1273results about How to "Achieve traceability" patented technology

The invention discloses a medical diagnosis intelligent decision-making system based on multi-modal data fusion, and the system comprises the steps: obtaining a medical image, biological features and electronic medical record data of a patient through a patient information collection unit, and extracting feature vectors from different modal data through a feature processing unit; the core of the system is a modal fusion engine, and the module combines a deep neural network and a Bayesian inference engine to realize deep learning and probability-dependent modeling of an association relationship between features; carrying out weight optimization on different modal features by adopting an attention mechanism through a self-adaptive feature optimizer, and generating optimized feature representation; the intelligent diagnosis analysis unit calculates the diagnosis probability based on the optimized features, performs auxiliary diagnosis in combination with a case knowledge base when the confidence coefficient is insufficient, and outputs a diagnosis report containing diagnosis categories, basis and suggestions; according to the method, multi-source heterogeneous data are fused, deep learning and probabilistic reasoning are combined, intelligent medical auxiliary diagnosis is achieved, and diagnosis accuracy and interpretability are improved.

The invention provides an intrusion detection and response method and system for a satellite internet target range, and relates to the technical field of network security technologies, and the method comprises the steps: carrying out the multi-dimensional real-time collection of the network flow of the satellite internet target range, carrying out the self-adaptive multi-scale feature extraction, constructing a network behavior baseline model, and positioning and recognizing the abnormal flow. Deploying an anomaly detection node based on edge calculation, performing preliminary screening at a track end, performing multi-angle association, and outputting an anomaly event report; a distributed intelligent honeypot is activated, deep interaction is induced, the information collection amount is maximized, multi-modal data is obtained, and multi-dimensional attack feature vectors and behavior sequences are generated; historical defense empirical data are integrated, a method of combining reinforcement learning and a genetic algorithm is utilized, a defense strategy is coordinated, a defense process is generated, and the defense process is recorded through a block chain and fed back to a network behavior baseline model.

The invention discloses a construction method and device of a multi-component data agent, and the construction method specifically comprises the following steps: obtaining sample data, and constructing a vector knowledge base; extracting a knowledge service flow according to the sample data, determining an ontology through knowledge representation, and forming a knowledge graph; training an initial language model based on the vector knowledge base, and giving a data processing model; and performing modularization arrangement and linking on the knowledge graph and the data processing model in combination with a logic tool chain and a thinking graph of the data intelligent agent, and constructing to obtain the multi-component data intelligent agent. According to the method, the knowledge graph is fused to construct the business rule engine, training data support is provided, the thinking chain process is modeled into the thinking graph of a graph structure, and the data processing model and the knowledge graph associated with the workflow node are executed, so that the accuracy of result reasoning of the data agent is enhanced, and the knowledge boundary is expanded; and particularly for financial auditing data, key information can be better extracted, and the working efficiency of power grid auditing personnel is improved.

The invention discloses a data quality monitoring method and system based on a knowledge graph. According to the method, data tables and fields in multi-source heterogeneous data are constructed into graph nodes, graph edges are constructed through main and foreign key constraint relationships and field value overlapping analysis, and a data blood relationship knowledge graph is formed; business rules are coded into the atlas to execute data constraint check, and an abnormal data influence range is traversed and tracked through a directed graph; and dynamic monitoring is realized based on the time change of the abnormal score sequence. The invention further provides system implementation including data acquisition and preprocessing, knowledge graph construction, quality monitoring, problem diagnosis and quality evaluation. The method solves the problems that a traditional method is difficult to track the influence range of abnormal data and lacks systematic evaluation, and improves the automation level and accuracy of data quality management.

The invention discloses a data security sharing method and system, and relates to the technical field of data security, and the method comprises the steps: receiving to-be-shared data uploaded by a data provider; fragmenting the data by adopting a biologically inspired adaptive fragmentation algorithm; generating a digital fingerprint for each fragment by using a multi-dimensional dynamic Hash algorithm; encrypting data fragments by adopting a post-quantum encryption algorithm and storing the data fragments in a decentralized network; recording the access authority, the digital fingerprint and the storage position information to a block chain security account book; when a data request is received, the access permission is verified; carrying out identity verification by adopting multi-mode biological feature recognition; after the verification is passed, obtaining a digital fingerprint and a storage position from the block chain; acquiring the encrypted fragments from the decentralized network and decrypting the encrypted fragments; verifying the integrity by using the digital fingerprint; and recombining the verified fragments to obtain complete data and returning the complete data to the requester. According to the method, efficient data sharing is realized while the data security is guaranteed, and the data value and privacy protection are effectively balanced.

The invention discloses a block chain-based digital right encryption management method and system, and relates to the technical field of digital right security management. According to the method, a unique identifier is generated for digital asset information through a Hash algorithm, data privacy is ensured by using asymmetric encryption, and asset uniqueness identification and information encryption protection are realized; according to the method, encrypted asset information is subjected to uplink evidence storage through an intelligent contract, node verification is carried out by adopting a consensus algorithm, tampering resistance, authenticity and transparency of the asset information are analyzed, authenticity of digital assets and safety and reliability of storage are guaranteed, legality of ownership transfer is verified through a consensus mechanism when the digital assets are transacted or transferred, and security and reliability of the digital assets are guaranteed. It is ensured that the transaction meets the preset compliance standard, the asset state on the block chain is updated in real time, and the security and compliance of the transaction are improved; the asset ownership history and the transaction path are tracked and recorded through the traceability mechanism of the block chain, and the traceability and transparency of the asset circulation process are realized.

The invention provides an agricultural product processing quality traceability monitoring method and system based on edge nodes, and the method comprises the steps: constructing an analytic hierarchy process model through employing the multi-source data of time-space alignment, decomposing an agricultural product processing quality evaluation index into a plurality of sub-indexes, forming an index hierarchical structure, and employing a fuzzy comprehensive evaluation method to obtain a traceability result of the agricultural product processing quality. The importance weight and the satisfaction degree of each sub-index are comprehensively considered, the comprehensive score of each scheme is calculated, and the processing quality of the agricultural products is judged; after the quality problem is processed, key control points and influence factors in the agricultural product processing process are analyzed through data mining, a quality evaluation model and a rule base are dynamically updated by adopting an incremental learning algorithm, and continuous optimization of the model is realized by continuously receiving new data samples and updating model parameters; and the accuracy of agricultural product processing quality evaluation is continuously improved.

The invention relates to the technical field of data desensitization, in particular to an electronic commerce platform information security desensitization scheme analysis system based on artificial intelligence, which comprises a data classification module, an access evaluation module, a dynamic desensitization module and a security audit module. According to the method, the data of the e-commerce platform is intelligently classified and evaluated by adopting an artificial intelligence technology, dynamic desensitization and finer access control of the data are realized, various data access requirements can be dynamically met by evaluating the sensitivity level of the file and the security level of the access request, data processing is performed according to requirements, and the data processing efficiency is improved. The security and privacy of user data are effectively protected, through automation of security audit, the recognition efficiency of abnormal behaviors is improved, the transparency and traceability of overall data management are enhanced, through real-time analysis and response, the risk of data leakage is reduced, and the practical value of data and the fluency of service operation are kept.

Along with promotion of industry 4.0 and rising of intelligent manufacturing, an industrial decision support system plays a more and more important role in improving enterprise operation efficiency and market competitiveness. The invention provides an industrial decision support system based on big data and artificial intelligence technology, and aims to provide accurate decision support for enterprises through deep analysis and real-time processing of massive industrial data. According to the system, by integrating an advanced data acquisition technology, monitoring and data capturing of the whole process of industrial production, including but not limited to production equipment states, process parameters, quality control, supply chain information and the like, are realized. By using artificial intelligence algorithms such as machine learning and deep learning, the system can identify a mode and predict a trend from complex data and construct a corresponding decision model. In addition, a built-in operation optimization engine of the system can provide an optimal solution for production planning, inventory management and resource configuration based on a data analysis result. Through an intuitive user interaction interface, a decision maker can easily obtain key business insights and intelligent decision suggestions so as to make a quick response in a continuously changing market environment. The industrial decision support system not only can improve the production efficiency and the product quality, but also can help an enterprise to reduce the operation cost, avoid potential risks, and promote the enterprise to transform to intelligence and digitization.

The invention belongs to the technical field of alkane gas leakage detection, and particularly relates to a leaked gas detection method based on deep learning and an optical flow method. The method comprises the following steps: simulating image data of real gas leakage by using a Gaussian plume model, and making a data set; a deep learning network YOLOv8-seg model is used for training; an intermediate infrared camera is used for monitoring an area where leakage may occur; when leakage occurs, a leakage gas area is detected and segmented, and a binary segmentation mask of leakage gas is generated; and analyzing the motion direction of the gas plume in the binary segmentation mask by using an optical flow method. The problem that leakage points are difficult to find in an existing gas detection means is solved, the diffusion track of leaked gas can be tracked more accurately, and rapid tracing is achieved.

The invention relates to the technical field of quality traceability management, in particular to an organic fertilizer quality safety traceability management method based on multi-source data. The method comprises the following steps: collecting organic fertilizer source data in real time from links of organic fertilizer production, circulation, storage and use, performing standardization processing, and performing production batch synchronous set processing and traceability association connection file establishment to generate an organic fertilizer multi-source data traceability association connection file; and carrying out production batch tracing extraction processing on the organic fertilizer multi-source data traceability association connection file, carrying out organic fertilizer quality index evaluation analysis and quality index error limitation analysis, carrying out organic fertilizer quality safety abnormity traceability processing and abnormity decision support processing, and generating an organic fertilizer quality safety abnormity problem decision support report. Therefore, the corresponding organic fertilizer quality safety abnormity traceability problem management work is executed. The scientificity and accuracy of organic fertilizer quality safety management can be improved.

The invention provides a multi-data fusion method and system based on edge computing. Belongs to the technical field of edge computing. The method comprises the following steps: receiving heterogeneous resource data from different sensors, equipment and sources through an edge task allocation unit; according to the encryption level corresponding to each task in the heterogeneous resource data, dividing each task data into data groups with identifiers according to a preset segmentation strategy; according to the operation condition of the edge task processor, performing distribution calculation on the data set according to a preset distribution strategy; after the distribution is completed, obtaining an identification sequence of the distribution mode; the distributed data set is calculated and processed through the edge task processor, and after data calculation and processing are completed, the result in the data set is sent back to the edge task distribution unit. Through intelligent scheduling of the edge task distribution unit, data from different sensors and equipment can be received and processed in real time, so that the data processing efficiency and response speed are improved.

The invention discloses an automatic contract text auditing method and system based on clause unified modeling, and belongs to the technical field of contract auditing, and the auditing method comprises the following specific steps: I, accessing a third-party credit investigation mechanism or a credit rating platform to extract the credit information of both parties of a contract, and carrying out the credit investigation and auditing of the both parties of the contract; iI, establishing an initial contract template classified according to economic businesses, and extracting contract key information through an NLP technology; the optimal rule can be quickly identified and selected in a large number of detection rules, the auditing efficiency is remarkably improved, the time required by manual intervention is shortened, the auditing process is more flexible, the method can adapt to specific requirements of different contracts, potential risks in the contracts can be comprehensively identified, auditing personnel can be helped to find high-risk terms in time, and the contract auditing efficiency is improved. The law risk in contract fulfillment is reduced, the benefits of colleges and universities are protected, more stable risk control and compliance guarantee are provided for the colleges and universities, the contract fulfillment condition is monitored through integration with an accounting system, the compliance and the executive force of the contract are ensured, and the transparency and the traceability of contract management are improved.

The invention provides an unmanned aerial vehicle visual language navigation method based on large model task analysis, and the method comprises the following steps: S1, decomposing a navigation instruction into a plurality of sub-tasks through a navigation task analysis module, and obtaining the text features of the currently executed sub-tasks through a sub-task switching module after the sub-tasks pass through a text encoder; s2, utilizing a visual encoder to obtain visual features of a current subtask input front view image; and S3, acquiring navigation action output corresponding to each moment of the current subtask through a multi-mode encoder by utilizing the visual text features. According to the method, the interpretability and the accuracy of visual language navigation action decisions can be improved.

The invention discloses an electronic archive data security system and method, and relates to the technical field of electronic document management. The electronic archive data preservation method comprises the following steps: acquiring a plurality of storage files to be stored and file attribute data of each storage file in real time; analyzing the risk level value of each storage file according to the file attribute data of the storage files, and marking the risk level value; classifying and storing the plurality of marked storage files into an electronic archive database; after a plurality of storage files are stored in a classified mode, time period access data of each storage file are obtained in real time, and real-time analysis is carried out in combination with risk level values. File attribute data are obtained in real time, and risk level evaluation is carried out on the file attribute data, so that the files can be marked and stored in a classified mode in real time; according to the method, the high-risk file is ensured to accept stricter protection measures, the risk assessment index is updated in real time, and the encryption strength is dynamically adjusted according to the risk assessment index, so that the protection capability on sensitive information is remarkably improved.

The invention discloses an industrial part defect traceability method, and relates to the field of quality prediction and quality traceability of industrial parts, and the industrial part defect traceability method comprises the following steps: obtaining defect data of the industrial parts; performing classification prediction on the preprocessed defect data based on a sequential logic regression tree model, and generating influence factors of process parameters on part defect formation in the industrial part production process based on a classification prediction result; constructing a causal graph database based on the influence factors of the process parameters on part defect formation, and performing causal graph modeling on industrial part defects to obtain an industrial part defect causal graph; and constructing a quality traceability model based on the industrial part defect causal graph, and performing quality traceability on the defect path by using the quality traceability model. According to the invention, the quality management efficiency and accuracy of industrial part production are improved, and the problem source can be quickly and accurately traced.

The invention provides a green power consumption classification accounting and right tracing method and system of a multi-dimensional account, and relates to the technical field of power, and the method comprises the steps: generating green power transaction matching data through collecting power utilization data and power generation plan data in real time; constructing a distributed account book node network, verifying the matched data, and generating a green power transaction block; and constructing a green power right traceability model based on the transaction block, calculating a green power consumption weight coefficient of the power consumption enterprise, and generating a green power consumption level and a right certificate. According to the invention, precise accounting and right traceability of green power consumption are realized, and the energy management efficiency is improved.

The invention discloses a campus smart canteen management platform based on AI and IOT. The campus intelligent canteen management platform comprises a global sensing system, an intelligent decision support system, a credible tracing system, a safety and supervision system and a user experience optimization system. The global sensing system is used for collecting multi-modal data and carrying out data fusion and preprocessing on the multi-modal data; the intelligent decision support system is used for performing multifunctional analysis on the fused and preprocessed multi-modal data to obtain a management decision; the credible traceability system is used for recording digital traceability information, storing reserved food samples and detecting pesticide residues in real time; the safety and supervision system is used for providing food reserved sample management, food quality event monitoring and analysis, supervision record management and parent applet functions; and the user experience optimization system is used for providing health inspection, health monitoring and personalized suggestions, identity verification and payment modes. In this way, the overall level of canteen management is improved.

The invention discloses an intelligent reverse scheduling optimization system and method for the traditional manufacturing industry. The system comprises a data collection module, a work order generation module, a resource verification module, a scheduling optimization module and a monitoring adjustment module. The data collection module is used for collecting order information and processing the order information to obtain a to-be-produced work order list; the work order generation module optimizes the equipment task based on the to-be-produced work order list to generate a work order; the resource verification module is used for checking whether the equipment and the raw materials meet the requirements of the work order and generating constraint conditions; the scheduling optimization module performs priority ranking on the work orders based on constraint conditions by using a reverse production scheduling algorithm to generate an optimized production plan; the monitoring and adjusting module is used for monitoring the production process and dynamically adjusting the production plan according to the actual situation. By optimizing the production process, the efficiency can be improved and the cost can be reduced in the traditional manufacturing industry, so that the overall competitiveness is enhanced.

The invention discloses an intelligent operation and maintenance network fault monitoring method, system and device and a medium, a communication network architecture is visualized through full-link topology reduction and real-time topology drawing, monitoring and maintenance are facilitated, and problems can be found and solved in time. Through combination of GIS and OTDR technologies, a communication optical cable fault point can be accurately positioned. The communication network architecture is visualized through full-link topology reduction and real-time topology drawing, monitoring and maintenance are facilitated, and problems can be found and solved in time; fault data in communication data can be quickly identified through real-time difference monitoring of communication data flow characteristics; by tracing the identified fault data and predicting the propagation path of the fault, multi-dimensional fault reason analysis and prediction can be carried out, the fault detection, positioning, tracing and prediction capabilities of the communication network are improved, and the intelligent operation and maintenance capability of the network is enhanced, so that the reliability of the network and the continuity of services are improved.

The invention relates to the technical field of medicine information management, in particular to a traceability method and system based on a medicine consumable traceability code. The method comprises the following steps: acquiring medicine purchasing information data; generating a unique code for the number of medicine purchasing information so as to generate a medicine warehousing traceability code; performing drug information association on the drug purchase information data according to the drug warehousing traceability code to generate a drug warehousing associated traceability code; storing the medicine warehousing associated traceability code in an information base to generate a medicine warehousing traceability code information base; performing scanning access mode division on the medicine warehousing traceability code, and generating a medicine dispensing scanning request mode and a medicine returning scanning request mode; and performing drug splitting tracing on the drug warehousing tracing code in a drug dispensing scanning request mode to generate drug splitting tracing data. According to the invention, through full-process closed-loop management, data association enhancement, intelligent scheduling optimization and drug return tracing improvement, the efficiency and precision of the drug consumable traceability code tracing method are improved.

The invention discloses a dynamic optimization algorithm-based citrus red wine brewing formula design method. The method comprises the following steps of S1, constructing an initial multi-dimensional data set; s2, generating an initial formula setting through a fuzzy adaptive coevolution algorithm; s3, dividing the brewing process into a primary fermentation stage, a middle-stage fermentation stage and a later-stage curing stage; s4, controlling exploration and development balance of each particle by using an improved particle swarm optimization algorithm, and performing real-time optimization by using a multi-agent coordination mechanism; s5, dynamically adjusting the variation amplitude in each stage by applying an adaptive variation distribution mechanism; s6, optimizing a specific brewing target in different brewing stages through a staged multi-target optimization system; s7, analyzing brewing data of each batch by applying a traceability analysis system; and S8, adjusting the initial formula setting of the next batch based on the traceability analysis result. According to the method, the fuzzy adaptive coevolution algorithm, the improved particle swarm optimization algorithm and the like are utilized, and multi-stage intelligent control over the brewing process of the citrus red wine is achieved.

The invention relates to the technical field of database establishment, in particular to a book library establishment method and system based on multi-version annotation. Preprocessing the data; comparing and analyzing the book contents of different versions by using a text comparison algorithm to generate a version difference report; performing preliminary annotation association; annotations between versions are traced and associated, a multi-version annotation comparison function is provided, a user can check annotations in different versions at the same position at the same time, and comparison and analysis are facilitated; storing data; designing a user interface, and providing functions of book version selection, content browsing and annotation viewing; recommending and optimizing intelligent annotations; authority control; according to the method, through intelligent annotation recommendation and optimization functions, a user can efficiently manage annotation information, repeated labor is avoided, and the utilization rate and management efficiency of annotation are improved.

The invention discloses a tongue picture characteristic change rule analysis method based on the rehabilitation process of a chronic disease patient, and relates to the technical field of health big data analysis, and the method comprises the steps of tongue picture data standardized collection, tongue picture data recording system establishment, tongue picture characteristic data analysis, result visualization and report generation. According to the invention, through a standardized tongue picture data acquisition mechanism, the quality and consistency of the tongue picture data are ensured, and through controlling illumination conditions, background colors, diet restrictions and posture requirements, the influence of external factors on the tongue picture data is reduced, and the accuracy and reliability of the data are improved. By comprehensively evaluating definition, noisy points, contrast ratio and color accuracy verification parameters, it is ensured that collected tongue picture data reaches a preset quality standard, and a high-quality data basis is provided for subsequent analysis; meanwhile, a comprehensive tongue picture data recording system is established, and tongue picture data are effectively stored and managed.

The invention belongs to the technical field of power system informatization, and provides a multi-modal power knowledge base construction method and retrieval method based on an AI large language model.The method comprises the following steps that S1, classification and named entity extraction are conducted on an original database, and a data set and an entity set are obtained; s2, aligning the data set and the entity set based on an AI large language model; s3, extracting and complementing entity relationships, and constructing an electric power diagram database; s4, performing joint embedding on the entities and the associated data, mapping the entities and the associated data to the same vector space, constructing a multi-modal power knowledge base, and generating a data index; and performing retrieval by using the constructed multi-modal power knowledge base, and performing source file backtracking through a data index. According to the method, multi-modal data are fused, the constructed multi-modal electric power knowledge base is rich and accurate, and source file backtracking can be carried out.

The invention provides a financial information management method and system based on a block chain technology. The method comprises the following steps: receiving a financial transaction request initiated by a user, and performing hash operation on a data block to generate a hash value of the data block; based on the hash value and identity verification information of the user, generating an encrypted signature by using an improved elliptic curve digital signature algorithm, and binding the transaction time of the preliminary transaction proposal with the encrypted signature by using a timestamp service; broadcasting the data block corresponding to the preliminary transaction proposal and the encrypted signature to a plurality of nodes by using a distributed network so as to determine an effective financial transaction; and linking the data blocks corresponding to the effective financial transactions to a preset block chain according to a time sequence and a logic relationship based on a Merkle tree structure, and regularly checking the consistency and integrity of data on the chain. The financial information management method which is high in safety, cannot be tampered and is traceable can be provided, and the safety of financial transactions is improved.

The invention discloses a multi-dimensional fusion prospecting prediction method based on multi-source data, and relates to the field of prospecting prediction. Comprising the steps of obtaining multi-dimensional data of a research area; constructing a multi-dimensional prediction model; respectively inputting the pixel-level geological data, the space-level geological data and the structure-level geological data into a multi-dimensional prediction model; respectively predicting the geological data of the three dimensions through a multi-dimension prediction model to obtain prediction results of the three dimensions; corresponding weights are introduced into a multi-dimensional prediction model, different weights are given to prediction results of three dimensions, weighted fusion is carried out, and a final prospecting prediction result is obtained and used for representing a potential mineral resource area. According to the method, the multi-dimensional geological data of the multi-source data are predicted by constructing the multi-dimensional prediction model, and the prediction results are subjected to weighted fusion, so that multi-dimensional feature fusion of the multi-source data is realized, and the prediction precision of mineral product prediction and the reliability of geological interpretation are improved.

The invention discloses an unmanned aerial vehicle cluster collaborative search method and system based on deep multi-agent reinforcement learning. The method comprises the following steps: defining a tracker unmanned aerial vehicle, a reconnaissance unmanned aerial vehicle and an intelligent target unmanned aerial vehicle; initializing an actor network, a commentator network and an experience playback buffer area for each unmanned aerial vehicle agent; each unmanned aerial vehicle agent selects an action and executes the action through an actor network according to the current state, and after the environment returns a new state and a corresponding reward, the interaction experience is stored in an experience playback buffer area; randomly extracting a batch of experience samples from the experience playback buffer area, and updating a commentator network; calculating gradients using the commentator network, and updating the actor network of each agent according to the gradients; and continuously performing interaction, data collection, network training and target network updating of the intelligent agent until a certain training period or performance standard is reached. According to the invention, the efficiency and safety of the unmanned aerial vehicle cluster for executing the collaborative search task in a complex environment can be improved.

The invention relates to the technical field of issuing management, in particular to an international standardization organization file issuing management system and method, and the system comprises a document content auditing module, a permission dynamic adjustment module, a manual auditing distribution module, a manual operation auditing module, a standard reference updating module and a file issuing module. According to the method, the standardization and accuracy of the file are ensured by analyzing the deviation between the format and the structure of the file, the auditing efficiency and the auditing quality are improved by distributing the file to the auditing personnel with the matched authority and performing priority ranking based on the sensitivity of the file type, and the auditing efficiency and the auditing quality are improved by monitoring the file revision operation and identifying the abnormal operation behavior. According to the file issuing management method and device, the safety and traceability of the file are improved, the timeliness and accuracy of the file content are ensured by updating expired and wrong content reference, the file issuing management efficiency is effectively improved, the risk of human errors is reduced, and accurate transmission and compliance of information are ensured.

The invention relates to the field of fan operation safety, and discloses a double-fan operation authority control system and method based on face recognition, and the system comprises an identity recognition module, a verification control module, a state feedback module, an authority management module and an abnormity monitoring module. The identity recognition module is used for collecting information of operating personnel and completing two-person in-place confirmation; the verification control module dynamically manages the double-person verification progress and adjusts an operation strategy; the state feedback module monitors the verification state in real time and generates a deviation signal for dynamically adjusting the verification strategy; the authority management module issues fan door authority and tool cabinet authority after verification is completed, and records an authority operation log; the abnormity monitoring module detects abnormal behaviors in the verification and permission using process, and when abnormity is detected, an alarm is triggered, and permission distribution is paused. According to the invention, the safety, verification efficiency and system reliability of fan operation are improved, and dynamic authority control and abnormity tracing functions are realized.