161 results about "Dynamic database" patented technology

The invention discloses a JDK8-based heterogeneous service large model dynamic adaptation and intelligent scheduling method and system, and belongs to the technical field of artificial intelligence service integration, and the method comprises a unified service module which provides standardized HTTP / WebSocket access for an application layer and shields the difference of multiple platforms at the bottom layer; the dynamic routing module is used for dynamically selecting service nodes based on service quality indexes and supporting weight calculation, fusing recovery and flow dyeing; the service execution module is used for loading a platform adapter through a dual-mode service factory, integrating a tool calling function and realizing protocol conversion and authentication injection; and the configuration management module supports dual-configuration source hot loading of the static file and the dynamic database and provides versioning rollback and consistency verification. According to the method, the problems of multi-platform protocol difference, service rigid expansion, tool calling coupling and the like can be solved, the complexity of multi-platform management is remarkably reduced, and the flexibility and maintainability of the system are improved.

The invention provides an automatic pavement settlement monitoring method and system based on a shield undercrossing airport runway. The dynamic database is constructed by collecting operation parameters such as shield tunneling machine propelling pressure, cutterhead torque and synchronous grouting pressure in real time and combining three-dimensional deformation data obtained through laser scanning. Strain sensing units are arranged in the soil layers to form a layered monitoring network, strain changes of the soil layers of different depths are collected, and spatial correlation is established between the strain changes and the tunneling position. And analyzing a spatial attenuation rule through a dynamic association processing unit, and generating a multi-dimensional feature data set. And establishing a settlement prediction model, fusing the shield dynamic load sequence and the layered strain conduction characteristics, and outputting settlement distribution information. And the system automatically compares the settlement difference value of the adjacent areas with a preset safety threshold value, and generates a graded early warning signal after verification of the three-dimensional deformation data space. According to the technical scheme provided by the invention, real-time three-dimensional prediction and spatial verification type graded early warning of settlement of the airport runway are realized.

The invention discloses a multi-channel meteorological risk early warning information self-adaptive targeted publishing system and method based on low-altitude flight, and solves the problems that existing low-altitude meteorological early warning is low in temporal-spatial resolution, single in risk identification and insufficient in information pertinence. The system integrates air-based, space-based, foundation, social and topographic data, and generates a low-altitude meteorological dynamic database through fusion; micro-scale risks such as wind shear are extracted and graded through an AI model, a digital twin simulation response is constructed in combination with the state of the aircraft, and a risk grid model and coordinates are output; calculating a comprehensive risk index, generating evasion guidance, converting into multi-role early warning information, generating a three-dimensional thermodynamic diagram, and finally adaptively selecting a publishing channel according to user roles, terminals and network states. According to the invention, the accuracy and timeliness of low-altitude flight meteorological risk early warning are improved, and the safety of low-altitude flight is guaranteed.

The invention belongs to the technical field of geological information processing, and discloses a geological mineral exploration data extraction system which comprises the following steps: constructing an air-ground-hole three-in-one acquisition network, and respectively acquiring earth surface mineral spectrum data, seismic longitudinal wave and transverse wave reflection data and rock core main quantity and trace element composition data; processing the collected data to form a five-dimensional data cube containing space coordinates, spectrum parameters, physical property parameters, element parameters and time indexes, performing physical layer attribute extraction on the five-dimensional data cube, performing analysis to obtain a wave impedance value and a lithology category probability array, performing semantic layer attribute extraction on the five-dimensional data cube, and performing semantic layer attribute extraction on the five-dimensional data cube; an alteration type probability array is obtained through analysis, an alteration zoning graph layer is generated, the mineralization potential index and a five-dimensional data cube are fused, a four-dimensional dynamic database is constructed, a mineralization potential prediction graph layer and a drilling priority graph are generated based on the four-dimensional dynamic database, and therefore the decision-making level of ore body drilling deployment is improved.

The invention relates to the technical field of power equipment state detection, in particular to an ultrahigh-frequency partial discharge online detection system, method and device and a medium, and the system comprises the steps: collecting an initial discharge signal in real time through an ultrahigh-frequency sensor array, and carrying out the preprocessing of the initial discharge signal, so as to obtain an ultrahigh-frequency discharge signal; carrying out peak detection on the ultrahigh-frequency discharge signal, and triggering a high-speed analog-to-digital converter to collect an original waveform when the amplitude exceeds a preset threshold value; performing multi-dimensional feature extraction on the original waveform by using a digital signal processor to obtain multiple groups of dimensional features; identifying and classifying the multiple groups of dimension features based on a random forest algorithm, generating discharge type labels and confidence coefficients, and storing the discharge type labels and the confidence coefficients in a dynamic database; carrying out spatial position calculation on the ultrahigh-frequency discharge signal by adopting a time difference method, and determining a three-dimensional coordinate of a discharge source; the dynamic database and the three-dimensional coordinates of the discharge source are subjected to space-time correlation and multi-dimensional analysis, a defect analysis result is generated, and high-precision online detection of the partial discharge defect of the high-voltage equipment is achieved.

The invention discloses a power grid energy storage capacity demand determination method and system based on multiple time scales, relates to the technical field of power grid energy storage capacity demand calculation, and aims to solve the problem of inaccurate energy storage capacity demand calculation. By generating multiple scenes, quantitatively screening key scenes and incorporating various uncertain factors, energy storage capacity calculation focuses on high-influence scenes, the coping capacity of the scheme to actual risks is enhanced, decision scientificity is improved, ultra-short-term to long-term multi-time scales are divided, core contradictions of all the scales are captured in a targeted mode, limitation of a single scale is avoided, and energy storage capacity calculation efficiency is improved. According to the method, capacity requirements and equipment distribution are integrated, operation rules are defined, visual documents are generated, multi-scale energy storage cooperative operation is achieved, the stability of a power grid is guaranteed, meanwhile, cost is reduced, scheme landing performance and operation efficiency are improved, multi-time-scale power grid state prediction is carried out based on a dynamic database, and historical rules and real-time data are combined, so that the power grid state prediction efficiency is improved. And the prediction coordination is ensured through multi-dimensional verification.

The invention discloses a pupil image recognition and disease change evaluation system based on image recognition, and relates to the technical field of monitoring analysis, and the system comprises the steps: obtaining the identity information and historical pupil data of a patient, and building a pupil dynamic database corresponding to the patient; alternately irradiating the eyes of the patient, and collecting a dynamic image sequence corresponding to a pupil light reflex area of the patient; performing spatial-temporal characteristic analysis on the dynamic image sequence, establishing a pupil diameter dynamic change curve, and generating a light reflection sensitivity index of the pupil of the patient according to the pupil diameter dynamic change curve; constructing a pupil dynamic evaluation model according to the pupil diameter dynamic change curve and the light reflex sensitivity index of the patient pupil, and outputting a pupil grade evaluation result based on the pupil dynamic evaluation model; comprehensive analysis is conducted on pupil grade evaluation results, analysis results are uploaded to an intensive care central system, and a grading early warning mechanism is triggered. The application has the effect of reducing the occurrence of illness state delay.

The invention discloses an engineering safety early warning method and system based on artificial intelligence real-time risk identification, and relates to the technical field of engineering safety, and the method comprises the steps: collecting scattered engineering safety data from each engineering platform in batches, and carrying out the preprocessing of the data, and constructing a dynamic database; according to the engineering safety data collected in batches, an engineering safety knowledge graph is constructed, and different risk levels are preset according to engineering safety standards. According to the method, multi-source engineering safety data are integrated, dynamically changing risk characteristics are analyzed in real time by using an AI risk identification model, the hysteresis of traditional manual inspection and static analysis is overcome, a nonlinear relationship among the risk characteristics is captured by using a random forest model through integrated learning of multiple decision trees, and the risk characteristics are analyzed in real time. And the probability values of high, medium and low risk levels are output in combination with Softmax probability normalization, so that the evaluation precision is remarkably improved, the risk features are positioned, the scientificity of risk traceability is ensured, and data-driven decision support is provided for engineering safety management.

The invention belongs to the technical field of aircraft coating processing, and particularly relates to an aircraft surface automatic spraying method based on a heavy-load robot, which comprises the following steps: carrying out geometric modeling on an aircraft body workpiece; a spray gun tool coordinate system is calibrated, and TCP coordinate optimization calculation is carried out by fusing spray gun 3D model parameters and flame long distance values; constructing a global workpiece coordinate system dynamic database; automatic coverage of a multi-layer U-shaped reciprocating spraying path is achieved through a path planning algorithm; after a fixed spraying distance parameter is set, the spraying software process package generates a robot track, and path simulation is carried out; a robot movement track program is generated by combining parameters of coating types, plasma flame lengths, spraying angles and spraying speeds; and a low-speed transition point is inserted into a track alarm point, and dynamic smooth connection of a spraying path is achieved. The spraying efficiency of the large machine body workpiece is improved, and manual intervention is reduced; and the adaptability is high, and spraying objects of different shapes and sizes can be rapidly dealt with.

Techniques for dynamic database redaction using protected encryption secret material are described. A masking policy is defined that includes a reference to a secret material stored by a secrets manager service. The masking policy further identifies a pseudonymous redaction function that utilizes a cryptographic function requiring such a secret material. The secrets manager service is configured to grant access to the secret material by an entity of the database service that executes queries, such as a leader node of a cluster. For a particular query, the cluster obtains the secret material from the secrets manager service in a secure manner, uses the secret material for applying the cryptographic function to values for redaction purposes, and deletes any copies of secret material thereafter.

The invention relates to a multi-agent search enhancement generation method and system. The method comprises the following steps: analyzing a task instruction through a first agent to generate a search feature set; matching a plurality of corresponding second intelligent agents and performing directional semantic conversion on the original semantic vector to generate a query statement set; screening a plurality of third intelligent agents, and executing query operation through the third intelligent agents to obtain a preliminary retrieval set; inputting the preliminary retrieval set and the retrieval feature set into a fourth agent for verification, and generating an optimized retrieval result; in conclusion, according to the multi-agent search enhancement generation method and system provided by the invention, intelligent analysis, cross-modal semantic conversion, dynamic database partition scheduling and search result optimization verification of task instructions are realized through a multi-agent cooperation mechanism; the method has the effects of improving the retrieval precision and efficiency, enhancing the adaptability to complex query intentions and optimizing the collaborative retrieval capability of the heterogeneous database.

The invention provides an intelligent infusion system and an intelligent liquid control method based on database dynamic feedback regulation and control, and relates to the technical field of medical equipment intelligent control. The system comprises an infusion terminal execution module, a multi-parameter monitoring module, a data transmission module, a central control server module and a dynamic database module; patient information, an infusion scheme, a threshold range and multi-dimensional data of historical records are stored through the dynamic database module, the central control server module calls the data and compares the data with real-time physiological parameters and infusion state parameters collected by the multi-parameter monitoring module for analysis, and the target infusion speed is calculated. Early warning is carried out according to the target infusion speed and the real-time monitoring parameters, accurate monitoring of the regulation and control parameters and the individual condition of the patient is ensured, the infusion speed is always in the optimal range adaptive to the physiological state of the patient, and the infusion adverse reaction caused by parameter errors and exceeding the physiological range is avoided.

The invention relates to a marine ranching digital construction method and system based on artificial intelligence, and the method comprises the steps: monitoring plankton in a marine ranching in real time, obtaining a water body image through an image recognition technology, and building a dynamic database, so as to regularly store and update the related data of the plankton. Based on monitoring data, a water quality evaluation model is established, and the correlation among plankton species, density and water quality parameters is analyzed. Factors such as water flow velocity and flow direction are integrated, a flow change model is established, and the influence of water flow on plankton is judged. A primary culture optimization scheme is formulated by simulating the interaction between cultured organisms and planktons, and the culture trend is predicted in real time and the management strategy is adjusted by integrating the water quality evaluation and the output result of the flow change model. According to the scheme, the intelligent management level of the marine ranching is effectively improved, dynamic and scientific management of the culture environment is achieved, powerful support is provided for healthy growth of cultured organisms, and sustainable development of the marine ranching is promoted.

The invention discloses an aircraft control simulation platform construction method, and the method comprises the steps: constructing a terrain multi-dimensional model, fusing a cross-scale meteorological particle system, and dynamically obtaining an environment disturbance factor; a space obstacle dynamic database is generated in combination with millimeter-level space positioning data, and virtual collision detection is carried out; performing eye movement-gesture collaborative decision, and identifying a control instruction with high confidence; the six-degree-of-freedom kinetic equation is coupled with an unreal engine interface in real time, and a high-fidelity instrument is driven to feed back; generating a training defect thermodynamic diagram, triggering an adaptive optimization strategy and iterating platform parameters; the invention provides a solution integrating multi-modal high-precision data acquisition, dynamic environment-physical rule coupling, intelligent decision and closed-loop optimization so as to realize aircraft control simulation training with high immersion degree, high fidelity and high adaptability.

The invention relates to the field of ecological monitoring, in particular to a marine ecological anomaly detection method and system based on multi-source data. Comprising the following steps: acquiring multi-source dynamic data of a target sea area; constructing a dynamic quality evaluation system, inputting multi-source dynamic data, and outputting a quality control report; based on the multi-source dynamic data conforming to the preset quality threshold value, establishing a marine ecological dynamic database in combination with a spatio-temporal indexing mechanism; based on the marine ecological dynamic database, space-time anomaly features are extracted, and candidate anomaly regions are determined; and generating a marine ecology comprehensive abnormal index by adopting the dynamic weight calculation model, outputting a prediction result through a preset abnormal trend prediction model based on the marine ecology comprehensive abnormal index, and generating an early warning measure. Through a distributed crawler and sensor gateway collaborative architecture, comprehensive acquisition of macroscopic data and near-shore real-time data is realized, and high-quality basic data is provided for subsequent detection.

The invention belongs to the technical field of data processing, and discloses a multi-source heterogeneous data fused remote sensing map dynamic database construction method and system, and the method comprises the steps: firstly obtaining multi-source heterogeneous remote sensing data of a target area, extracting remote sensing time sequence data of a land parcel after preprocessing, and obtaining a remote sensing time sequence; constructing a time sequence semantic track containing a key semantic feature time sequence for each land parcel; identifying a semantic evolution trend by using a semantic drift detection model, and generating an abandoned suspicious score; the static factors, the dynamic factors and the prior knowledge of the land parcels are fused, a structure causal graph is constructed by means of a causal discovery algorithm, and abandoned causal credibility is reasoned based on a graph neural network model; and automatically generating abandoned land labels for the land parcels of which the double scores exceed the threshold value, and finally writing the land parcel information into a dynamic database. And the problems of construction lag and information incompleteness of the dynamic database are solved.

The invention relates to the technical field of intelligent warehousing, in particular to a double-extension-position stereoscopic warehouse scheduling method and system and a computer readable storage medium, and provides a double-extension-position stereoscopic warehouse scheduling method which comprises the steps that a to-be-executed task dynamic database is generated according to real-time to-be-executed tasks of a double-extension-position stereoscopic warehouse; establishing a storage location-task dynamic mapping relation based on a preset three-dimensional model of the double-extension-location stereoscopic warehouse and a to-be-executed task dynamic database; obtaining an initial path plan based on the storage location-task dynamic mapping relation; calculating a task conflict index according to the equipment parameters of the double-extension-position stereoscopic warehouse and the to-be-executed task dynamic database; and adjusting the initial path plan based on the task conflict index to obtain a scheduling scheme. According to the double-extension-position stereoscopic warehouse scheduling method, the technical problem of efficiency loss caused by insufficient dynamic scheduling capability of a double-extension-position stereoscopic warehouse scheduling system in related technologies is solved.

The embodiment of the invention provides a reservoir capacity dynamic monitoring method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining a reservoir surface feature data set through the unmanned aerial vehicle, determining the boundary of a water area, scanning the bottom of the reservoir, obtaining the underwater terrain elevation and sediment distribution data, and constructing a three-dimensional terrain model; and determining terrain and deposit change data based on the three-dimensional terrain model dynamic change data of the continuous time sequence. Sedimentation space distribution characteristics are obtained through change rate analysis, and the sediment transportation amount and the sedimentation and deposition space rule are determined in combination with the water flow speed. Key driving factors are screened through correlation analysis, a sedimentation high-risk area is determined according to the key driving factors, and the change of sediments in the area is monitored, so that the change of the reservoir capacity is mastered. The reservoir siltation can be accurately and dynamically monitored, the high-risk area and the change mode are defined, the dynamic database is constructed to update the capacity change in real time, and important technical support is provided for reservoir management and flood control and disaster reduction.

The invention discloses a site-level CCUS dynamic source-sink matching optimization method for an oil field enterprise, relates to the technical field of large-scale deployment of carbon capture, utilization and storage of the oil field enterprise, and particularly relates to the site-level CCUS dynamic source-sink matching optimization method for the oil field enterprise. Comprising the following steps: integrating a carbon source end full life cycle technical economy evaluation system and a storage target area'geology-potential-economy 'three-dimensional grading model to form a dynamic database; on the basis of the dynamic database, a mixed integer linear programming model fusing source sink dynamic priority coefficients is constructed, three scenes of cost minimization, oil displacement income maximization and carbon sink subsidy excitation are set, and a constraint system is coupled; according to the method, carbon source technology economic evaluation and storage target area three-dimensional grading are fused, the specific injection-production cycle, policy incentive and pipe network constraint of an oil field are converted into time-varying weight coefficients, traditional static optimization limitation is broken through, and site-level source-sink dynamic accurate matching is achieved.

The invention discloses a time-sharing coal blending combustion method, device and equipment for a coal-fired power plant and a storage medium, and relates to the technical field of coal blending control, and the method comprises the following steps: establishing a dynamic database containing coal reserves and coal quality parameters of the coal-fired power plant, and updating coal characteristic data in real time; dividing the whole day into a plurality of time periods according to a power grid dispatching instruction and historical load data, and setting a reference load range for each time period; and by taking the fire coal characteristic data and the reference load range as constraint conditions and taking the maximum fire coal profit as a target function, generating a fire coal blending combustion scheme of the coal mill in each time period. According to the method, coal quality parameters and inventory data are integrated in real time by constructing a dynamic database, time periods are intelligently divided and a load reference is set in combination with a power grid dispatching instruction, and a coal blending combustion scheme of each time period is generated by using a mathematical optimization algorithm; the problems of poor coal quality fluctuation adaptation, lagging load adjustment, high fuel cost, difficulty in emission control and the like of a traditional artificial experience method are effectively solved.

The invention discloses a self-adaptive control method and system for a mining drilling machine. The method comprises the steps that state parameter data in the drilling process of the drilling machine are collected in real time through a multi-source sensor; quickly matching the acquired data with a pre-constructed dynamic database, generating an initial control parameter, and inputting the initial control parameter into an optimization model to generate a self-adaptive control strategy; performing closed-loop control on a drilling machine executing mechanism according to the self-adaptive control strategy; and monitoring and evaluating the drilling performance index of the drilling machine in real time, and feeding back an evaluation result to the optimization model to realize online iterative optimization. Through three technical means of dynamic database construction, layered double closed-loop control and multi-parameter coupling optimization, adaptive sensing, intelligent decision making and accurate execution of the drilling machine under complex geological conditions are realized, the drilling efficiency is remarkably improved, the energy consumption is reduced, the operation safety is enhanced, and the engineering application value is extremely high.

The invention relates to the technical field of boiler operation management, in particular to a boiler soot blowing steam consumption multi-dimensional data analysis system which comprises a dynamic database construction module, a steam consumption calculation model construction module, an evaluation module and an optimization strategy generation module. The dynamic database construction module is used for cooperatively acquiring the ash scale thickness value of the heating surface partition of the boiler through an infrared thermal imager and a sound wave thickness measuring device, and realizing structured time sequence data fusion in combination with steam parameters and working condition parameters; the steam consumption calculation model building module is used for building a multi-dimensional consumption data set based on space, time and energy efficiency dimensions; the evaluation module outputs a unit ash scale removal steam consumption value, an invalid steam injection proportion value and a regional energy efficiency deviation coefficient value; the optimization strategy generation module generates a soot blowing pressure gradient adjustment strategy and a steam saturation temperature compensation strategy through index correlation analysis, fine evaluation and control optimization of soot blowing energy efficiency can be achieved, and the boiler operation efficiency and the steam utilization rate are improved.

The invention discloses an air travel plan and service customization system, and particularly relates to the technical field of big data, the air travel plan and service customization system comprises a user terminal module, an intelligent travel planning engine and a service integration platform, and the user terminal module is connected to the intelligent travel planning engine and the service integration platform through the Internet. A travel demand and a current position of a user are obtained through a travel preference input module and a positioning module of a user terminal module, a dynamic scheme is generated in combination with a flight dynamic database, a hotel resource pool and a scenic spot ticket API under an intelligent travel planning engine, and after the user confirms a travel, the travel demand and the current position of the user are obtained. The service integration platform synchronously books air tickets and hotels and generates electronic certificates, when flights are delayed, alternative traffic scheme calculation is automatically triggered, travel nodes are dynamically connected to map navigation service, and the travel state of a user is updated in real time.

The invention relates to the technical field of data processing, and discloses a low-code business data storage method and system based on an EAV model, and the method comprises the steps: carrying out the data model transformation of a common data type for the EAV model of each entity in a database, and carrying out the data model transformation when a user carries out the interaction of a target entity, according to the method, the corresponding data model can be called to realize rapid data query, the problem of low query efficiency caused by data format conversion is avoided, meanwhile, load limitation brought to system memory and CPU resources by performing data model transformation on each entity in each operation unit time period is considered as a constraint condition, and maximization of the query efficiency is taken as a target. The most reasonable data model transformation and updating of each entity are executed for each operation unit time period, the data model management efficiency of the database is improved, the data query efficiency is improved while excessive resources of the system are not occupied, and a dynamic database model capable of realizing low-code development flexibility and expandable service attributes is provided.

The invention relates to the technical field of bioinformatics, in particular to an analysis method of pathogen metagenome sequencing data, the host background is greatly reduced through two-stage host filtering (especially ML fine filtering), and the detection capacity of low-abundance pathogens is greatly improved and the false negative rate is reduced by combining a low-abundance enhancement module (UMI / denoising); the dynamic database integration ensures that the coverage is wide and newest; a priority ranking algorithm is combined with clinical metadata, so that the ranking of real pathogenic bacteria is effectively improved, and the interference of non-pathogenic bacteria / contaminating bacteria is inhibited; the confidence evaluation of the low-abundance module reduces false positive; dynamically updated databases and intelligent sequencing effectively cope with the problems of new pathogens and incomplete databases, and missing detection is reduced; false detection is reduced through strict denoising, confidence evaluation and interpretation based on clinical rules.

The invention relates to the technical field of production data processing, in particular to a printed circuit board production quality tracking method and system, and the method comprises the following steps: setting a quality detection point at each key production node of PCB production, carrying out the detection at each quality detection point, and collecting the quality data of a PCB in real time; analyzing the collected data by using a machine learning algorithm, and identifying the quality deviation of each key production node; a dynamic database is established, and production process data and quality detection results of each PCB are recorded in real time and used for tracking and analyzing the overall production quality; and judging whether the production node is an accidental fault or an actual fault based on a quality detection result in the dynamic database. According to the method, the quality problem in the production process can be quickly and accurately identified, and the uncertainty and risk of relying on later detection are greatly reduced.

The invention belongs to the field of big data, and particularly relates to a case detection optimization method based on big data. Acquiring case information for data preprocessing, and summarizing the data to a dynamic database in real time; identifying and associating data in the dynamic database, and establishing an index database with association logic; automatically constructing an evidence chain through an inference model and an index database; logic detection is carried out on the evidence chain, the evidence chain with strong logic association of time, figures and material evidences is reserved as a reference evidence chain, and abnormal points of the reference evidence chain are marked; and performing analysis and prediction based on abnormal point marks of the reference evidence chain, and giving at least one prediction direction to guide case detection. Data summarization is carried out through a big data technology, associated data is rapidly extracted through an index database, multiple evidence chains are automatically constructed for screening, investigation direction expansion is carried out through mark analysis and prediction of abnormal points, and the case solving efficiency is improved through data driving.

A distributed photovoltaic grid-connected point electric energy quality dynamic monitoring system relates to the field of distributed photovoltaic grid-connected points, and comprises a knowledge graph construction module used for constructing a dynamic knowledge graph based on a dynamic database and updating the dynamic knowledge graph by using real-time data and integrated dynamic knowledge; the causal analysis module is used for establishing a causal relationship model of the power quality problem based on the dynamic knowledge graph; and the backtracking and positioning module is used for performing causal chain backtracking based on the causal relationship model, positioning a root cause influencing the power quality and generating an analysis report. According to the method, the causal chain backtracking is carried out along the weighted directed acyclic graph by taking the abnormal index as a starting point based on the causal relationship model, so that the root cause node, such as a specific equipment fault or health deterioration, which causes the power quality problem can be quickly and automatically positioned, and the automation degree and the positioning precision of fault diagnosis are greatly improved.

The invention provides an unmanned cargo automatic handover system based on multi-mode biological recognition. The unmanned cargo automatic handover system comprises a biological recognition device, an identity verification module, a cargo handover control device and a cloud management platform. The biological recognition device integrates a fingerprint recognition module, a face recognition module and an iris recognition module, and high-accuracy identity recognition is ensured by using advanced sensors and algorithms. The identity verification module guarantees the safety of the verification process through real-time comparison and multi-factor verification in combination with dynamic database management. The cargo handover control device automatically controls unlocking and locking of a cargo cabin door, and records, encrypts and stores all handover operation data at the same time. According to the technical scheme, high safety and high efficiency of cargo handover of the logistics vehicle are achieved, the method is suitable for being widely applied to the modern logistics field, the safety and accuracy of the logistics vehicle in the cargo handover process are improved, and automation and traceability of operation are achieved.

The invention relates to the technical field of emergency command and dispatch, in particular to a dual-mode-based vehicle-mounted emergency dispatch method, system, device and medium, after a vehicle is started, a vehicle-mounted terminal carries out power-on self-test, after power-on self-test is passed, registration is carried out, vehicle data are collected, and the vehicle data are packaged and uploaded through dual-mode switching; the method comprises the steps of analyzing and processing packaged and uploaded vehicle data, storing the vehicle data in a dynamic database, detecting the vehicle data in the database, giving an alarm prompt for abnormal data, issuing an instruction according to the alarm prompt, executing a task according to the instruction after the instruction is confirmed, feeding back confirmation information, and meanwhile, carrying out continuous detection through dual-mode switching. The problem of failure of single-mode communication under the scene of base station damage or satellite bandwidth limitation is solved, the communication interruption rate is reduced to below 5%, high-speed transmission of large-capacity data is guaranteed, the Beidou guarantees key information basic communication, the bandwidth requirement and the global coverage are both considered, and the scheduling real-time performance and the environmental adaptability are enhanced.