643 results about "System integration" patented technology

Disclosed in the present invention is a four-network integration architecture for an unmanned swarm system. The four-network integration architecture has the capabilities of heterogeneous platform resource pooling, intelligent dynamic computing power allocation, and timely decision planning, so as to maximize the overall benefit. The present invention focuses on abstracting and integrating independent submodules to form a mesh topology of a swarm. The present invention designs a four-network integration architecture for an unmanned swarm system, which comprises a computing power network, a perception network, a decision network and a communication network as core modules. The structure aims to achieve efficient cooperation of all parts in the swarm, thereby improving the overall performance and adaptability of the system. The system integrates environmental perception, a swarm network modeling component, a knowledge base and a resource pool, providing an intelligent environmental perception strategy and a network modeling strategy for the interior of the swarm. Therefore, the perception of environments, tasks and networks by nodes can be facilitated, thereby completing establishment of intelligent networks, so as to ensure the characteristics of the stability and flexibility of networks.

The invention discloses a data center machine room AI energy-saving control method and system, a digital twin model of a machine room operation state is constructed through a holographic perception and heterogeneous data fusion technology, centimeter-level monitoring of an equipment state and environmental parameters is realized, and the system integrates a laser radar array, an acoustic sensor and a gas sensor network. The time-space alignment of multi-modal data is completed by combining edge computing nodes, holographic mapping including thermodynamic characteristics, vibration characteristics and gas leakage risks is formed, historical temperature control strategy characteristics are extracted by adopting a variational auto-encoder based on a dynamic strategy generation mechanism of generative artificial intelligence, and a load trend is predicted by combining a long-short-term memory network. Constructing a self-adaptive strategy pool; the multi-agent reinforcement learning framework enables temperature control, equipment scheduling and power grid response to form game optimization, the strategy robustness in a complex scene is improved, and the system innovatively fuses power grid real-time electricity price and carbon transaction data so as to establish a multi-target decision system.

The invention relates to a humanoid robot double-arm body operation method based on reinforcement learning, belongs to the field of robot cooperative control, and is characterized in that a strategy of trajectory block prediction and time integration fusion is used for double-arm cooperative control, and continuity and stability of double-arm operation are improved; in reinforcement learning control, a three-dimensional pose track generation and correction module is introduced, condition generation and denoising correction of a three-dimensional space are carried out on a track block layer, and geometric consistency and naturalness of a generated track are guaranteed; the invention further provides a reinforcement learning optimization framework and a simulation-reality migration process, and through system integration of reward, value guidance and migration processes, strategy deployability and safety are guaranteed; compared with the prior art, the method has the advantages that the naturalness, the collaboration and the success rate of double-arm operation can be remarkably improved, the generalization ability is high, and the good simulation-to-reality migration ability is achieved.

The invention relates to the technical field of environmental governance and remediation, in particular to a combined contaminated site soil remediation data management method, which comprises the following steps of: firstly, integrating multi-source heterogeneous data through a unified space-time coordinate system to generate a fusion data pool, and then driving a pollution assessment model to generate a multi-dimensional space-time pollution map; and then intelligently matching a candidate repair process chain based on an associated knowledge base, performing iteration deduction through a dynamic multi-objective optimization engine to obtain an optimal repair scheme, converting the optimal repair scheme into a digital regulation and control instruction, and finally monitoring an execution state and environmental response data in real time and feeding back and updating a data pool to realize closed-loop optimization. According to the method, the data quality and the pollution evaluation accuracy are improved, the adaptability of the repair process and the cost performance of the scheme are optimized, the repair effect is ensured to be stable and reach the standard, and the efficiency and reliability of the repair project are improved.

The invention discloses a multi-base-station AOA cooperative low-altitude target rapid positioning system. The system comprises an AOA measurement base station network, an AOA data preprocessing module, an adaptive weighted intersection positioning module, an extended Kalman filtering state estimation module and a data fusion and system integration module. The system synchronously measures the arrival angle of a target signal through multiple base stations, removes noise in combination with smoothing filtering and an anomaly rejection algorithm, and then solves the initial position of a target by using a self-adaptive weighted intersection algorithm based on measurement quality and geometric distribution. And then, fusing the target motion model and the measurement model by adopting an extended Kalman filtering algorithm to realize dynamic estimation and prediction of the position, the speed and the course. The system can realize high-precision and real-time positioning and continuous tracking of targets such as low-altitude unmanned aerial vehicles, small aircrafts and the like in a complex electromagnetic environment and a sight distance limited scene, and has visual display and regional alarm functions.

The invention provides an intelligent medical information system based on multi-system integration, a data security management method and device, electronic equipment and a storage medium, and is applied to the technical field of medical data processing. According to the method, three types of information including core security elements, multi-system heterogeneous adaptation (including HIS / LIS / EMR interface specifications and the like) and full-process auditing (including operation logs and the like) are obtained. Normalizing the core security elements by using technologies such as AI multi-mode privacy identification and the like to generate a standardized security baseline file; heterogeneous adaptation information is processed through a lightweight API connector and the like, and a multi-system security access scheme is formed. Associating the baseline file with the access scheme, and generating a full-life-cycle protection strategy in a zero-trust architecture; and processing the audit information to generate a dynamic security audit report. And finally, based on a security-adaptation linkage decision engine and compliance verification, outputting a cross-system data security sharing scheme and a privacy disclosure emergency response instruction, and realizing a medical data security management closed loop.

The invention discloses a diamond cutter path error compensation method based on cutter wear evolution, which comprises the following steps: S1, multi-modal sensor data acquisition and processing: in the machining process, key state information between a diamond cutter and a workpiece is acquired in real time through a multi-modal sensor; s2, tool wear modeling: key geometric parameters and evolution trend of tool wear are extracted from the collected and processed data, and tool wear modeling is carried out. And S3, artificial intelligence prediction of the abrasion loss: training a tool abrasion prediction model based on a deep learning network structure. And S4, tool path compensation is conducted, specifically, the predicted abrasion loss and the actual free-form surface model are combined, the tool path compensation amount and the compensated control point coordinates are calculated, a new machining path is generated, and dynamic compensation of tool cutting edge abrasion is achieved. And S5, system integration and closed-loop control: integrating the steps on processing equipment, and realizing real-time data interaction and synchronization through a unified data protocol.

The invention relates to the technical field of image processing, in particular to a picking ingot coating defect online detection method and system based on machine vision, and the method comprises the steps: obtaining a multi-view image, carrying out the feature extraction of an image data stream after preprocessing, and carrying out the matching or clustering with a preset defect dictionary; the method comprises the following steps: preliminarily identifying a potential defect area, triggering refined multi-angle image acquisition, applying a multi-task CNN model fused with a polarization perception convolution kernel, jointly optimizing pixel-level segmentation loss and boundary prediction loss, outputting pixel-level semantic segmentation and an accurate boundary of the defect area, and calculating a reconstruction error to carry out defect classification. And finally, the system also has the functions of defect root cause analysis and process flow adjustment, so that defect tracing and production optimization are realized. According to the system, advanced visual technology, deep learning and multi-modal information fusion are integrated, and online, accurate and automatic detection of picking ingot coating defects is realized.

The invention discloses a bridge girder erection machine structure health intelligent management system and an intelligent management method thereof. The system comprises a state data acquisition unit, a data preprocessing module, a structure state modeling module, a state prediction and residual life estimation module, an early warning and control module, a self-learning and optimization module and a system integration module. The method comprises sensor deployment, data preprocessing, structural state modeling, state prediction and residual life estimation, early warning and control, self-learning and optimization and system integration. Structural state monitoring data are collected in real time through a sensor, a structural state model is constructed after preprocessing, a future state is predicted in combination with time sequence prediction and a mechanism model, and the remaining safe life and risk points are estimated. And the system triggers multi-stage early warning according to a prediction result, executes an active maintenance control strategy, and continuously improves a prediction model through a self-learning and optimization module. According to the invention, real-time health management, remote operation and full-life-cycle risk control of the bridge girder erection machine structure can be realized.

The invention particularly relates to an intelligent factory integrated operation management method based on multi-system integration, and relates to the technical field of intelligent factory operation management. Performing intelligent planning and robustness decision making; performing production execution and monitoring; and carrying out equipment full-life-cycle management. According to the method, the limitation of a traditional static plan is thoroughly broken through through a collaborative mechanism of multi-dimensional risk index linkage, dynamic time buffering and a plan chain; on one hand, the DTB performs real-time calculation based on a supply chain risk index, an equipment health index and a process stability index, approaches reference buffer to avoid resource waste when the risk is low, automatically expands to absorb disturbance when the risk is high, and ensures dynamic balance of plan efficiency and disturbance resistance; and on the other hand, the pre-generated plan chain is rapidly triggered when the disturbance exceeds the limit, global rearrangement is not needed, and continuous production can be guaranteed only through local adjustment.

The invention relates to the technical field of power equipment monitoring, in particular to a multi-mode sensor power equipment state monitoring system based on AI smell. The data acquisition module is used for acquiring multi-dimensional sensing data around the electrical equipment; the AI olfaction processing module is used for converting original sensor data into standardized odor feature fingerprints by performing spatial-temporal feature extraction and multi-modal feature fusion on the multi-dimensional perception data; the overheating state analysis module is used for generating an overheating comprehensive index and an equipment state evaluation result based on the smell characteristic fingerprints; the multi-stage early warning and response module is used for carrying out early warning grade judgment based on the overheating comprehensive index and the equipment state evaluation result and generating graded early warning information; and the storage module is used for storing historical monitoring data and early warning event records. According to the invention, through system integration of the multi-mode sensor array of the bionic olfaction mechanism, AI feature fusion processing and intelligent analysis decision, active sensing and accurate identification of the overheating state of the power equipment are realized.

The invention belongs to the technical field of industrial system integration, and provides a design method for rapid adaptive integration of a cross-domain system. Through the method, developers extract business logic rules of the industry, standardize characteristic mapping and select, develop and integrate functional modules, logic processes and scene components of the cross-domain system, so that the cross-domain system which conforms to industry characteristics and meets business circulation is formed, and meanwhile, the development efficiency of the cross-domain system is improved. The newly added industry logic component can be classified into each industry characteristic model based on the knowledge base, and a multi-dimensional characteristic vector is generated in the platform, so that cross-industry integrated component multiplexing is realized, and the cross-field applicability and flexibility of the platform are further improved. Modularized construction and configuration type development are supported, mapping and matching functions are carried out on various industrial manufacturing industry characteristics such as machining, electronic and electrical appliances and equipment sets, the requirements for subdivision production in the industrial field and rapid adaptation of business processes are met, the system integration cost is reduced, and the application development efficiency is greatly improved.

The invention relates to the technical field of data security, in particular to a data desensitization method and system based on combination of an MCP protocol and rule driving. The method comprises the following steps: loading a service rule through an MCP Server and registering a desensitization tool as a standardized tool; user query is received through the MCP Client, and user intention is analyzed by the AI agent; the AI agent dynamically calls a standardized tool to form a desensitization task chain according to a user intention and a business rule; and executing the desensitization task chain to process the target data, wherein the calling process comprises dynamic strategy adjustment based on context information. The system comprises an MCP service layer, a desensitization tool library module and an intelligent decision and integration layer. Through MCP protocol standardization integration, service rule driving and AI agent decision making, dynamic weight distribution and probabilistic algorithm selection of a desensitization strategy are achieved, the flexibility, the intelligent level and the system integration efficiency of data desensitization are effectively improved, and meanwhile the safety and compliance guarantee capacity is enhanced.

A system for detecting the motion state of a vehicle behind a mobile maintenance vehicle based on thunder-vision fusion belongs to the technical field of traffic intelligent control and comprises a multi-source sensing module, a target detection module, a trajectory tracking module and a vehicle motion state modeling module. A structured state output module performs standardized integration on vehicle target trajectory information provided by a detection and tracking module based on a unified data expression model to form a multi-dimensional structured data format including fields such as a relative distance, a relative speed, an acceleration, a vehicle type, a lane number, a frame timestamp and the like; consistency analysis and downstream calling can be conveniently carried out among different systems, the problem that a traditional detection system is difficult to integrate due to lack of a structured interface is avoided, the system supports flexible butt joint with a road sensing platform, a vehicle-mounted control system and an upper-layer data analysis module, good expansibility and cross-platform adaptation capacity are achieved, and the system is suitable for being popularized and applied. And the engineering availability and deployment efficiency of the traffic flow state information are obviously improved.

The invention discloses an urban rail transit energy management method based on multi-source fusion, and the method comprises the steps: employing a support vector machine algorithm to analyze a correlation mode between train intensive operation and passenger flow surge according to an obtained energy demand fluctuation index, and determining a potential energy consumption peak value position; the determined adjustment parameters are obtained, a power supply system control instruction is updated in combination with real-time train track information, and dynamic power supply load configuration is obtained; whether the obtained dynamic power supply load configuration is matched with the current passenger flow surge data or not is judged, if yes, a mode switching signal is sent to an equipment controller, and energy use feedback data after execution is obtained; according to the obtained energy use feedback data, evaluating the response accuracy of the system integration effect to demand fluctuation by adopting a gradient boosting decision tree algorithm, and determining further trajectory optimization suggestions; and updating a train operation scheduling model through the determined trajectory optimization suggestion to obtain an integrated multi-source information linkage mechanism.

The invention relates to the technical field of embedded system construction, in particular to a software dependency relationship automatic integration method and system based on meta packaging, and the method comprises the steps: S1, creating a meta packaging framework in an embedded construction system, and defining and constructing a stage hook through a Makefile; s2, automatically deploying the pre-compiled software package and the shared dependency library to a target directory of a construction system through a resource injection script; s3, analyzing a metadata file of the pre-compiled software package, extracting dependency relationship information, and generating a global dependency relationship list based on a directed acyclic graph topological sorting algorithm; and S4, inversely writing the global dependency relationship list into the dependency declaration configuration of the meta-packaging by utilizing the dependency aggregation script. By implementing the scheme, the problems of dependency sequence disorder and metadata auditing failure during embedded system integration are solved, the reliability of system integration is improved, the system construction efficiency is optimized, the safety of system construction is enhanced, and the maintainability and expansibility of system construction are improved.

The invention discloses a wireless and passive carrier roller monitoring device and a using method thereof, and belongs to the technical field of online monitoring, the wireless and passive carrier roller monitoring device comprises a stator assembly fixedly arranged on the outer side of a carrier roller shaft in a sleeving mode and a rotor assembly fixed in a bearing seat of a carrier roller uterus body, an annular or semi-annular circuit board is arranged in the stator assembly, a sensor module, an inductance coil and a wireless transmission module are arranged on the circuit board, and permanent magnets are evenly arranged on the side, close to the carrier roller uterus body, of the rotor assembly. According to the stator and rotor separation type structural design, the monitoring device and a carrier roller mechanical structure are deeply fused, direct capture of energy of a rotating part and stable acquisition of signals of a static part are achieved, the problems of power supply and wiring on a rotating body in a traditional monitoring scheme are solved, and the system integration degree and reliability are improved.

The invention discloses a multi-scene micro garbage real-time detection method and a multi-scene micro garbage real-time detection system, and aims to solve the defects of an existing garbage detection method in the aspects of small target identification, environmental adaptability and system integration. According to the method, on the basis of an improved YOLOv8 architecture, a Transform module and an efficient channel attention mechanism are fused, and the tiny target feature extraction and multi-scale information fusion capability is enhanced. The system adopts dynamic resolution reasoning, INT8 quantitative compression and time-space domain post-processing, so that the detection precision and the reasoning efficiency are remarkably improved. Experiments prove that the system effectively improves the small target recall rate and environmental adaptability in a garbage detection task, comprises eight corresponding modules, realizes full-process automatic detection, has relatively high real-time performance and automation level, is suitable for various application scenes such as smart city environment monitoring, and has important practical application value.

The invention discloses a full-process automatic voice-driven electronic medical record generation system, and belongs to the technical field of medical information. The invention aims to solve the problems of insufficient medical term precision, low medical record structuring efficiency, lack of clinical knowledge support, data security risk and the like of voice recognition in the prior art. According to the system, a full-automatic process from voice input to structured medical record output is realized by integrating medical level voice acquisition, term enhanced voice recognition, intelligent medical record generation based on a large language model and a knowledge base, multi-modal interaction and system integration and a full-process safety compliance system adopting edge computing and block chain technologies. According to the method, the medical record writing efficiency can be remarkably improved, manual errors are reduced, medical safety and data privacy are guaranteed, and the method has wide clinical application value.

The invention relates to the technical field of auxiliary diagnosis and treatment systems, in particular to an intelligent auxiliary diagnosis and treatment system for doctors. Comprising a system initialization and role service registration module, a user input analysis and intention recognition module, an intelligent routing and professional tool set selection module, an SSE streaming response initialization and workflow starting module, a knowledge graph retrieval and reasoning module, a diagnosis and treatment logic reasoning and scheme generation module and a streaming result output and node state updating module. A dynamic knowledge update triggering and verifying module; according to the invention, on the system integration level, efficient integration of multi-professional medical resources is realized based on role-as-a-service architecture, and complete coverage of multi-professional knowledge modules of internal medicine, surgery, oncology and the like is ensured through a role service registration validity evaluation mechanism.

The invention discloses a multi-factor coupled mountain area mountain torrent dynamic partition early warning method, belongs to the technical field of mountain torrent defense, and realizes dynamic partition early warning of mountain area mountain torrent disasters by systematically integrating multi-source data, quantifying multi-factor synergistic effect and constructing a differentiated early warning model. The method comprises the following steps: firstly, collecting and preprocessing high-resolution topographic data, multi-source meteorological and hydrological observation data and soil attribute data, dividing hydrological partitions and constructing a basic database; secondly, screening small watershed units with similar parameters by adopting a control variable method, and simulating and analyzing a coupling driving mechanism of rainfall, terrain and soil through a distributed hydrological model; constructing a dynamic partition early warning model based on a multi-factor action rule, and dynamically adjusting an early warning threshold by combining real-time data; and finally, outputting the mountain torrent risk levels of different hydrological partitions, and providing quantitative support for mountain torrent early warning in a mountainous area.

The invention discloses a drainage pipe network data cleaning and intelligent repairing method fusing multiple models. The method comprises the following steps that S1, original monitoring data flow is collected; s2, carrying out the preliminary detection and elimination of the abnormity based on IQR; s3, performing upstream and downstream multi-source feature extraction and XGBoost weight analysis; step S4, multivariable depth autoregression prediction based on PatchTST is carried out; s5, generating and publishing continuous high-quality data; according to the method, the key technical problems of various abnormal types, incomplete abnormal detection, low cleaning and repairing precision, poor data continuity and trend consistency, high system integration and real-time processing difficulty and the like commonly existing in the actual collection and transmission process of the online monitoring data of the drainage pipe network are solved.

The invention discloses a monitoring data trend analysis and decision suggestion generation method based on a large language model agent. The method comprises the following specific steps: step 1, constructing a domain knowledge enhancement module; 2, developing and registering a callable tool set, and compiling a series of performance functions by using Python; 3, constructing an agent arrangement platform and a core agent, and creating a master control agent in the agent arrangement platform as a scheduling center of an analysis task; step 4, executing an intelligent agent analysis process, and when an analysis request is received, enabling the intelligent agent to autonomously run according to the following process; 5, system integration and interface calling are carried out, the whole agent is packaged into RESTful API service and deployed in an enterprise server, by introducing a large language model agent architecture, an RAG enhanced reasoning mechanism and a callable tool set, the technical transition from passive data display to active insight generation is achieved, and the method has the advantages of being high in practicability and easy to popularize. And the analysis efficiency, the decision accuracy and the system intelligence level are remarkably improved.

The embodiment of the invention provides a thermal power plant waste heat gradient utilization management method and system, belongs to the field of energy and power engineering, and aims at solving the problems that in the prior art, waste heat gradient matching is unreasonable, the low-temperature waste heat recovery efficiency is low, and the system integration degree is insufficient. The method comprises the following steps: constructing a multi-physics field coupling digital twinborn model and performing real-time correction; establishing a dynamic matching matrix of the residual heat source and the heat consumption load based on balance analysis; solving a Pareto optimal operation scheme through a multi-objective optimization model; and generating and issuing a cooperative control instruction. The system comprises a panoramic data sensing module, a digital twin modeling module, a balance and optimization decision module and a cooperative control instruction generation module. Through global optimization and dynamic regulation and control, the waste heat utilization efficiency, the economical efficiency and the equipment reliability are remarkably improved.

The invention provides a cross-equipment circulation and collaborative operation and maintenance method for a gap atomization service, relates to the technical field of computer system operation and maintenance, is applied to complex scenes such as a smart park, and aims to solve the problems that services among different gap terminals cannot be seamlessly coordinated and the operation and maintenance efficiency is low. According to the method, granularity self-adaptive splitting and standardized packaging are carried out on atomization services by constructing a swan gap terminal cluster; a cross-device circulation scheduling center is established based on the Gawan distributed soft bus, and dynamic migration of the service sub-modules among the terminals is realized through a circulation decision algorithm; meanwhile, a collaborative operation and maintenance system is constructed, operation and maintenance data are integrated, and intelligent early warning and batch operation and maintenance are achieved through a fault prediction model; and the data consistency before and after service circulation is guaranteed by combining a distributed incremental synchronization mechanism. According to the method, through deep fusion of seamless circulation of atomization services and collaborative operation and maintenance, the multi-terminal service collaborative efficiency in a complex scene is improved, the resource utilization rate is optimized, and the operation and maintenance cost is reduced.

The invention discloses an intelligent medical inquiry and medical record generation method and system based on multi-technology fusion, and the system comprises a specialized dynamic pre-inquiry module which is used for sequentially executing the analysis of a patient chief complaint, the matching of a dynamic template, the intelligent inquiry and the completion and verification of a knowledge graph according to a chief complaint text inputted by a patient, and obtaining the comprehensive information of the patient; the dual-drive medical risk control module is used for performing dominant and recessive risk identification based on a rule engine and a large model in combination with an RAG knowledge base according to the comprehensive information of the patient, and realizing a collaborative early warning closed loop; the voice structuring and medical record generation module is used for executing double-track voice separation and transcription, specialized structuring processing and multi-source data fusion and EMR template matching in the dialogue process of the patient and the doctor to generate a structured electronic medical record; and the system integration and performance optimization module is used for adapting to a hospital HIS / EMR system through a standardized interface and executing data preloading and cache optimization to guarantee the response performance of the system.

The invention discloses an embedded system integration test case multiplexing method and system, and the method is characterized in that the multiplexing of a test case is carried out based on the existing software assets: firstly, based on the existing software requirements and the test case, an existing requirement-test case tracking chain is constructed; performing demand change analysis on the new demand activity graph information; performing change influence range analysis based on a change analysis result in combination with a demand-test case tracking chain, and determining a test case reuse and generation scheme; and finally, executing the test case multiplexing and generating scheme. According to the method, the tracking relation between existing requirements and test case assets is established, meanwhile, the execution cost of downstream test tasks caused by requirement change is reduced through multiplexing, and the test efficiency of integration testing is improved.

The invention discloses a safety fence protection method and device based on the Internet of Things technology, and belongs to the technical field of safety alarm and Internet of Things application, and the method comprises the following steps: respectively collecting the distance, heat source distribution and movement track information of a target protection area through an ultrasonic sensor, an infrared sensor and a millimeter wave radar; generating a virtual fence model with an initial alarm boundary by adopting a confidence weighted fusion algorithm based on environmental condition dynamic adjustment; environmental change parameters such as obstacle distribution and meteorological conditions are monitored in real time, when the change exceeds a preset threshold value, a dynamic boundary adjustment strategy is triggered, and the fence alarm boundary shape is optimized through a boundary node position redistribution mechanism; a distributed computing framework is adopted to decompose boundary detection tasks, and the tasks are dynamically allocated according to the real-time resource utilization rate of edge computing nodes. According to the invention, the problems of insufficient real-time performance, limited dynamic adjustment capability and low system integration degree in a complex environment in the prior art can be effectively solved.

The invention discloses a field event element extraction method and system based on prompt learning and knowledge completion, and the method comprises the steps: converting an extraction task into a constrained generation problem through structured prompt, achieving the stable extraction under a small number of labeled samples through the existing knowledge prior of a large language model, reducing the dependence on large-scale labeled data, and improving the extraction efficiency. And the cross-domain adaptive capacity is improved. A vectorization knowledge retrieval mechanism is introduced, related evidences are obtained from an external knowledge source, elements which are not clearly expressed in a text are complemented, the integrity of information is enhanced, and the accuracy and credibility of a result are improved through knowledge verification. And through a self-adaptive fusion mechanism of prompt and knowledge, the model can flexibly balance text context and external knowledge, and the robustness of complex context and fuzzy expression is improved. And finally, standardized and structured element data are output, a convenient interface is provided for downstream event atlas construction and analysis tasks, and the automation degree of domain knowledge processing and the system integration efficiency are improved.

The invention discloses a commercial password evaluation report automatic generation and quality control system based on an artificial intelligence technology, and the system comprises a multi-modal data processing and knowledge graph construction module which constructs a knowledge graph of a password evaluation field based on the multi-modal data related to the commercial password evaluation field; the field large model fine tuning module is used for constructing an instruction fine tuning data set in the password evaluation field, performing field adaptation training on the pre-trained large language model by adopting a hybrid fine tuning strategy, and generating a password evaluation field expert large model; the report automatic generation module is used for automatically generating a commercial password evaluation report based on the knowledge graph and the expert large model; the quality control module is used for carrying out automatic quality verification and evaluation on the report; and the system integration and optimization module is used for integrating the modules into an interactive application system and continuously optimizing the knowledge graph and the expert large model. According to the method, the commercial password evaluation report can be automatically generated, and the correctness and logic consistency of the report can be ensured.