1260 results about "Data identification" patented technology

The invention relates to a multi-source sea area data intelligent supervision method, system and device and a storage medium, and the method comprises the steps: obtaining sea area geographic space data and historical sea area supervision data of a target sea area, and carrying out the monitoring construction, and obtaining an island monitoring grid; acquiring multi-source monitoring data of the target sea area according to the island monitoring grid, and performing grid data identification to obtain a global pattern spot dynamic data set; extracting a suspected specific target area coordinate of the global pattern spot dynamic data set and calculating a coordinate specific target confidence coefficient; and carrying out range comparison on the suspected specific target area coordinates, and carrying out behavior evaluation in combination with the coordinate specific target confidence to obtain a sea area specific target early warning report.

The invention relates to the technical field of slope stability data identification, and particularly provides a multi-sensor fusion slope stability intelligent analysis system and an edge calculation method.The system comprises an environment sensing subsystem, a data processing subsystem and an edge calculation subsystem, the environment sensing subsystem monitors the change of the soil moisture content through a soil humidity sensor, and micro movement data of a slope are obtained through a displacement sensor; using a pressure sensor to sense the change of the ground pressure; meanwhile, pre-screening out abnormal data to form a fused data set; the data analysis subsystem is used for converting abnormal data of the fusion data set into a quantitative index of slope stability, and deducing a potential sliding risk of the slope by analyzing the relevance of the soil water content, the tiny movement data or the ground pressure; and the decision support subsystem provides specific reinforcement countermeasures according to the analysis result of the potential sliding risk. According to the method, the potential sliding risk of the side slope is comprehensively deduced by analyzing the relevance of the soil water content, the tiny movement data and the ground pressure.

The invention discloses a bridge construction abnormity monitoring data identification method and system based on big data, and relates to the technical field of bridge construction monitoring, the system comprises a collection module, an analysis module, a process matching module and an execution module, the collection module collects construction data, transmits the construction data to the analysis module, carries out linkage verification on the construction data through an analysis unit, and carries out process matching on the construction data; identifying and outputting abnormal data, transmitting the abnormal data to a process matching module, constructing a judgment standard for dynamic matching through a construction process, comparing the abnormal data with the judgment standard, outputting a preliminary judgment result, transmitting the preliminary judgment result to an execution module, executing multi-stage cross validation on the preliminary judgment result, and outputting a final abnormal judgment result. By responding to multi-source data, missed judgment is prevented, a space-time linkage verification mechanism is constructed to improve anomaly recognition accuracy, a dynamic judgment standard adapts to risks of all stages, false anomalies are filtered through three-stage verification, the efficiency is improved through full-process automation, hidden danger early warning is assisted, accidents are avoided, and bridge construction quality and safety are guaranteed.

The invention relates to a mechanical equipment fault data identification method based on artificial intelligence, and belongs to the technical field of data processing and artificial intelligence. The problems of insensitive signal processing, insufficient feature extraction, significant noise interference, low model training efficiency and the like in fault diagnosis in the prior art are solved. According to the method, a self-adaptive normalization method based on energy density is provided, the influence of non-stationary signals is effectively inhibited, and key features are reserved; through mixed energy entropy feature extraction and inter-band energy jump penalty terms, the sensitivity to a complex fault mode is significantly enhanced; constructing a fault feature enhancement strategy based on a Gaussian potential well, and reinforcing the response of a fault feature accumulation area; a weighted loss function and a gradient directional correction mechanism are adopted, so that the robustness and accuracy of the model are improved; and in combination with an entropy weighted learning rate and a covariance scaling strategy, adaptive training is realized, and the convergence speed and adaptability are improved. According to the method, the precision and efficiency of mechanical fault diagnosis are improved, and support is provided for industrial intelligent development.

The invention relates to the technical field of broadband spectrums, and discloses a broadband spectrum monitoring system based on dynamic channel allocation, which comprises a data acquisition unit, a data identification unit, a region division unit, a data alignment unit and a channel allocation unit.The system dynamically adjusts the coverage area through beacon nodes, accurately calculates the coverage radius in combination with the urban environment, and improves the coverage efficiency. Meanwhile, a channel allocation strategy is optimized by a game theory, the change of a frequency spectrum environment is responded, and frequency spectrum state parameters are constructed by fusing multi-dimensional characteristics, so that communication smoothness is guaranteed, blind areas of monitoring nodes are eliminated, data space-time alignment is realized, the monitoring sensitivity and the resource utilization rate are effectively improved, and stable frequency spectrum and reliable communication under large-scale activities in cities are ensured; and the dynamic and reasonable distribution of the channels is ensured.

The invention relates to the technical field of inventory management, in particular to an intelligent warehouse management system for main shaft parts based on digital twinning, which comprises a main shaft mapping module for acquiring a main shaft shipping space and an allocation path, comparing shipping space nodes with a moving timestamp difference to generate an identification result, the fluctuation identification module extracts channel nodes to analyze fluctuation threshold deviation and generate a report, the allocation control module generates a scheduling list based on queuing duration and shipping space data distribution weight, and the abnormity positioning module extracts unallocated parts to analyze interface difference and mark abnormal links. And the state early warning module integrates four indexes of abnormal data identification risk paths to generate a management scheme. According to the method, bin position nodes and main shaft movement timestamps are synchronously compared to identify difference nodes, a dynamic weight distribution mechanism is established in combination with channel node fluctuation analysis, an interruption link difference trend and interface delay data are integrated to construct a risk assessment model, and abnormal conduction path positioning and early warning are realized. And scheduling conflicts and resource waste caused by data deviation are reduced.

The invention discloses a memory data migration method and electronic equipment, and relates to the technical field of computers, and the method comprises the steps: determining the access heat of a storage device accepting the access of a computing node based on a memory access task executed by a switching controller and an address mapping table, so as to determine a source device and a candidate data unit, according to the access parameter of the candidate data unit and the state parameter of the storage device, calculating a performance score after the candidate data unit is stored in the storage device so as to determine the data unit to be migrated and the target device, so that cold and hot data identification from the perspective of a computer fast interconnection memory heterogeneous system is realized; the rationality of data migration is improved on the basis of quantitative performance scores; according to the method, the to-be-migrated data unit is controlled to be migrated from the source device to the target device, and the address mapping table is updated, so that the data migration task is prevented from influencing the host work of the computing node, and a cold and hot data migration scheme without being perceived by the host in the computer fast interconnection memory heterogeneous system is realized.

The invention relates to the technical field of financial accounting and artificial intelligence combination, in particular to an artificial intelligence-based financial data identification and accounting method, which comprises the following steps of: firstly, calculating a fractal dimension by using a multi-level grid splitting technology, and constructing a fractal financial semantic map covering transaction and exchange entities; and performing interference operation by using a quantum mixing aggregator, fusing fractal atlas attributes and subject candidate results to obtain interference scores, and performing symbol rule screening or manual auditing confirmation to generate calibrated subject information. Then, the subject information is used for automatically generating an accounting entry and performing account book archiving, and the whole decision process is recorded under the quantum hash technology; by continuously analyzing archived decision data, the system can adaptively adjust fractal dimension calculation and interference parameters, and precise recognition and efficient accounting of complex financial data are realized.

The invention discloses a credit loan fraud data identification method and system based on artificial intelligence and a graph algorithm, and relates to the technical field of credit loan risk control. User information and behavior data are collected from multiple sources, a relation network graph is constructed after cleaning, dense communities are identified, cross-community intermediary nodes are positioned, community portraits are generated, behavior patterns are quantified, and similar groups are clustered. According to the method, a complex fraud mode is accurately recognized, hidden association is mined through a graph algorithm, the recognition accuracy is improved in combination with community portraits and behavior clustering, the dynamic adaptability is high, real-time feedback and incremental learning ensure that novel fraud is rapidly coped with, and the method is suitable for popularization and application. A high-interpretability report meets compliance requirements, user trust is improved, large-scale data is efficiently processed, modular design adapts to different credit and loan scenes, cost is reduced through automatic decision, the omission ratio is reduced, fraud loss is directly reduced, and the rate of return on investment is high.

The invention relates to the technical field of carbon emission data processing, in particular to a carbon emission data processing method, device and equipment and a readable storage medium. According to the technical scheme, the method comprises the steps of data acquisition and preprocessing, abnormal data identification and correction based on a hybrid machine learning model, energy optimization configuration assistance and real-time monitoring and feedback adjustment, and data acquisition and preprocessing: constructing a multivariate data acquisition network and preprocessing the acquired data; therefore, standard carbon emission data for subsequent analysis can be obtained. Through a multi-element acquisition network and fine preprocessing, the comprehensiveness, accuracy and timeliness of data are greatly improved, a foundation is built for accurate analysis, abnormity is accurately recognized and corrected through a hybrid machine learning model, the accuracy is improved, data reliability is guaranteed, association between carbon emission and energy is mined, enterprises are assisted in optimizing energy configuration, and the energy utilization efficiency is improved. The carbon emission is reduced, and the system stability is enhanced by real-time monitoring feedback.

The invention discloses a horizontal wind field inversion method and device based on the radial velocity of a wind profile radar, and relates to the technical field of wind profile radars, and the method comprises the steps: reading the radial velocity data of a wind profile radar in each direction wave beam, carrying out the rearrangement and splicing arrangement of the radial velocity data of each direction wave beam according to a preset sequence, and carrying out the reconstruction of the radial velocity data of each direction wave beam; forming a beam radial velocity profile; performing data identification, one-dimensional median filtering and one-dimensional moving average filtering on the beam radial speed profile in sequence so as to perform consistency processing on the data of the beam radial speed profile; carrying out time-space continuity processing, and then carrying out time consistency average processing; and obtaining an inverted horizontal wind field according to the processed radial speed data of the beams in all directions based on a space geometrical relationship between the wind field and the radial speed data of the beams in all directions. According to the invention, the problem of low accuracy during horizontal wind field inversion in the prior art is solved.

The invention relates to the technical field of the Internet of Things, and discloses a charging pile control system based on the Internet of Things, and the system comprises the steps: generating a charging pile hardware identifier according to charging pile identifier data, and carrying out the digital binding of the charging pile hardware identifier, and obtaining a charging pile unique identifier; performing data identification on the charging pile operation data by using the charging pile unique identifier to obtain fusion data, and constructing a resource sharing network based on the fusion data; the method comprises the following steps: extracting idle resources and resource demand data in a resource sharing network, constructing a multi-layer dynamic resource pool table according to the idle resource data, and performing resource matching on the resource demand data and the dynamic resource pool table to obtain resource nodes; and performing data distribution on the resource nodes according to the resource sharing network to obtain distribution data, and performing charging pile resource calculation on the distribution data by using the resource nodes to obtain distribution power. According to the invention, the problem that the current charging pile control system still has insufficient control accuracy in charging pile resource allocation can be solved.

The invention provides a WSI long-tail data identification method and device based on multi-modal distillation guidance and a readable storage medium thereof, and aims to solve the problem of low tail category identification accuracy caused by long-tail distribution in weak supervision multi-instance learning of a digital pathological total image (WSI). According to the method, performance improvement is realized through double-branch integrated multi-instance learning, text feature extraction and prompt generation and multi-modal distillation optimization. The double branches are trained by adopting balanced distribution and original long-tail distribution data respectively, an aggregator is shared, and prediction consistency is constrained; text features containing semantic prompts are generated to serve as supervision signals, and semantic alignment is achieved through image-text comparison loss; soft and hard label collaborative distillation loss is designed, and a total loss function is constructed in combination with consistency loss. Experiments show that the algorithm can significantly improve the tail category and overall recognition performance, is high in universality, can adapt to various multi-instance learning methods, and provides reliable technical support for intelligent analysis of pathological images.

The invention discloses a data management method and device, electronic equipment and a medium, and the method comprises the steps: responding to obtained transmission data of first equipment, extracting the transmission data, and obtaining a check code and equipment information; when it is confirmed that the transmission data is qualified according to the check code, desensitizing the transmission data, and determining an encryption confidentiality level mode corresponding to the transmission data according to the equipment type indicated by the equipment information; according to an encryption method corresponding to the confidentiality level, encrypting the desensitized transmission data by adopting an encryption mode to obtain encrypted data; performing desensitization processing and identification processing on the encrypted data to obtain encrypted identification data after desensitization processing; and storing the encrypted data identification data after the desensitization processing to a target storage area. The method can be applied to the business fields of financial science and technology, medical health, old-age care and the like, the overall data safety is improved, privacy protection of sensitive information is increased, meanwhile, the data management and tracing capacity is improved, and balance between safety and performance is achieved.

The invention discloses an AI-based regulation and control abnormal data identification and correction method, system and device and a medium, and belongs to the technical field of power system regulation and control, and the method comprises the steps: obtaining multi-source data of a main distribution network, carrying out the preprocessing, and constructing a power grid topology knowledge graph; the multi-source data comprises full voltage class model data, real-time measurement data and equipment state data. According to the method, a three-layer detection system of a statistics-density mixed detection layer, a graph neural network association detection layer and a time sequence Transform detection layer based on an LOF algorithm and a 3 sigma principle is constructed, deep association analysis of multi-source data is realized in combination with a power grid topology knowledge graph, and an intelligent correction module is used for matching a differentiation correction algorithm for different types of anomalies; the correction weight is dynamically adjusted based on reinforcement learning, and the problems that a traditional method depends on artificial experience to set a threshold value, the recognition capacity of a single algorithm is limited, a correction strategy lacks adaptability, and multi-source data association analysis cannot be achieved are solved.

The invention provides a power cable test data identification method and system, and the method comprises the steps: obtaining an electrical signal sequence of a power cable under a plurality of preset test condition combinations, carrying out the space-frequency joint feature decomposition of the electrical signal sequence, and constructing a three-dimensional time-frequency data body comprising a space position, a time sequence and a frequency feature; constructing a dynamic phase space on the basis, reconstructing a state trajectory of each physical segment, calculating a fractal dimension spectrum and a nonlinear divergence coefficient set of the trajectory, and generating a defect evolution characteristic tensor; inputting the defect into a spatio-temporal chaotic mapping network, aligning characteristic evolution paths of different physical segments, and constructing a defect propagation spatio-temporal atlas; and analyzing the fault development trend of each physical segment in the time dimension and the defect diffusion path of each physical segment in the space dimension, and outputting an identification result containing a defect type evolution probability sequence and a three-dimensional position offset field. According to the invention, a more accurate and comprehensive basis is provided for state evaluation and maintenance of the power cable.

This disclosure describes methods, devices and systems for abnormal network data identification and policy enforcement. An example method includes obtaining incoming network data from a network device, the networking data including operating information for the network device and packet metadata. The method also includes classifying the incoming network data using one or more machine learning models, including identifying abnormal network data from the incoming network data. The method further includes causing a policy rule to be generated based on the abnormal network data.

The present invention belongs to the technical field of incremental-data synchronization. Provided is an incremental-data identification, positioning and updating method. The incremental-data identification, positioning and updating method based on an Ethereum MPT in the present invention uses a storage structure of an Ethereum MPT in the blockchain field and performs adaptive fine-tuning on the storage structure, and applies the hash pointer and prefix sharing characteristics of the Ethereum MPT to incremental-data identification, positioning and updating in the field of data synchronization.

The invention provides an electrocardiogram monitoring and data analysis system based on artificial intelligence, which relates to the technical field of data analysis, and comprises a data acquisition module used for connecting an electrocardiogram sensor and generating an electrocardiogram signal time sequence data set; the de-noising processing module is used for calling the joint sensor network, executing synchronous data acquisition to establish an additional data set, and performing data de-noising to establish a data de-noising result; the feature extraction module is used for calling a feature extraction network, activating time domain, frequency domain, nonlinear and deep learning feature sub-channels to perform feature extraction, and establishing a feature set; and the analysis and identification module is used for performing similarity analysis and comparison on the joint features and a preset feature library and performing data identification. The technical problems that in the prior art, electrocardiogram data analysis mostly stays at a basic feature extraction stage, comprehensive analysis capacity for multi-dimensional features is lacked, complex information in data cannot be fully mined, and consequently the accuracy and comprehensiveness of signal analysis are insufficient are solved.

The present invention relates to the technical field of intelligent out-of-distribution fault detection for construction machinery, and discloses an out-of-distribution fault detection method and system based on energy propagation and graph learning, and the method includes: acquiring vibration acceleration signals in typical fault states, carrying out similarity calculation to obtain an adjacency matrix composed of the maximum mutual information coefficients, and taking the adjacency matrix as input in a graph neural network; carrying out feature extraction on the adjacency matrix through adopting a GraphSage graph convolution method, and generating each node representation; calculating an energy score of each node, and distinguishing between in-distribution data and out-of-distribution data; and enhancing out-of-distribution data confidence estimation for each node, and carrying out out-of-distribution data identification and out-of-distribution data detection under different working conditions of a rolling bearing.

The invention discloses a transformer insulating oil chromatographic data abnormity identification method, and relates to the technical field of data identification, and the method comprises the following steps: carrying out dissolved gas analysis on an insulating oil sample through a gas chromatograph to obtain a chromatographic original response curve; performing baseline correction and de-noising processing on the chromatographic original response curve, positioning each gas peak based on a first-order derivative method and a second-order derivative method, and extracting chromatographic peak height and chromatographic peak area parameters; according to the extracted chromatographic peak height and chromatographic peak area, three Duval fault ratios are calculated, and the ratio of the total soluble gas concentration to each gas concentration threshold value is calculated; combining into a multi-dimensional diagnosis feature vector; according to the method, the chromatographic peak shape of the insulating oil sample is subjected to multi-stage baseline correction and de-noising treatment, and the gas peak position is accurately positioned based on the first-order and second-order derivative methods, so that the high precision of chromatographic peak height and chromatographic peak area extraction is ensured, the accuracy of dissolved gas concentration calculation is improved, and the misjudgment risk caused by noise or baseline drift is reduced.

The invention relates to an abnormal data identification method and device, computer equipment and a storage medium. The method comprises the following steps: constructing a user social network graph according to user communication information; dividing risk groups according to the user login information and the user transaction information to obtain high-risk groups; performing association marking in the user social network graph according to the high-risk group to obtain a potential abnormal group; marking a target user to be identified for the user login information and the user account information of the potential abnormal group; and obtaining the logout times of the to-be-identified target user, and marking the to-be-identified target user as target abnormal data according to the logout times. The method and the device can be applied to financial anti-fraud application scenes, target abnormal data with fraud risks in users can be effectively and accurately identified, and the identification reliability is improved.

The invention discloses a data security intelligent treatment method based on data identification. The method comprises the following steps: identifying a data source and identifying the data source; configuring the category and the level of the data, obtaining a classification and grading retrieval rule of the data content, and performing classification prediction on the data content by utilizing the intelligent classification model obtained by training; identifying structured data in various databases, identifying various unstructured data in various file servers, and extracting identification information; various data security protection devices are added and managed, and a security protection strategy based on data identification is configured and issued; collecting logs and identification information, and obtaining an abnormal record or a risk alarm through a security audit strategy; obtaining an abnormal data access behavior of the log, recording abnormal feature information, and generating an abnormal detection result; and carrying out fusion correlation analysis on the obtained abnormal record or risk alarm and the abnormal detection result. According to the invention, efficient, intelligent and system-linked data security management is realized.

The invention discloses a data filtering method and system for a digital twin industrial control safety target range. The method comprises the following steps: firstly, marking an IP address of an industrial control system network in a segmented manner; feature extraction is carried out on the network behavior data of each IP segment, and splicing is carried out on the IP segment label to serve as a feature sample for constructing an industrial control network security identification model; a plurality of inverted residual modules with the same structure are adopted as core construction units, network behavior features in the feature samples are extracted and learned, and classification training is carried out; deploying the trained industrial control network security identification model to a pre-filtering module of a digital twin industrial control security target range, and carrying out classification, identification and filtering on network behavior data flowing in each IP segment in real time; and the misjudged sample is subjected to backtracking analysis, and the IP marking rule is updated. According to the technical scheme, the accuracy of network behavior data identification can be improved, so that the effectiveness of industrial control system simulation is improved.

The invention relates to a disaster monitoring data identification method and device, a storage medium and electronic equipment, and relates to the technical field of disaster monitoring, and the method comprises the steps: carrying out the fitting of a monitoring data fluctuation curve and a corresponding normal fluctuation curve, and obtaining a fitting rate; if the fitting rate is smaller than a preset fitting rate threshold value, determining the corresponding monitoring dimension as a suspected abnormal fluctuation dimension, and determining a first risk coefficient of monitoring data abnormal fluctuation of the suspected abnormal fluctuation dimension influenced by an actual interference event; when the first risk coefficient does not exceed a preset first threshold value, determining that the monitoring data fluctuation of the suspected abnormal fluctuation dimension is abnormal fluctuation; and when the first risk coefficient exceeds a first threshold value, determining whether the monitoring data fluctuation of the suspected abnormal fluctuation dimension is abnormal fluctuation or not based on other suspected abnormal fluctuation dimensions. The method has the effect of improving the accuracy of identifying the disaster monitoring data.

The invention relates to a target molecule identification and quantification method based on a solid nanopore detection technology, electronic equipment and a computer readable storage medium. The method comprises the following steps: (1) acquiring current signal data generated by a target molecule through a nanopore sensor within a period of time; (2) carrying out filtering processing on the current signal data; (3) identifying the filtered data to obtain a target via hole event set; (4) filtering the target via hole event set, and extracting signal data of the filtered events to obtain a target molecule recognition event; and (5) calculating the average interval time of occurrence of the target molecule recognition event, and obtaining the occurrence frequency of the target molecule based on the average interval time, namely the relative concentration of the target molecule. According to the method, automatic identification and quantitative analysis are realized aiming at mass signal data generated by a solid-state nanopore detection technology, and the analysis efficiency and accuracy are improved by optimizing a data identification method.

The invention provides an energy efficiency prediction and energy-saving scheduling optimization system and method for a food cold chain system, and relates to the technical field of cold chain system energy-saving scheduling, and the system comprises a data recognition module which is used for recognizing cold chain loading state data, and analyzing the space influence characteristics of the loading state data of a cold chain loading space; the strategy obtaining module is used for carrying out energy efficiency optimization on cold chain energy supply according to the space energy efficiency relation between the cold chain space and the link energy supply parameters and the space influence characteristics; the state chain establishment module is used for carrying out loading state dynamic analysis based on the cold chain vehicle path and predicting a path switching node; and the optimization strategy obtaining module is used for carrying out path state node scheduling optimization according to the cold chain path state chain by taking a space energy efficiency strategy as an initial parameter based on the vehicle energy supply coupling relationship and the space energy efficiency relationship of the cold chain system. The technical problem of energy consumption waste in cold chain transportation in the prior art can be solved, and the technical effect of reducing cold chain energy consumption is achieved.

The invention provides a matched tobacco appliance identification method and system based on a metal heating inductor, and belongs to the technical field of data identification. In the process of heating the metal heating inductor, a plurality of temperature points are set, real-time temperature parameters and electromagnetic parameters of the metal heating inductor are obtained, and a temperature-electromagnetic mapping table is established; constructing a three-dimensional parameter grid based on the electromagnetic parameters, performing mean division on the three-dimensional parameter grid to obtain a mean grid, and constructing a coupling change rate matrix according to the mean grid; performing eigenvalue decomposition on the parameter coupling change rate matrix to obtain a deformation eigenvector, and comparing the deformation eigenvector with a pre-stored appliance mode recognition vector to obtain a mode similarity score; acquiring an appliance judgment mode based on the mode similarity score, and executing a mode mechanism according to the appliance judgment mode; the interference of temperature drift on an identification result is reduced, the problem that a traditional identification mode depends on voltage, current and resistance value detection is solved, and the misjudgment probability is reduced.

The invention relates to the technical field of cable monitoring, in particular to a graphene cable monitoring system based on deep learning, which is used for extracting time-frequency domain characteristics by acquiring current, voltage, electromagnetic wave signals and temperature distribution data and utilizing Fourier transform and wavelet transform to improve the data identification degree. Gaussian filtering noise reduction is carried out on temperature data, and an abnormal hot spot area is identified through image segmentation, so that the local overheating detection capability is improved. And the edge calculation module fuses multi-source data, eliminates acquisition delay by utilizing feature alignment, and improves data synchronism and fusion quality. Through a multi-head self-attention mechanism, time sequence characteristics of historical monitoring data are extracted, and change modes of current, voltage, electromagnetic wave and temperature distribution are learned. And calculating an attention weight matrix to extract correlation between time steps, and forming a time sequence feature matrix. The characteristic matrix is subjected to nonlinear transformation through a feedforward neural network, cable state parameters of a future time step are predicted, the cable state is evaluated in advance, and the fault risk is reduced.

The invention discloses an inspection report generation method and device, equipment and a storage medium. The method comprises the following steps: determining abnormal inspection data and a report object of data center equipment; determining structured inspection data according to the abnormal inspection data, a preset equipment knowledge graph and a historical case library; determining a report constraint condition according to the report object and a preset report generation rule; and inputting the structured inspection data and the report constraint condition into a pre-trained large language model, and generating an inspection report oriented to the report object. According to the technical scheme, the preset equipment knowledge graph and the historical case library are constructed in advance, the fault linkage effect and the historical similar cases across the equipment are identified based on the inspection data, the inspection reports facing different report objects are dynamically generated, and the generation efficiency, the flexibility and the comprehensiveness of the inspection reports are improved.