628 results about "Influence analysis" patented technology

The invention relates to the technical field of surface quality detection, in particular to a PVC floor surface coating defect detection method and system, and the method comprises the steps: carrying out region division on a to-be-detected coating surface, obtaining a region scanning image, and carrying out image optical compensation on the region scanning image; analyzing high-dimensional image features of each area scanning image, performing difference analysis on the high-dimensional image features and reference features, and selecting a surface defect area; a high-dimensional defect feature analysis model is established, defect positioning information and defect geometric parameters are analyzed and generated, and the defect type of the surface defect area is judged according to the defect positioning information and the defect geometric parameters; and according to the defect positioning information and the defect geometric parameters, performing quantitative evaluation on the sensitivity weight of the surface performance of the to-be-detected coating in combination with defects, and outputting an overall surface performance influence analysis result. According to the invention, the problem that the capability of identifying small-size and complex-shape defects is insufficient is effectively solved.

The invention relates to the technical field of data analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data consanguinity map processing method, device, equipment and medium, and the method comprises the steps: collecting metadata change information and data operation event information, and building a mixed consanguinity map model; monitoring a metadata change event and a data operation event, and triggering incremental updating based on an event type; and performing data change influence analysis based on the updated blood relationship map model, generating an analysis result, and outputting the mixed blood relationship map model and the data change influence analysis result. According to the invention, by collecting metadata, incrementally updating the blood relationship map and carrying out data change influence analysis, full-link data tracing and influence analysis are realized, the problems of blood relationship breakage and insufficient real-time data tracing in traditional data blood relationship analysis are solved, and the accuracy of data tracing and the timeliness of business decision are improved.

The invention provides an automatic generation method and system for an intelligent pavement maintenance scheme, and relates to the technical field of road maintenance, and the method comprises the steps: obtaining a pavement image, and recognizing a disease through a convolutional neural network; a multi-scale grid is utilized to adaptively extract geometric features, and dynamic evaluation vector classification is constructed; matching a basic scheme based on the evaluation result; a maintenance scheme is optimized through a cross attention enhanced hierarchical decision network; and space-time influence analysis and dynamic adjustment are carried out. The pavement maintenance efficiency and quality are improved, the resource waste is reduced, and the influence on traffic is reduced.

Systems and processes are disclosed for a multidimensional, cross-node, hashchain, network-based Meta-Content Enabler (MCE) providing real time anomaly detection and impact analysis. Unique hexadecimal sequence identifiers are generated based on real time indexing and categorization to create a golden virtual metadata copy used by AI engine to determine content score to identify the degree of deviation therefrom, and to identify the hexadecimal nodes in the hashchain. The identified discrepancy is verified across cross-node hashchains to give end to end parallel impact analysis on the anomaly. By leveraging real-time monitoring, multidimensional verification, and blockchain-based storage, the system provides a robust and efficient solution for ensuring the accuracy and integrity of user activities. The system integrates privacy-preserving techniques, such as differential privacy or secure multi-party computation, to protect sensitive metadata. These techniques enable the system to analyze and process the metadata while preserving the privacy of individual users.

The invention provides a cluster reliability test method based on fault simulation, which comprises the following steps: acquiring injection parameter information, and performing fault simulation in a cluster based on the injection parameter information and a fault transfer model to obtain a complex fault scene; key performance index records are obtained in real time based on the complex fault scene; executing a reliability detection step based on a preset reliability detection model and the key performance index record, and obtaining a data analysis report; and obtaining a reliability score, an influence analysis result and a risk prediction result based on a data analysis report, a weighted scoring algorithm, an anomaly detection algorithm and linear regression analysis, and completing reliability detection of the cluster. According to the cluster reliability test method and system based on fault simulation provided by the invention, the test efficiency and the reliability detection level are greatly improved, the fault simulation is carried out in the cluster and the reliability detection model is combined, so that the accurate quantification of the reliability detection result is realized, and the reliability detection efficiency and the detection capability are improved.

The invention provides an artificial influence weather-based image recognition method and system, and the method comprises the steps: firstly obtaining a meteorological image data set of a target region containing a plurality of original meteorological images in sequential arrangement, carrying out the feature extraction of the original meteorological images, and obtaining a meteorological element distribution feature and a dynamic evolution feature; and then, based on a preset weather influence analysis model, carrying out weather influence analysis on the meteorological element distribution characteristics and the dynamic evolution characteristics, generating weather intervention decision characteristics, finally, according to the weather intervention decision characteristics, generating an operation strategy of artificial influence weather, and sending the operation strategy to operation execution equipment to trigger weather intervention operation. Therefore, meteorological image features can be comprehensively and deeply analyzed, the operation strategy is scientifically and accurately generated, and the effect and efficiency of artificial influence weather operation are improved.

The invention belongs to the technical field of smart campus, and relates to a smart campus safety management early warning method and system based on big data. The method comprises the following steps: collecting registration information, real-time behavior data and state information of an environment of a foreign vehicle; constructing a dynamic space-time diagram, and generating an edge attribute reflecting a relationship between deployment nodes in the dynamic space-time diagram; calculating a reasonable path set of the foreign vehicle, and analyzing trajectory deviation data of the foreign vehicle; abnormal scoring is carried out on the external vehicle; generating a corresponding intervention control strategy; performing causal influence analysis and generating a decision basis; and training the local graph neural network model, and carrying out centralized aggregation and distribution updating on shared model parameters in a training result of the local graph neural network model. According to the method, a dynamic behavior modeling strategy based on a heterogeneous graph neural network is introduced, structural modeling and time sequence behavior evolution expression of campus moving target multi-source data are achieved, the system can capture tiny anomalies in individual trajectory evolution, and the abnormal behavior recognition sensitivity is improved.

The invention relates to the field of power systems and intelligent power grids, in particular to an intelligent analysis system and method for the influence of multi-scene new energy power generation on a distribution network, and the system comprises a data collection module, a multi-scene modeling module, an influence analysis module, an intelligent evaluation module and an optimization suggestion generation module. And the data acquisition module, the multi-scene modeling module, the influence analysis module, the intelligent evaluation module and the optimization suggestion generation module are in communication connection with an external data bus. The hybrid digital twinborn model is constructed through the multi-scene modeling module, four typical scenes of new energy output fluctuation, extreme weather, AC / DC hybrid access and power grid topology change are dynamically simulated, and dynamic configuration of scene parameters (such as new energy permeability and load growth rate) is supported. According to the model, through fusion modeling (such as a power flow equation, an LSTM time sequence and a graph neural network GNN) driven by a physical mechanism and data, the running state of the distribution network under the multi-scene coupling effect can be reflected more truly.

The invention discloses a tuna pond culture strategy optimization method and system based on meteorological influence analysis, and the method comprises the steps: collecting regional meteorological data and pond culture monitoring data, and constructing a multi-modal pond ecological feature vector; establishing a weather-water body dynamic coupling model by using a space-time diagram neural network, and generating water body situation response prediction information; a breeding regulation and control strategy is made based on the prediction information, and strategy self-adaptive optimization is achieved through real-time monitoring data; evaluating the anti-disaster capability of the tuna after the meteorological influence is finished, screening high-quality germplasm resources by combining genetic analysis, and generating an anti-disaster breeding scheme. According to the method, full-chain optimization from meteorological early warning to culture regulation and control to genetic breeding is achieved, and the climate toughness and economic benefits of tuna pond culture are comprehensively improved.

The invention relates to an intelligent IT asset management system, and the system comprises a configuration asset management module which is used for automatically collecting IT asset information in a network, building a complete configuration item relation model, and supporting the dependency relation mapping and impact analysis between configuration items; the service desk management module is used for establishing a service catalog, defining a processing flow, response time and a service level protocol, automatically carrying out classification and priority evaluation on the received service requests through an intelligent classification engine, and distributing the service requests to proper processing personnel according to a preset rule; the contract supplier management module is used for full-life-cycle management of suppliers and contracts; the IT service financial module is used for multi-dimensional budget management and fine management of IT cost; and the multi-tenant management module is used for realizing entity separation and authority control. The problems of a traditional IT asset management system in the aspects of asset tracking, service management, cost control and the like are solved, and a set of complete IT service management solution is provided for enterprises.

The invention discloses a civil aviation airport ground guarantee resource scheduling method and device and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a real-time flight plan, and constructing a guarantee resource coupling network based on the real-time flight plan and a predefined guarantee flow rule base; performing conflict prediction and conflict influence analysis on the guarantee work of the future flight by traversing the guarantee resource coupling network, and generating a structured conflict early warning report; triggering an on-demand scheduling mechanism of a virtual resource pool for querying and allocating available resources on demand according to the structured conflict early warning report, querying available shared guarantee resources in the virtual resource pool, and generating a conflict resolution scheme which can be directly executed; and executing the conflict resolution scheme, monitoring an execution result through the Internet of Things data, feeding back the execution result to the guarantee resource coupling network, and calibrating a duration parameter in the guarantee resource coupling network. Through the constructed support resource coupling network, the prospective performance and the cooperative robustness of ground support scheduling are improved.

The invention relates to an underground engineering construction design technology, discloses a method for analyzing the influence of underground excavation under-crossing on an existing subway station, and improves the efficiency and precision of analyzing the influence of underground excavation under-crossing construction on the existing subway station. The method comprises the steps that by defining a structure and geometrical shapes and material attributes of a stratum, boundary conditions and external loads are set, grid division is carried out, and a finite element three-dimensional basic calculation model is constructed; according to theories of porous media and the like, a multi-physical field coupling model of a seepage-deformation-temperature full coupling effect is constructed; the rigidity of the underground excavation vertical pile foundation is calculated, and the rigidity of the underground excavation vertical pile foundation is corrected based on the influence factors of the soil squeezing effect and the negative friction resistance; subsurface excavation construction is subdivided into a plurality of stages, structural stress, deformation and stratum displacement are calculated through an incremental method, and the timeliness of a supporting structure and the influence of different excavation modes on stratum disturbance are analyzed; and obtaining a structural response through finite element iteration solution, and judging the influence of underground excavation under-crossing on the existing subway station.

The invention discloses a code change influence analysis method and device, equipment and a storage medium, and relates to the technical field of software, the method comprises the following steps: performing grammar analysis on a multi-programming language code to obtain a back-end abstract syntax tree and a front-end abstract syntax tree, and converting the back-end abstract syntax tree and the front-end abstract syntax tree into a uniform syntax tree structure through a front-end and back-end calling mapping chain; a cross-language dependency graph is constructed according to the unified syntax tree structure, the cross-language dependency graph is composed of nodes and edges, the nodes represent code entities, and the edges represent dependency relations; and when the code change of the bank business is detected, traversing the cross-language dependency graph through a graph traversal algorithm to obtain a code change influence result. Due to the fact that the back-end abstract syntax tree and the front-end abstract syntax tree of the multi-programming language codes are constructed and connected in series, the perfect cross-language dependency graph is constructed, the front-end link and the rear-end link are connected, cross-language tracking can be achieved when the codes of the banking business are changed, and the influence of code change can be accurately analyzed.

The invention relates to the technical field of silver paste preparation, in particular to a silver paste conductivity data modeling and formula optimization system based on machine learning, which comprises a data acquisition and storage module, a preprocessing module, a characteristic influence analysis module, a formula optimization module, a simulation verification module and the like. The method comprises the following steps: acquiring original data of a silver paste formula, preprocessing, calculating influence coefficients of all components on target performance by utilizing a machine learning model, and identifying high and low influence components; the formula optimization module is combined with component content constraints and adopts a multi-objective optimization algorithm to generate candidate formulas; and the simulation verification module verifies the performance of the formula through sintering simulation and process adaptation, and feeds back optimization. According to the invention, the full-process intelligentization of the silver paste formula from data processing to optimization verification is realized, the conductivity and other performances of the silver paste are accurately improved, the research and development cost is reduced, the period is shortened, the suitability of the formula process is enhanced, and the research and development and industrial upgrading of the silver paste are promoted.

The invention discloses a thermal power generating unit load distribution optimization method and system, and relates to the technical field of load optimization distribution, and the method comprises the steps: quantifying the contribution degree of the position of each operation state monitoring device to a load prediction error based on operation influence data, and screening out a representative monitoring point set covering a unit core energy consumption node; constructing a multi-scale influence analysis model, identifying the change influence degree of the operation state data of each representative monitoring point on the load data of the thermal power generating unit, and generating a load distribution initial scheme; running state data of representative monitoring points are monitored in real time, a multi-target optimization mechanism is introduced, the weight value of each target is dynamically adjusted, and the load distribution of the thermal power generating unit is optimized in real time in combination with a load distribution initial scheme. Accurate dynamic load distribution based on multi-scale analysis and multi-objective optimization is realized, and the comprehensive benefit of a unit is improved.

The invention relates to an engineering construction stage user multi-dimensional demand analysis system and method, and the system comprises a multi-modal semantic model construction module which integrates a multi-source knowledge base, and extracts triple description statements from the provisions of the multi-source knowledge base to construct an engineering object ontology knowledge graph; the demand structured guidance and intelligent analysis engine module dynamically identifies an engineering stage and a user role, guides a user to perform text demand input, and performs semantic completion and standardization processing; the demand conflict detection and coordination correction module is used for detecting conflicts in user demands and generating a conflict influence analysis report and a coordination processing scheme; the intelligent drawing and dynamic optimization module is used for automatically drawing according to the triple obtained through demand analysis; and the demand analysis visualization and flow closed-loop module is used for generating an executable parameterized demand document and an association chart, and completing demand-design-verification engineering closed-loop management, so that intelligent analysis and standardization of user multi-dimensional demands are realized, and the system has the advantages of high efficiency, accuracy and intelligence.

The invention discloses an OLED (Organic Light Emitting Diode) production equipment fault diagnosis terminal capable of being remotely monitored, which relates to the technical field of fault diagnosis, and comprises the following steps: acquiring key process parameters of equipment to obtain equipment operation data; performing operation state analysis according to the equipment operation data to obtain an abnormal index; performing fault mode identification according to the abnormal index to obtain an equipment fault type; performing abnormal influence analysis according to the equipment fault type to obtain key influence parameters; performing remote abnormality diagnosis according to the key influence parameters to obtain a diagnosis result; and performing abnormal trend analysis according to the diagnosis result to obtain the equipment health state. Through parameter deviation degree calculation and multi-parameter correlation analysis, the collected key process parameters are subjected to real-time comprehensive analysis, the parameter deviation degree is quantified, coupling abnormity among the parameters is identified, the accuracy and comprehensiveness of fault diagnosis are improved, and the false alarm rate and the missing report rate are reduced.

The invention provides a site pollution remediation technology carbon emission level comprehensive evaluation method coupled with environmental economic impact analysis. The method comprises five links of evaluation boundary determination, carbon emission accounting, environmental impact analysis, remediation cost measurement and calculation, expert score evaluation and the like. The carbon emission accounting adopts an IPCC emission factor method, the environmental influence analysis adopts an LCA ReCiPe 2016 method, and the repair cost calculation comprises construction cost accounting and carbon emission cost accounting. According to the technical scheme, on the basis of calculating the carbon emission of the restoration activity, the restoration efficiency, the restoration cost and the overall environmental impact analysis are introduced, the carbon emission level comprehensive evaluation method which gives consideration to carbon reduction, decontamination, greenness and economy is formed, and the practical guidance effect of the evaluation result is improved. The method can be used for comparing the carbon emission levels of different site pollution remediation technologies, can also be used for evaluating and comparing the carbon emission levels of different construction schemes of the same remediation technology, provides reference for remediation decision making, and has good application prospects.

The invention discloses a temperature and pressure integrated transmitting measurement method and system based on nonlinear compensation, and relates to the technical field of measurement. The method comprises the following steps: synchronously acquiring a temperature original signal and a pressure original signal of a target environment to carry out cross influence analysis, and constructing a temperature-pressure cross influence matrix; performing nonlinear segmented compensation on the temperature original signal and the pressure original signal according to the temperature-pressure cross influence matrix to generate a temperature compensation signal and a pressure compensation signal; and fusing the temperature compensation signal and the pressure compensation signal, generating an initial fusion measurement value, carrying out data change diagnosis, carrying out multi-stage verification according to a diagnosis result, and determining a temperature and pressure integrated transmission measurement value. The technical problem that in the prior art, temperature and pressure signals interfere with each other in the temperature and pressure measurement process, effective compensation and correction cannot be achieved, and consequently measurement is inaccurate is solved, and the technical effect of improving temperature and pressure integrated transmitting measurement precision is achieved.

The invention belongs to the technical field of unmanned system reliability, and provides an unmanned cluster fault mode analysis and transverse and longitudinal propagation analysis method and system based on a task chain, electronic equipment and a storage medium. The method comprises the steps of model construction, fault mode recognition, severity setting, fault transmission recognition, adjacency matrix construction, network model construction and fault transmission influence analysis. According to the method, the unmanned cluster is subjected to hierarchical division, so that the problem of complex calculation process of a physical model is avoided, and the real-time performance of fault mode analysis is improved; through fault mode identification, severity setting and fault transmission identification, analysis of complex cooperation relation and interaction among individuals is realized, and adaptability of a dynamic change environment is improved. Through adjacency matrix construction, network model construction and fault transmission influence analysis, the analysis of the mutual dependency relationship between different fault modes is improved, and the accuracy of the propagation path is improved.

The invention relates to an automatic magnesium oxide coating analysis system and method, and belongs to the technical field of coating quality analysis. The method comprises the following steps: acquiring attributes of a magnesium oxide coating, an infrared spectrum and environment temperature and humidity data, and constructing a multi-source feature set; analyzing the distribution dispersion and local enrichment risk of free water based on a dynamic migration model, and analyzing the bonding weakening degree and dissociation risk of bound water based on a chemical stable state model; respectively generating free water treatment process parameters for inhibiting structural damage and combined water treatment process parameters for preventing performance degradation according to the abnormal analysis result and the coating attribute; by performing coupling influence analysis on the two types of process parameters, the balance requirements of the structural integrity and chemical stability of the coating are coordinated, an optimized process scheme is generated, and cooperative regulation is realized. According to the method, accurate identification, independent analysis and cooperative control of the free water and the bound water are realized, and the accuracy, reliability and quality consistency of coating water treatment are improved.

The invention relates to the technical field of generative AI watermarks, and discloses a domestic credible watermark embedding and tracing method and system based on generative AI output. An acquisition module of the system acquires original content data output by a generative AI, and establishes a trusted watermark configuration database mapped with the original content data; the watermark target setting module configures a watermark embedding target based on the database and covers watermark robustness optimization, watermark invisibility optimization and watermark traceability association optimization; the watermark classification identification module determines target classification identifications, wherein the target classification identifications comprise a main watermark target, an auxiliary watermark target and a reference holding target; after the key watermark parameters are selected by the watermark influence analysis module, the influence analysis of the key parameters on the watermark embedding target is executed; the watermark traceability module establishes a watermark optimization constraint according to the influence analysis result, executes watermark parameter directional embedding, and completes localized credible watermark embedding; and the traceability path construction module generates a traceability path based on the embedding result to obtain a traceability verification result.

The invention provides a mass spectrometer operation state monitoring method, system and device and a medium, and relates to the technical field of fault monitoring, and the method comprises the steps: collecting environment parameters in a mass spectrometer operation environment, carrying out the vacuum degree influence analysis of a mass spectrometer according to the environment parameters, and obtaining vacuum degree influence parameters; analyzing the mass axis offset influence of the mass spectrometer according to the environmental parameters to obtain mass axis offset parameters; testing to obtain an actual vacuum degree influence parameter and an actual mass error parameter of the mass spectrometer, performing state influence verification by combining the vacuum degree influence parameter and the mass axis offset parameter, and processing to obtain a vacuum verification coefficient and a mass verification coefficient; and according to the vacuum verification coefficient and the quality verification coefficient, performing fusion state anomaly analysis on the vacuum degree influence parameter and the quality axis offset parameter to obtain a state anomaly coefficient, and performing fault judgment and early warning. The technical problem that fault early warning is not timely in the monitoring process of the operation state of the mass spectrometer in the prior art is solved.

The invention relates to the technical field of intelligent monitoring management, particularly discloses an intelligent assembly component whole-process management system, and aims to optimize production efficiency and product quality through real-time data acquisition and analysis. The system comprises a data acquisition module which monitors and records the switching time of each process and the quality inspection qualified rate of each component in real time through a sensor and automation equipment on an integrated production line, a production plan evaluation module which calculates a switching time abnormal coefficient, identifies a bottleneck or low-efficiency operation in the process switching time, and evaluates the quality inspection qualified rate of each component according to the bottleneck or low-efficiency operation. And the process switching time is dynamically adjusted based on the specific numerical values of the influence interference factors and the positive and negative characteristics of the influence interference factors, the production quality evaluation module calculates a quality abnormal coefficient according to the quality inspection qualified rate and judges the quality fluctuation condition of each component, and the influence analysis and adjustment module predicts the quality qualified rate by using a gradient enhanced regression model. The process switching time is dynamically adjusted to eliminate potential quality problems.

The invention belongs to the technical field of underwater production system information processing and analysis, and discloses a data-driven underwater production system fault mode and influence analysis system, which comprises four modules: firstly, a historical data collection and storage module is used for collecting, storing and checking historical faults in detail, and outputting a key fault mode; the historical data analysis module is used for determining data analysis indexes according to available data so as to form fault analysis data, specifically, four main data analysis indexes including the occurrence rate, the production loss, the average repair time and the fault interval time are focused on, but in practical application, the occurrence rate, the production loss, the average repair time and the fault interval time are not influenced. Other data analysis indexes can be included according to more comprehensive historical data; thirdly, the data analysis index weight calculation module obtains the weight of the data analysis index by using an objective weight calculation method; and the fault mode sorting and analyzing module is used for carrying out priority sorting on the fault modes by utilizing a multi-criterion decision algorithm, and finally outputting a result and recommending necessary prevention measures.

The invention relates to the technical field of image recognition, and provides an intelligent ash removal method of a fully-closed driving type sweeper. The method comprises the following steps: acquiring a smudginess image of an area to be dedusted; performing visual saliency identification analysis on the dirty image to obtain a dirty block; performing dry-wet discriminant analysis on the multi-dimensional image feature information of the dirty block to obtain a discriminant result; when the judgment result shows dry garbage pollution, introducing a predetermined pollution evaluation interference function to perform interference influence analysis on the initial pollution index to obtain an effective pollution index; and matching a corresponding ash removal plan based on the effective dirt index, and executing an ash removal operation by using the target sweeper. According to the method and the device, the technical problems of poor cleaning strategy adaptability and serious resource waste caused by difficulty in accurately distinguishing the dirt type and quantifying the pollution degree based on a single image recognition technology in a dry and wet mixed garbage scene of the existing cleaning equipment are solved, and the technical effect of improving the matching precision and the execution efficiency of the cleaning plan is achieved.

The invention relates to the technical field of cost prediction, in particular to an operation cost prediction method and system based on artificial intelligence, and the method comprises the following steps: collecting and sorting human resource cost, material cost and energy use cost, creating an independent data set for each cost category, representing nodes through different cost categories, and predicting the cost of each cost category; edges are represented by interaction among cost categories, node weights are determined through cost influence analysis, and a cost factor relation graph is constructed. According to the method, the adaptability and accuracy of cost prediction are effectively improved by constructing the cost factor relation graph and the dependency relation graph and introducing dynamic data processing and time sequence analysis, node data are allowed to be updated in real time to reflect changes of market and operation conditions, and the accuracy of cost prediction is improved through quantitative cost deviation analysis. The difference between prediction and reality can be accurately determined, cost control is finer through screening and trend analysis of key nodes, and resource allocation optimization and budget making in a complex market environment are helped.

The invention belongs to the technical field of semiconductor manufacturing management, and particularly relates to a semiconductor manufacturing-oriented gaseous molecular pollutant online monitoring system, which comprises a small-size traceability dynamic historical influence analysis module, a multi-source real-time monitoring and early warning module, a prospective early warning analysis module, an active control detection module and an intelligent feedback interface, the small-size traceability dynamic historical influence analysis module is based on a hybrid model, historical data and real-time data are combined, the future influence trend of pollutants can be predicted and graded, and therefore enterprises can make coping strategies in advance, reasonably arrange production plans and reduce the influence of the pollutants on the product yield. According to the method, pollutant diffusion paths can be simulated, high-risk stations can be positioned, pollutant sources and propagation paths are analyzed through the Bayesian network, and a root cause probability sorting list is output, so that a basis is conveniently provided for prevention, control and treatment of pollutants, and targeted measures can be quickly and accurately taken.

The invention relates to the technical field of risk assessment based on big data, and provides a post-disaster regional operation risk assessment method based on a Beidou technology, and the method comprises the steps: building an impact analysis model based on disaster types through multi-dimensional information, and combining with the condition of a target work site, so as to assess the risk of post-disaster field operation. In order to achieve the purpose, the post-disaster regional operation risk assessment method based on the Beidou technology is provided and comprises the steps that daily multi-source data collection and standardization processing are carried out on a target region, and indoor and outdoor scene dynamic judgment models are established respectively for risk index calculation; and after the disaster occurs, detecting the target area again, comparing the detection result with the comprehensive risk index, and accurately evaluating the risk again. According to the method, after-calamity risk assessment is converted from fuzzy judgment to quantitative accuracy, operation preparation is converted from experience driving to data driving, and direction guidance is provided for after-calamity safe and efficient operation in the power industry through risk assessment.

The invention belongs to the field of bioinformatics, and relates to a protein post-translational modification prediction method based on multi-modal deep learning. The method comprises the following steps: firstly, performing multi-modal feature extraction by inputting a protein sequence and three-dimensional structure data to obtain a sequence feature vector and a structure feature vector; secondly, carrying out feature fusion by adopting a cross-modal attention mechanism and a self-adaptive gating network; then, combining the fusion features with the disease type information, and performing fine adjustment on the prediction probability through a disease specific coding network; then, using a multi-task learning framework to predict the locus probabilities of various protein post-translational modification types in parallel; finally, feature importance is calculated through a gradient back propagation technology, and a comprehensive report is output in combination with variation influence analysis. According to the method, high-precision and explainable protein post-translational modification prediction with disease perception capability is realized, and an important calculation and analysis tool is provided for revealing a disease molecular mechanism and finding accurate drug targets.