2785 results about "Data accuracy" patented technology

The invention discloses an intelligent control system for a current transformer, and particularly relates to the technical field of power system monitoring, which comprises a fluctuation identification unit, a redundancy acquisition unit, an error fitting unit, a protection delay unit and a fault response unit, realizes real-time identification and classification of current fluctuation, and has standby path high-speed sampling and fusion signal construction capabilities. The fluctuation trend is judged through error regression, a protection strategy is dynamically set, response is delayed to avoid misoperation, finally, a high-level protection instruction is triggered and a fault event is output when offset overruns or alignment fails, and the data accuracy and the system response stability in the power supply switching process are guaranteed; according to the method, accurate identification of current fluctuation is realized through multi-dimensional feature extraction, data reliability is enhanced by using redundant sampling and error fitting, and by combining delay control and a hierarchical response mechanism, the risk of misoperation is effectively reduced, the fault judgment accuracy and the system protection stability are improved, and the method is suitable for a complex power supply switching scene.

The invention discloses a hybrid energy storage system optimization scheduling method based on AI intelligent regulation and control, and relates to the technical field of hybrid energy storage, and the method comprises the following steps: collecting real-time data, and carrying out the cross verification of data consistency through a multi-source data fusion technology; and identifying data abnormity caused by sensor faults, communication delay and environmental interference by combining adaptive threshold detection with a statistical analysis method, and eliminating abnormal data. According to the method, energy storage scheduling is optimized through data cleaning, time sequence prediction and reinforcement learning, and the intelligent management and dynamic adaptive capacity is improved. Multi-source data fusion and anomaly detection are adopted to ensure data accuracy, energy consumption is predicted by means of LSTM and Transform, and an energy storage strategy is optimized in advance. By combining reinforcement learning and system dynamic adjustment charging and discharging, the photovoltaic consumption rate is improved, the electricity purchasing cost is reduced, the SOC is intelligently controlled to be 40%-80%, and the service life of the battery is prolonged. Meanwhile, the system stability is improved through anomaly detection and correction, and the maintenance cost is reduced.

The invention belongs to the technical field of power system monitoring, and particularly relates to a wireless cable temperature measurement system and method, and the system comprises an operation information collection module, a layout planning analysis module, a data standardization module, a change trend analysis module, an early warning threshold adjustment module, and a fault positioning analysis module. According to the invention, cable operation data and environment information are analyzed, the coverage range, data transmission delay and signal attenuation are considered, the layout of temperature sensors is optimized, the response speed and data accuracy of a temperature sensor network are enhanced, temperature data are formatted and processed, time sequence analysis is carried out, and the temperature change trend is evaluated. Abnormal temperature risk recognition is achieved in the early stage, the accuracy of fault prediction is improved, the cable temperature risk monitoring threshold value is adjusted in combination with cable specifications and meteorological information, the timeliness and effectiveness of risk management are improved, the fault response speed and the system stability are improved, the maintenance cost is reduced, and the reliability and safety of a power grid are improved.

The invention relates to the technical field of data processing, provides a multi-node carbon emission intelligent contract verification method based on a block chain, and aims to solve the problems that data verification in an existing carbon asset transaction system is non-transparent, high in trust cost and difficult to adapt to policy changes. Distributed storage and dynamic verification of data are realized by constructing a hierarchical block chain architecture, the data are collected by using edge computing nodes, and third-party verification data are obtained through an oracle machine node cluster. The dynamic rule engine monitors data changes in real time, triggers a multi-stage verification process, and improves the data accuracy. And meanwhile, an improved Byzantine fault-tolerant mechanism is adopted to reach a consensus, and the system has the functions of strategy dynamic updating and security enhancement, so that the flexibility and security of the system are improved. According to the method, multi-party consensus and real-time verification of the carbon emission data are realized, trust cost of cooperation among enterprises is reduced, and healthy development of a carbon emission permit trading market is promoted.

The invention belongs to the technical field of data detection, and particularly relates to an efficient data anomaly automatic detection and alarm system. The detection and alarm system comprises a sensor and data acquisition device, a data processing and analysis module, an alarm module, a monitoring center and visual interface, a communication and network module, a storage and backup module, an intelligent decision support module and a user feedback and adjustment module. According to the system, data can be monitored and analyzed in real time, abnormity can be quickly found, an alarm is given, manual monitoring requirements are reduced, data misjudgment is avoided, data accuracy is ensured through data cleaning and processing, an intelligent decision support module provides optimization suggestions based on AI, and a user can adjust rules to adapt to requirements. The system has the functions of data processing, anomaly recognition and trend prediction, the monitoring center visually displays the state, the storage and backup module ensures long-term storage of data, the automatic mechanism improves the response speed and the operation benefits, and the adaptability is high.

The invention discloses an intelligent robot control method based on multi-sensor cooperation, and relates to the field of intelligent robots, and the method comprises the following operation steps: S1, carrying out the architecture deployment of sensors; s2, establishing communication; s3, edge intelligent calculation enhancement; s4, a dynamic adaptive fusion strategy; s5, carrying out group intelligent decision making; and S6, carrying out sensor role dynamic conversion. According to the intelligent robot control method based on multi-sensor cooperation, the sensor data credibility can be adjusted in real time according to the environment condition, the fusion weight can be dynamically allocated, the data accuracy is ensured through the cross check of the adjacent sensor data, and meanwhile, the intelligent robot control method based on multi-sensor cooperation can be realized through the ant colony cooperation algorithm in combination with the self-adaptive pheromone volatilization regulation and control. The path exploration strategy can be flexibly adjusted according to task complexity and environment dynamic change, the search efficiency can be effectively improved, various environments can be more quickly adapted, and the optimal action path can be quickly found.

The invention relates to the technical field of pile foundation monitoring, and discloses a pile foundation state real-time monitoring and diagnosis system based on a digital twinborn technology. The system comprises a multi-source data acquisition module, a data confidence evaluation module and an acoustic emission monitoring decision module. The multi-source data acquisition module comprises a plurality of sensor groups deployed at different depths of a pile foundation, each group comprises a strain sensor, an acceleration sensor, an acoustic emission sensor and a temperature sensor, and pile foundation data can be acquired in multiple dimensions; the data confidence evaluation module receives original data, generates a correction data sequence through time sequence noise separation and reconstruction, and calculates data confidence according to correction data distribution dispersion; the acoustic emission monitoring decision module judges whether acoustic emission monitoring is started or not according to the data confidence coefficient, and controls the acoustic emission sensor array at the top of the pile foundation to collect acoustic emission signals during starting. The system can comprehensively obtain pile foundation data, improve data accuracy, achieve early damage recognition and guarantee pile foundation safety.

The invention discloses a natural resource engineering monitoring method and system based on cloud computing, and relates to the technical field of engineering monitoring. A cloud access layer is constructed, a standardized monitoring data set is formed, a monitoring quality abnormity label set is established, and an engineering state vector set is generated based on a space-time alignment monitoring data set; according to the method, standardization and unified management of multi-source monitoring data are achieved through cloud access, the data accuracy is guaranteed in combination with time synchronization, space mapping and a quality label mechanism, the method constructs an engineering state profile and generates consistent snapshots, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. Historical comparison and tracing are supported, a risk event set and a risk evidence chain are generated through causal atlas inference, risk full-chain tracing is achieved, a scheduling instruction is further generated and combined with an execution receipt to form closed-loop management, differential evaluation and dynamic adjustment are supported, and therefore linkage and continuous optimization of monitoring, risk identification and engineering scheduling are achieved.

Computer-implemented methods and systems are disclosed for Information Technology Service Management (ITSM). The pioneering AI-driven system revolutionizes IT service management by uniquely validating, suggesting, and inferencing incident and problem data. At its core are cutting-edge generative AI techniques like GANs and LLMs, requiring intricate training and iterative refinement on varied data sets, showcasing a depth of expertise in database structures and AI concepts. It bridges critical gaps in incident resolution and classification through cognitive computing, AI, NLP, and deep learning, applied to both historical and current data. The system comprises modules for Incident Validation & Classification, Resolution Validation, Generative Intelligence, Problem Probability Calculation, and Prevention Recommendation, each employing AI to enhance standard compliance, predictive analysis, and proactive management, thereby setting new standards for IT service management efficiency and effectiveness.

The invention discloses an indoor water tank sediment starting speed measurement system and method, and aims to solve the technical problem that data accuracy is affected due to the fact that a sensor disturbs a flow field in an existing measurement method. The core of the technical scheme is that the non-contact measurement characteristic of the PIV technology is applied to the whole process from flow field construction to data acquisition. The device is mainly composed of an imaging unit, a laser lighting unit, a movable sliding frame, a water flow adjusting unit and a buffer plate. Optical measurement is carried out through the imaging unit and the laser illumination unit, a physical sensor does not need to be arranged in a flow field, and interference of a measuring instrument on a water flow structure and a sediment starting process is fundamentally avoided. The movable sliding frame which is integrally designed integrates the functions of sediment laying, particle throwing and measuring, so that a test area can be quickly positioned; and the buffer plate and the water inlet and drainage adjusting unit are combined, so that a gradually-changed flow field from weak to strong can be accurately and stably simulated.

The invention relates to the technical field of traffic planning, in particular to a transfer connection optimization method in multi-mode traffic integrated planning, which comprises the steps of data acquisition and demand analysis, multi-mode transfer network topology design, multi-mode timetable collaborative optimization, transfer service system development and facility transport capacity dynamic matching. According to the transfer connection optimization method in the multi-mode traffic integrated planning, a dynamic and static combined multi-source data acquisition framework is constructed based on a multi-dimensional data fusion system, the data precision can be improved, the network can be perceived in real time, association modeling of weather events and demand fluctuation is realized based on construction of a multi-agent simulation system, and the optimization of the transfer connection in the multi-mode traffic integrated planning is realized. A dynamic weight mechanism is introduced based on a hypergraph model, the robustness of the algorithm is improved, a layered optimization framework processes global optimization through upper-layer integer programming, dynamic adjustment is achieved through lower-layer deep reinforcement learning, a buffer time intelligent insertion strategy can improve the late absorption capacity, and a facility utility function model is established to improve the transfer efficiency.

The invention relates to the technical field of constructional engineering, in particular to a cement concrete durability optimization method and system for an airport runway, and the method comprises the steps: building a data collection frame, and collecting the data of the airport runway; preprocessing the collected data, and performing preliminary analysis based on the processed data to obtain main factors influencing the durability of the cement concrete; based on the preliminary analysis result, using machine learning and deep learning technologies to construct a durability prediction model; a self-learning mechanism is introduced, a feedback loop is established, actual operation data is compared with a prediction result, and model parameters are dynamically adjusted; and based on a prediction result of the durability prediction model, combining a plurality of durability indexes, and adopting a multi-objective optimization algorithm to generate an optimal cement concrete formula and a construction process scheme so as to obtain a final durability optimization scheme. By means of the method, the defects of a traditional method in the aspects of data accuracy and model adaptability are effectively overcome, and the durability optimization level of the airport runway cement concrete is improved.

The invention discloses an unmanned intelligent inspection method and system for an intelligent power plant, and relates to the technical field of power plant inspection, and the method comprises the steps: carrying out the inspection partitioning according to the function partitioning and environment attribute information of a target power plant, and arranging an inspection equipment group and an edge processing node; receiving real-time environment monitoring data through each edge node, adaptively optimizing a conventional routing inspection scheme, and executing intelligent routing inspection to obtain a routing inspection data set; and obtaining a local inspection result through independent anomaly analysis, uploading the local inspection result to a master control center for correlation influence analysis, and correcting the local inspection result. The technical problem that in the prior art, along with the complexity trend of an intelligent power plant, a traditional inspection method cannot adapt to complex and changeable inspection requirements, so that the inspection efficiency and accuracy are low is solved, and real-time monitoring and quick response of the operation state of the power plant are achieved through unmanned intelligent inspection. And the inspection efficiency and the data accuracy are improved.

The invention relates to the technical field of data processing, and discloses a knowledge graph incremental updating and consistency checking method and system. The method and the device are used for solving the problem of low incremental updating and consistency checking efficiency of a large-scale knowledge graph. The method comprises the following steps: collecting a to-be-updated data source, sorting and separately storing data, and marking priority labels; performing change detection on the marked data source, identifying change items by comparing entities and relationships, and generating a log; incremental updating is executed based on the log, and nodes and edges are processed in a hierarchical fusion mode; preliminary consistency verification is carried out, and attribute uniqueness and relation directivity are checked; expanding a verification range, traversing an association path through cascade check, and recording problems; and optimizing storage according to the record, merging the update area, updating the index and cleaning the log. The method solves the problem of low efficiency of incremental updating and consistency verification of the large-scale knowledge graph, improves the response speed and the data accuracy of the system, and is suitable for a high-frequency dynamic data environment.

The invention discloses an IMU (Inertial Measurement Unit) fault diagnosis and repair method and device, vehicle-mounted navigation equipment and a storage medium, and the IMU fault diagnosis and repair method comprises the following steps: obtaining IMU time domain data, and analyzing and processing the IMU time domain data to obtain an initial measurement value; performing periodic analysis on the initial measurement value, and performing calculation and statistics to obtain missing measurement points; diagnosing and analyzing the missing measurement points to obtain fault feature description; a fault data mapping relation is established, and fault judgment parameters are obtained through characteristic decomposition; calculating to obtain a fault classification value by using the fault judgment parameter; data accuracy measurement is carried out based on the fault classification value, and a fault degree value is obtained through threshold value dynamic comparison; calculating a correction parameter by using the fault degree value, and obtaining an adjustment sequence through parameter conversion; performing extreme value decomposition, and obtaining a steady-state quantity through waveform recombination; and inputting a quantization equilibrium equation, and performing frequency domain modulation to obtain a final repair result. According to the technical scheme, the fault diagnosis accuracy and the repair comprehensiveness are improved, and the fault can be accurately diagnosed and repaired under the complex working condition.

The invention relates to the technical field of artificial intelligence, in particular to a government work report generation system and method based on multi-path retrieval and a large model, and the system comprises a data engineering module, a database system module, a multi-path retrieval module, a cue word engineering module and a report generation system module. The system has the beneficial effects that database management and work report generation capabilities are considered, a user not only can store, manage and check own government affair data, but also can be linked to a report generation module to generate a government work report based on a knowledge base constructed by a client, and the data accuracy and personalization of report content are greatly improved.

The invention provides a gas data collection and analysis system. The gas data collection and analysis system comprises a data acquisition module, a data transmission module, a data storage module, a data analysis module and an alarm and visualization module. According to the system, gas operation parameters are collected in real time through flow, pressure, temperature and gas concentration sensors, the data accuracy is improved through a correction algorithm, data are transmitted to a cloud or a local server for storage based on the wireless communication technology, and data safety and efficient storage are guaranteed through a distributed architecture. According to the gas data collection and analysis system, gas demand prediction and anomaly detection are achieved through a time sequence prediction model and a clustering algorithm, leakage or abnormal operation conditions are accurately recognized, when gas leakage, flow anomaly or equipment faults are detected, an alarm is automatically triggered, related personnel are remotely notified, meanwhile, a data visualization interface is provided, and the system is convenient to use. And real-time monitoring and trend analysis are supported.

The invention relates to the technical field of expressway monitoring, and provides an expressway abnormal state detection method and system based on a support vector machine, which processes vehicle trajectory data and ETC gantry data through Kalman filtering and adaptive weighted average to improve the data accuracy, introduces a Gaussian kernel function to construct an SVM model, and improves the detection accuracy of the expressway abnormal state. An original feature space is mapped to a high-dimensional space to deal with a nonlinear problem, samples with labels are used to train a multi-classification SVM model, and accurate detection of various abnormal states such as traffic congestion, accidents and vehicle faults is realized through a voting mechanism. According to the method, the accuracy of abnormal state detection is improved, powerful decision support is provided for expressway managers, the traffic flow is optimized, the road traffic capacity is improved, and the development of an intelligent traffic system is promoted.

The invention discloses a container and encryption-based data behavior security management method. The method comprises the steps of preparation, data asset creation and association, data storage, data asset behavior recording, data asset behavior management and control and data asset output. The data is encrypted and stored by adopting an encryption technology, and the original data is converted into the ciphertext, so that the data is prevented from being illegally acquired in the transmission and storage process; access authority is strictly limited through RBAC control, it is ensured that only authorized users or application programs can access and operate data, and data leakage and tampering are prevented; data injection is realized by utilizing a data volume of a containerized platform, so that an application program in a container accesses external data, the data can be shared in different environments conveniently, the data can be stored persistently, and the availability of the data is ensured; the containerization expandability is convenient for expansion and adjustment when the data scale is expanded; the binding of the data ID and the comprehensive behavior record flow ensure the unique identification of the data and improve the accuracy and traceability of the data.

The invention discloses a wind profile radar radial speed quality control and horizontal wind field inversion method. According to the method, through the steps of multi-mode detection splicing, signal-to-noise ratio threshold value quality control, beam consistency inspection, horizontal wind component inversion, space and time continuity inspection and the like, the quality control process of the wind field data is optimized, the precision of the horizontal wind field data is remarkably improved, and particularly, the root-mean-square error and deviation in high-level data are remarkably reduced. The core innovation comprises a mode splicing strategy based on sounding data comparison, dynamic signal-to-noise ratio threshold calculation, threshold setting of beam consistency check and a wind component compensation algorithm when a vertical beam is missing. Through experimental verification, compared with a traditional wind profile radar data processing method, the wind profile radar data processing method has remarkable advantages in data accuracy and stability.

The invention relates to the technical field of environment assessment, in particular to a coastal zone culture area multi-dimensional environment assessment method and a cloud platform. The method comprises the following steps: continuously recording dissolved oxygen concentration, pH value, centigrade temperature, salinity unit, turbidity scattering unit, chlorophyll fluorescence intensity, ammonia nitrogen milligram per liter and nitrite milligram per liter, synchronously calling multi-source remote sensing images covering a culture area and an adjacent water area, extracting remote sensing reflectivity data through image correction and atmospheric correction, and calculating to obtain water color parameters. According to the method, the water quality sensor array is deployed at the key point of the culture area, continuous multi-parameter environmental data acquisition is carried out, multi-source remote sensing images are synchronously integrated, water color information is extracted in real time, and the timeliness and data accuracy of culture water area environmental monitoring are improved; trend decomposition and dynamic baseline construction are carried out based on the data sequence, so that the environmental fluctuation evaluation of the breeding area is more objective and accurate.

The invention relates to the technical field of data processing, and further relates to a forest ecology monitoring system and method with an unmanned aerial vehicle carrying a multispectral sensor. The system comprises an unmanned aerial vehicle, a hyperspectral data processing device and an ecological health detection platform, the unmanned aerial vehicle carries a multispectral sensor so as to acquire multispectral data of a forest area and perform atmospheric correction on the surface reflectance; the hyperspectral data processing device is used for reconstructing hyperspectral data from the multispectral data by using a compressed sensing theory and carrying out noise reduction by using wavelet transform to obtain the hyperspectral data after noise reduction; and the ecological health detection platform is used for calculating forest health indexes and forest carbon reserves according to the hyperspectral data after noise reduction, and presenting the forest health indexes and the forest carbon reserves to users. According to the method, comprehensive and accurate evaluation of the health condition and the carbon reserve of the forest ecosystem is realized, and the defects in the aspects of data precision, processing efficiency and evaluation comprehensiveness in the prior art are overcome.

The invention discloses a water conservancy facility monitoring method and system based on digital twinning, and relates to the technical field of water conservancy monitoring. Historical operation data of a water conservancy facility are obtained based on a digital twinning model for analogue simulation, and full life cycle data of the water conservancy facility are obtained; and importing the standard operation data into the digital twin model, carrying out data comparison and data compensation on the standard operation data and the full life cycle data, verifying the accuracy of the standard operation data, and generating output verification data after comparison is completed. The verification data is generated by comparing the operation data with the data simulated by the digital twin model and combining cross verification of multiple operation data in the same area, so that data accuracy confirmation and data compensation can be carried out on the data distortion problem caused by abnormal data acquisition of the water conservancy facility operation data acquisition equipment; the reliability of the collected operation data is ensured, and the accuracy of evaluation and decision-making of the subsequent water conservancy facility monitoring operation state is guaranteed.

The invention discloses a water conservancy water regimen monitoring management system based on Beidou short message communication, which relates to the technical field of water regimen monitoring and comprises a scheduling module, a data compression module, a data preprocessing module and an early warning module. The scheduling module determines a transmission queue by marking a water level state, calculating various scores and correction parameters, and implements a preemption mechanism and setting optimal transmission in a silent period; the data compression module classifies and processes data, adjusts the compression rate according to channel signals, and adopts forward error correction coding and the like to guarantee the data accuracy; the data preprocessing module filters the original data, detects abnormity by using space-time correlation analysis, and fills missing values; the system effectively realizes water conservancy water regimen monitoring management, has significant advantages in the aspects of data transmission, processing, early warning and the like, improves the accuracy and timeliness of water regimen monitoring, and provides powerful support for water conservancy management.

The invention relates to the technical field of earthquake prediction, in particular to a submarine seismic oscillation prediction method and device based on earthquake exploration, and the method comprises the following steps: selecting a monitoring region, collecting the submarine topographic data of the monitoring region, and drawing a topographic map; selecting a plurality of monitoring points according to the topographic map; geomagnetic field information, electric wave information, micro-seismic activity information and seabed deformation information of each monitoring point are collected and recorded; converting the collected information into acoustic signals and transmitting the acoustic signals to a sea surface buoy, and converting the acoustic signals into electric signals by the sea surface buoy and transmitting the electric signals to a ground data center; by optimizing site selection of monitoring points, integrating geomagnetic-electric wave-microseismic-deformation multi-source data, realizing high-efficiency data transmission by using an acoustic-electric staged communication chain, and finally realizing early recognition of earthquake precursor in combination with threshold analysis, the method can significantly improve the coverage range, data precision and early warning timeliness of seafloor earthquake monitoring, and has good application prospects. And a technical support is provided for marine geological disaster prevention and control.

The invention relates to the technical field of remote sensing imaging, in particular to a marine organism monitoring method and system based on a remote sensing technology, and the method comprises the following steps: constructing an environment layer based on a remote sensing image and sensing data, analyzing the relation between a spectrum and an environment, monitoring changes, recognizing the spatial distribution of species, extracting a migration path, and superposing environment factors to analyze ecological response. And outputting a remote sensing indication species ecological response interaction layer. According to the method, the remote sensing image and environmental data are sorted and denoised, multi-dimensional factors are effectively integrated, data precision is enhanced, noise interference is eliminated, spectral change and water temperature gradient deviation are combined, abnormal pixels are accurately screened, sea area change trend is insight in advance, key wave bands and hot salt structures are extracted, target organisms are accurately positioned, and identification efficiency is improved. Analyzing a space offset track, restoring a migration path, constructing a dynamic evolution view angle, superposing water temperature and algae changes, analyzing time sequence synchronization characteristics, revealing ecological response turning points, and strengthening early warning and dynamic monitoring capabilities.

The invention provides a field NL2SQL data automatic synthesis method and system based on a large language model, and the method comprises the steps: building an SQL template library through collecting a public data set and real business query data, carrying out the grading according to the SQL complexity, and forming an SQL template set covering different difficulties; specific SQL queries are generated based on an SQL template library and a large language model, verification is carried out through multiple mechanisms such as grammar check, execution verification and result dimension consistency check, and correctness and performability of the SQL queries are ensured. Generating a plurality of candidate natural language questions according to the verified SQL query and the large language model, calculating a semantic consistency score of each candidate question and the SQL query through a cross consistency evaluation mechanism, screening out the question with the most consistent semantics as a final question-answer pair sample, and meanwhile, eliminating low-quality samples by setting a consistency threshold value, so as to obtain a final question-answer pair sample; and the data accuracy and consistency are further improved.

The invention discloses a soil heavy metal content estimation method and device, and relates to the technical field of soil heavy metal estimation.The method comprises the steps of sample collection and data acquisition, specifically, based on geographic information of a research area, soil samples are collected through a layered and partitioned sampling method; according to the method, comprehensive and accurate collection and data acquisition of soil samples are realized through a layered and partitioned sampling method in combination with a high-precision positioning and measuring instrument, the sample representativeness and the data accuracy are ensured, and through an improved correlation analysis algorithm and a depth model based on feature fusion, the soil sampling accuracy is improved. Accurate analysis and efficient modeling of correlation between environmental magnetic parameters and heavy metal content are realized, prediction precision is improved, dynamic optimization of model parameters is realized by applying a self-adaptive step length strategy in model verification and optimization, model stability and generalization ability are guaranteed, and all modules of the device work cooperatively, so that the reliability of the device is improved. And deep analysis of heavy metal sources and migration and transformation rules is realized by using a geochemical analysis module and related models.

The invention discloses a liquid cooling charging module health state prediction system, and relates to the technical field of electrochemical detection, and the system comprises a multi-parameter collection unit which collects the pressure and flow data of a cooling liquid, and is provided with an independent collection node at each branch of a double-gun charging system, and provides basic data for monitoring; the dynamic calibration unit communicates with the acquisition unit, compensates data drift based on a temperature-vibration interference model of machine learning training, executes baseline calibration by using an idle period to update a reference value, and guarantees data accuracy; the leakage detection unit is used for calculating a data change rate after compensation, outputting an early warning according to a preset condition and positioning a leakage branch; and the health state evaluation unit fuses the early warning signal and the historical operation data to generate a prediction result. The problems that tiny leakage detection is difficult and a sensor is prone to interference can be solved, manual inspection is reduced, the maintenance cost is reduced, and the requirement of a high-power charging scene is met.

The invention discloses a three-dimensional target detection and fusion method based on multi-sensor fusion, and relates to the technical field of image processing, and the method comprises the steps: obtaining multidirectional two-dimensional image data through a camera, synchronously employing a laser radar to obtain point cloud data, constructing a two-dimensional bounding box and a three-dimensional bounding box respectively, and screening out a preferable two-dimensional image and the point cloud data through matching, and carrying out feature fusion to obtain a two-dimensional fusion image to be detected. And then the recognition accuracy is accurately obtained, sorting is carried out according to a descending order, high-accuracy fusion images are sequentially extracted to be combined with point cloud data to determine a preliminary detection result, target geometric information is scientifically set based on the result, and finally a standard three-dimensional target detection result is determined by integrating the three-dimensional geometric information, fusion image details and point cloud data precision. The method has the advantages that multi-sensor heterogeneous data are deeply fused, and the accuracy and reliability of three-dimensional target detection in a complex scene are remarkably improved through multi-stage fine processing and intelligent analysis.