performance under various power scenes, but also can ensure the generalization ability of the large model to various algorithm tasks.

The invention discloses a test case determination method and device, a computer readable storage medium and a processor. The method comprises the following steps: identifying a source code of software by utilizing a pre-trained large model to obtain a plurality of functional modules in the software and a dependency relationship among the plurality of functional modules; in response to updating of the source code of the software, determining a code updating part in the source code; determining a test case matched with the code updating part from a test case library of the software based on the code updating part and a dependency relationship among the plurality of functional modules, the test case being used for testing the code updating part; based on a test result of the test case, adjusting a selection strategy of the test case; and re-determining the test case for testing the code updating part based on the adjusted selection strategy of the test case. The technical problem that a traditional regression test strategy is low in test efficiency is solved.

The invention relates to the technical field of data processing, and discloses a cross-border trade data intelligent processing method and system based on an intelligent port. The method comprises the steps of performing functional model hierarchical processing on cross-border trade business data, obtaining a functional module calling relation network, performing process template construction processing, obtaining a dynamic process assembly rule base, performing service burying point processing, obtaining a user behavior analysis data set, performing guide strategy construction processing, and obtaining a user behavior analysis result set; and obtaining a service guidance rule base, carrying out knowledge graph construction processing, obtaining a service knowledge inference rule set, and carrying out cross-border trade data processing to obtain a port service intelligent decision scheme. According to the method, intelligent processing of cross-border trade data is realized, a complete technical chain from data acquisition, behavior analysis to decision support is constructed, accurate business guidance and intelligent decision support are provided, and the problems of low business processing efficiency, inaccurate service guidance, weak decision support capability and the like in the prior art are solved.

The invention relates to the technical field of agent construction, and discloses an agent construction method and system based on agentive workflow, and the method comprises the following steps: defining an agent core function module and distributing a unique identifier, building an inter-module communication protocol standard, and determining a message format and a priority rule; the method comprises the following steps: distributing computing resources for each module, setting an elastic capacity expansion and contraction strategy, generating a module dependency graph and an architecture metadata file, detecting the loss and delay of multi-modal input data, generating compensation characteristics based on historical context, calculating the quality confidence of each modal, and carrying out dynamic weighted fusion. According to the invention, a three-level alignment architecture is provided to realize cross-modal semantic consistency, and the generated content is ensured to accord with real scene logic; a double-layer decision-making mechanism is designed to drive workflow dynamic optimization, and the system agility under a complex task is remarkably enhanced; a privacy-efficiency balanced federated architecture is constructed, and differential content generation is supported while enterprise data sovereignty is protected.

An apparatus for data ingestion and manipulation, the apparatus including at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive a resource data file from one or more data acquisition systems, classify the resource data file to one or more educational categorizations, generate an educational module as a function of the resource data file and the classification of the educational categorizations wherein the education module comprises one or more machine learning models, retrieve a user profile of a plurality of user profiles as a function of a user input, create user-specific outputs as a function of the educational module, the user profile, and a conversational input and generate a virtual avatar model as a function of the user-specific outputs.

The invention discloses an industrial internet security monitoring system and method, and relates to the field of industrial monitoring management, and the system comprises a management module which is used for connecting a production intranet, carrying out the identity verification, and carrying out the safety monitoring according to the current production period and the operation state of specific production equipment, acquiring the control authority of each image acquisition component and the operation acquisition component, and editing and issuing a control instruction for each functional module; the acquisition unit is used for dividing acquisition targets of operation areas of production equipment according to production targets, setting a moving path scheme of an image acquisition component and an acquisition frequency of the operation acquisition component, and extracting images and operation data acquired in each area according to a preset time period; through accurate data analysis, the accuracy of anomaly detection is greatly improved, more complex state data can be processed, anomaly can be identified, dynamic analysis can be carried out according to historical data, and the monitoring frequency of a target area and the moving path of image acquisition equipment can be dynamically adjusted.

The invention relates to the technical field of chip development, and discloses a chip post-simulation verification acceleration method, system and device and a storage medium, and the method comprises the steps: collecting multi-dimensional data of a large-scale chip; collecting all module names of a given chip; establishing a mapping relation between a redundant module RTL and a corresponding gate-level netlist, ensuring that interfaces before and after replacement are completely consistent, and designing an automatic script identification and processing parameterization module; establishing a post-simulation verification environment, verifying the consistency of output responses before and after replacement, analyzing the time delay change of a key path, calculating the dynamic power consumption and static power consumption of each functional module before and after replacement, and generating a simulation verification result. According to the method, the hybrid simulation model is constructed, and part of the gate-level netlist irrelevant to the current simulation function is replaced with the RTL, so that the actual simulation scale of a chip is reduced, the problem of overlong simulation time after a large-scale integrated circuit is remarkably solved, and the simulation efficiency is effectively improved.

The invention discloses a mobile communication load collaborative scheduling system and method based on energy efficiency perception, and belongs to the technical field of mobile communication, energy consumption data of each functional module in a base station and external network environment parameters are collected in real time through a multi-source sensor network in the base station, and a dynamic energy efficiency map is generated; based on historical energy efficiency data and real-time load characteristics, constructing an energy efficiency construction prediction model, and predicting an energy efficiency hotspot area; based on the historical movement track of the user and the spatial and temporal distribution characteristics of the service flow, the movement trend of the user and the change trend of the service flow are analyzed, and in combination with an energy efficiency prediction model, a potential overload base station is identified, and overload early warning is triggered; after receiving the early warning information, screening candidate target base stations according to a preset radius by taking the overload base station as a core; and generating a load idle list in a descending order according to the real-time energy efficiency ratio of the candidate base station, and performing energy efficiency sensitivity priority grading on the to-be-migrated service, thereby generating a collaborative scheduling strategy and executing load migration.

The invention belongs to the technical field of computers, and particularly relates to a zero-code agent construction platform. The system comprises a module database, a semantic analysis and intention decomposition unit, a task flow tree generation unit and an agent construction unit, the semantic analysis and intention decomposition unit is used for performing image language analysis on a natural language instruction input by a user, decomposing the natural language instruction into a plurality of intention units with a sequence, and constructing an intention vector; the task flow tree generation unit is used for generating a plurality of corresponding task flow trees according to the intention vector; and the agent construction unit is used for analyzing all the task flow trees, finding out an optimal task flow tree, calling all target function modules in the task flow tree and completing the construction of the agent. According to the method, the precision of semantic analysis, the flexibility of task logic generation and the automation degree of agent construction are remarkably improved.

The invention relates to a financial bill management system and an implementation method thereof. The financial bill management system comprises a data acquisition module, an AI identification module, an auditing module, a cost reimbursement module, a report generation module and a data storage and backup module. According to the financial bill management system and the implementation method thereof, by introducing the AI recognition module, automatic recognition and classification of bill types are achieved, the requirement for manual operation is reduced, the working efficiency and accuracy are improved, the AI recognition module can automatically process bill information, the whole process from image preprocessing to final key information extraction is highly automatic, and the efficiency is high. The system is not limited to bill identification and classification, but also covers a plurality of functional modules such as data acquisition, auditing, expense reimbursement processing, report generation, data storage and backup and the like, and provides a one-stop solution for users. The image preprocessing unit can effectively remove image noise, correct inclined images, cut redundant parts and enhance image contrast and brightness.

The invention relates to a prefabricated building quality defect tracing system and method applied to the field of building informatization, and the system comprises a user interaction layer, a data processing and storage layer, a data collection layer and a function layer, and the data collection layer is used for collecting key quality data of prefabricated building components in different links; the user interaction layers interact with each other through a network and the function module layer; the data processing and storage layer performs encryption, hash operation, scattered storage and index verification on the quality data transmitted by the data acquisition layer by using an intelligent contract, an IPFS network and a block chain; the function module layer provides support for each unit of the user interaction layer to trace quality defects, judge responsibility and take prevention measures, and the system can collect the quality data of the whole process of the prefabricated building, comprehensively manage the quality tracing process, strengthen multi-party collaboration and sharing, and improve the quality of the prefabricated building. And it is ensured that tracing of the quality defects of the fabricated building has comprehensiveness, transparency and non-tampering performance.

The invention relates to a metal cutting machining path planning system, and aims to optimize the efficiency and precision of a metal machining process through advanced technical means. The system comprises a plurality of functional modules: an image acquisition module captures a multi-angle high-resolution image of a to-be-processed metal workpiece; the three-dimensional reconstruction module reconstructs a three-dimensional model by using the images; the cutting simulation module simulates the three-dimensional model in a virtual environment and records stress and heat distribution conditions in the cutting process in real time; the intelligent segmentation module divides the machining process into a plurality of stages according to the stress and heat data; the comprehensive optimization module is combined with a genetic algorithm and a simulated annealing algorithm to optimize the cutting path, depth and feeding speed of each machining stage; the feedback adjusting module monitors stress and heat changes in actual machining, and when the stress and heat changes exceed threshold values, cutting parameters are automatically adjusted or machining is paused. According to the system, through comprehensive application of image processing, analog simulation and optimization algorithms, the cutting efficiency and the product quality are remarkably improved.