1228 results about "Level data" patented technology

The invention relates to the technical field of financial data processing, in particular to a multi-source heterogeneous fund data processing method and system, and the method comprises the steps: recording source information through building a data source registry, and marking a unique identifier for data; performing differential analysis on the fund data in different formats to generate standardized column type storage data; traversing column type storage data to extract statistical characteristics, performing field classification through metadata analysis and financial dictionary matching, analyzing business connotations of fields difficult to classify in combination with a localized large language model, and mapping the business connotations to a header fusion knowledge graph; generating a mapping rule from the source field to the enterprise-level data model by applying a rule engine template on the basis of field classification and semantic recognition results; converting the data structure according to the mapping rule and executing standardization processing; the quality is further optimized through data cleaning; and finally, the data are verified, standardized fund data supporting data traceability are output, and the strict requirements of financial supervision application are met.

The invention discloses an intelligent environment monitoring and regulation method for a marine ranch, and belongs to the field of environment monitoring, and the method comprises the steps: collecting water body parameters, employing a data fusion algorithm to generate a comprehensive environment state index, and solving the business problems of data dispersion, response lag and inaccurate adjustment in water body environment monitoring and regulation. A time sequence analysis model and an optimization algorithm are combined, potential anomalies are analyzed, a risk change trend is predicted, environment adjustment requirements are dynamically calculated, a precise regulation and control instruction set is generated and transmitted to equipment to be executed, and meanwhile, through deviation comparison between real-time feedback data and a preset threshold value, comprehensive indexes and regulation and control parameters are iteratively updated, so that environment stability is ensured. Through multi-level data processing and a self-adaptive adjustment mechanism, the timeliness and accuracy of water body environment regulation and control are remarkably improved, closed-loop management from monitoring to optimization is achieved, and efficient support is provided for intelligent decision making in a complex environment.

The invention discloses an unsupervised semi-pairing cross-modal retrieval method and system based on deep learning, relates to the field of artificial intelligence, and is used for solving the problems of annotation data dependence, asymmetric semantic association and high-dimensional storage efficiency. According to the method, a double-branch visual encoder and a dynamic prompt text encoder are combined, dynamic weighting of visual-text features is achieved through gating cross attention, and modal redundancy interference is restrained. An enhancement strategy is generated through low-frequency semantic guidance, and the long-tail word coverage rate is increased; a dual-stage quantitative hierarchical index is constructed, coarse-grained clustering and fine-grained product quantitative compression feature storage is adopted, and million-level data real-time retrieval is supported. A degradation aware increment maintenance mechanism monitors data distribution offset through a KL divergence threshold, and triggers index reconstruction to maintain long-term update precision. According to the method, limitation of a traditional strong pairing model is broken through, cross-modal sensitive content second-level positioning is achieved, asymmetric semantic alignment is effectively solved, and retrieval efficiency is improved.

The invention discloses an optical fiber embankment underwater piping leakage event time-space correlation analysis method, and relates to the technical field of leakage event intelligent identification and risk assessment in embankment safety monitoring, and the method comprises the following steps: S1, obtaining continuous time-space monitoring data of an embankment underwater region obtained through monitoring by a distributed optical fiber sensing system; and S2, processing the continuous space-time monitoring data by adopting a feature recognition model based on a neural network, and recognizing a suspected leakage event. According to the time-space correlation analysis method for the underwater piping leakage event of the optical fiber embankment, by fusing multi-level data processing and self-adaptive feature learning, false alarms caused by environmental interference are effectively restrained, and the recognition accuracy of a real leakage event in a complex underwater scene is improved. An analysis framework combining space-time association diagram construction and physical mechanism verification is adopted, the internal relation between events can be deeply mined, and the space-time evolution rule of a seepage path is accurately restored.

The invention discloses a wharf shoreline terrain reconstruction method based on remote sensing data, and belongs to the technical field of three-dimensional terrain reconstruction. The method comprises the following steps: acquiring a multi-temporal multispectral remote sensing image, a synthetic aperture radar image, a laser radar point cloud and synchronous tidal water level data of a target wharf area; processing the multispectral image to generate an initial land and water segmentation map; carrying out tide correction by utilizing the tide water level data, fusing the multi-polarization characteristics of the synthetic aperture radar image, and correcting the ground feature type to obtain an accurate land and water boundary diagram; processing the laser radar point cloud to generate a digital surface model, extracting a shoreline contour with sub-pixel-level precision from the digital surface model, and endowing the shoreline contour with an elevation value; and superposing the shoreline contour with the elevation with the multispectral image to generate a three-dimensional terrain model. According to the method, the problems of low terrain reconstruction precision, large tide influence and insufficient automation degree of a traditional method in a complex wharf environment are solved, and high-precision and high-efficiency wharf shoreline three-dimensional automatic reconstruction is realized.

The invention relates to the technical field of data processing and analysis, and discloses a parallel sensor data analysis method and system for cable fault detection, and the method comprises the steps: synchronously collecting cable fault transient traveling wave signals through distributed monitoring terminals, and forming a multi-channel signal data set; and carrying out wavelet packet decomposition and reconstruction, and denoising to obtain a denoised data set. The method comprises the following steps: firstly, generating node time sequence data with a timestamp, constructing an undirected weighted graph model, calculating a plurality of candidate fault positions through a double-end positioning formula on the basis of corrected time data, and finally, allocating dynamic weights to effective candidate positions according to signal quality and confidence, and generating a fusion positioning result by adopting a weighted fusion algorithm. And physical correction is carried out based on cable laying constraints, and accurate fault position coordinates are output. According to the method, through multi-level data processing and fusion, the fault positioning precision and the system robustness under complex working conditions are improved.

The invention discloses a multi-modal heterogeneous medical equipment data fusion and decision support method and device, and aims to solve the problems that the fusion precision is low due to space-time semantic difference of medical equipment multi-modal heterogeneous data (equipment operation parameters, clinical records, fault signals and the like), equipment management decisions depend on experience, and standards are not uniform. According to the method, breakthrough is achieved through three-level data alignment of'time-space-semantics', hierarchical fusion of'data level-feature level-decision level ', three-level decision driven by a knowledge graph and dynamic feedback optimization: time alignment uses a dynamic time warping algorithm, space alignment depends on a unified data dictionary, and semantic alignment introduces an attention mechanism; the feature level fusion quantifies the feature support degree based on the D-S evidence theory; the decision-making layer constructs a'rule-case-prediction 'three-level system, and combines cosine similarity retrieval and information entropy quantification uncertainty. The method and device can support medical equipment maintenance, clinical diagnosis and treatment and other scenes, and the medical service standardization level and the equipment management efficiency are improved.

The invention relates to a copper-clad plate production line collaborative regulation and control method based on digital twinning, and the method comprises the steps: constructing a multi-level data set of dynamic virtual-real mapping through full-process digital twinning modeling, process data normalization and multi-level feature tagging, achieving the high consistency of a physical production line working condition and a simulation system, and achieving the collaborative regulation and control of a copper-clad plate production line in combination with an autonomous scheduling intelligent agent. Real-time perception and joint feature mining of multi-dimensional data such as equipment load, health degree and energy consumption are realized, the real-time adaptive scheduling capability of a production line to states such as sudden load and equipment aging is improved, scheduling target weights are periodically and adaptively generated, optimal instant balance of multi-target income is realized, and the optimal real-time scheduling capability of the production line is realized by adopting an evolutionary game and swarm intelligent optimization. A cross-device optimal resource scheduling scheme under the multi-target constraint is obtained, an evolution model is continuously fed back through real-time production data, a scheduling-execution-calibration-rescheduling closed-loop mechanism is formed, and the self-learning and long-period stability capabilities of a scheduling system are remarkably improved.

The invention relates to the technical field of artificial intelligence, and provides a marketing data generation method and device based on portrait data, equipment and a medium, marketing association data can be collected and purified based on a three-level data gateway, feature fusion is carried out by using a star-shaped collaborative network constructed based on a dynamic weight mechanism and a federated learning mechanism, and the marketing data generation efficiency is improved. The problems of data dimension limitation and data island are solved; scene recognition is performed based on a marketing data graph constructed by a secondary scene classification tree including a gift scene, and the problems of low utilization efficiency of unstructured data and insufficient crowd portrait granularity are solved; the marketing strategy is generated by using the target engine matched with the scene, so that the problems of scene engine deficiency and gift scene adaptation imbalance are solved; and generating the target marketing data according to the target marketing strategy and the marketing data graph. The problems of low operation efficiency and insufficient content accuracy are solved.

The invention discloses a gleying rice field intelligent irrigation and drainage regulation and control method based on reinforcement learning. The method comprises the steps that meteorological data, soil data and field surface water level data are collected; calculating crop state information based on the meteorological data and the soil data by using a crop growth model; inputting the meteorological data, the soil data, the field surface water level data and the crop state information into an offline training strategy model, outputting action suggestions, and performing verification and decision making according to preset regulation and control logic in combination with the meteorological data, the soil data, the field surface water level data, an external data source and agronomic rule base data; a control strategy is generated, irrigation, drainage or oxygenation is executed, and irrigation and drainage regulation and control are completed. Compared with the prior art, the method has the advantages that priority regulation and control logic for ORP / pH value real-time feedback is established, a prospective pre-drainage mechanism for rainfall forecast is fused, water layer constraint in the growth period and conditional filtering rules in the fertilization safety period are fused, and a perception-decision-execution closed-loop control system is formed.

The invention discloses a production line data integration method based on a digital twin model, and belongs to the technical field of electronic data processing. The production line data integration method comprises the following steps: step S0, pre-preparation and standard definition; the method comprises the following steps: S1, data acquisition and preprocessing; step S2, data cleaning and integration; s3, data analysis and mining; 4, constructing a digital twinborn model; and 5, managing system-level data. The method has the following advantages: data islands are cracked, data quality and processing efficiency are improved, internal business logic of data is mined, high-precision feature extraction and pattern recognition are realized, and the method is suitable for popularization and application by means of unifying data standards and cleaning rules, optimizing a merge sorting strategy, building a double-layer association analysis model, adopting a CNN-LSTM hybrid model, perfecting data security and authority management and the like. The method supports the cross-line collaborative decision and precise production optimization, finally solves the problem that the prior art cannot support the system-level digital twinning application, and improves the application efficiency and effect of the digital twinning system.

The invention discloses a row-level data recovery method and device for a damaged file of a GoldenDB database, and the method comprises the following steps: S1, scanning a to-be-recovered GoldenDB data file, and recognizing and caching all data pages; s2, on the basis of metadata of the data pages, cluster index leaf node pages containing user data are screened out from the cached data pages; s3, for each screened leaf node page, analyzing an internal record organization structure of the leaf node page, and performing row-level data recovery; the row-level data recovery comprises the steps of traversing user records according to a virtual record pointer, and performing cross check and recovery on a traversal path in combination with a page directory slot so as to extract and mark the user records one by one; and S4, outputting all the recovered row-level data.

The invention discloses a multi-modal fusion bank receipt intelligent processing system and method based on vision and NLP, and the method comprises the steps: receiving a bank receipt picture or a PDF document, and completing the text detection, direction correction and character recognition through a visual processing engine; a three-level receipt independent segmentation mechanism is applied, and independent receipt records are divided through spatial clustering analysis, semantic analysis, visual verification and cross-page association processing; jointly extracting text features and layout features of each receipt through a double-flow multi-modal fusion model, and fusing the text features and the layout features; field-level data extraction is executed through a field extraction engine, and financial data verification including account number, amount, date and abnormity quadruple verification is carried out; and generating structured JSON output to obtain a bank receipt processing result. According to the invention, the innovative five-layer processing architecture realizes high-precision analysis of the bank receipts through an independently researched and developed receipt independent segmentation engine, a vision-semantic fusion model and a financial data verification system.

A federated byte latent transformer platform utilizing homomorphically-compressed and encrypted byte-level data. The system integrates dynamic entropy-based patching into federated learning to enable efficient, robust, privacy-preserving collaborative learning across distributed nodes. Client devices convert local data into dynamically sized patches based on entropy thresholds, encrypt these patches, and send them to a central server that processes them without decryption. The system offers improved robustness to input noise, enhanced character-level understanding, and better adaptation to low-resource languages compared to token-based approaches. It enables simultaneous scaling of both patch size and model size while maintaining fixed inference budgets, allowing efficient deployment on resource-constrained devices. These innovations address critical challenges in federated learning: efficiency, robustness to data heterogeneity, and privacy preservation.

The invention belongs to the technical field of energy storage system control, and particularly relates to an SOC offset optimization control method and system for a 5G base station energy storage system, and the method comprises the steps: obtaining the operation data of an energy storage battery; updating an SOC offset optimization interval through a dynamic threshold adjustment algorithm, performing prediction in combination with a base station load prediction model to obtain a prediction result, and dynamically selecting a target strategy in a charging mode and a discharging mode according to the prediction result and a battery state to perform charging or discharging; based on the target strategy, optimizing the output power of the bidirectional converter by adopting a model prediction control algorithm, and correcting the SOC offset in real time in combination with a dynamic loss model; and the corrected SOC offset, temperature and load fluctuation parameters are transmitted to a cloud monitoring platform, multi-level data fusion analysis is carried out, meanwhile, self-inspection is carried out on the health state of the battery regularly, and three-dimensional alarm information containing a fault position probability cloud picture is fed back. Therefore, the problems of poor adaptability, insufficient estimation precision and the like in the prior art are solved.

The invention discloses an industrial internet data three-level connection method and system, and the method comprises the steps: collecting equipment data at an equipment layer, generating an equipment abnormality index based on multi-source data in combination with a process stage label, and triggering edge preliminary screening; receiving each equipment anomaly index at the production line layer, and calculating the comprehensive disturbance intensity of the production line according to the equipment anomaly indexes and the process dependence; the method comprises the following steps: collecting a disturbance intensity sequence of each production line at a factory layer, and calculating a coupling risk index through dynamic time warping, mode entropy and energy consumption dispersion; carrying out marginal sensitivity analysis on the coupling risk indexes to obtain a key production line set, and obtaining a potential root cause conduction path according to the equipment anomaly indexes and the comprehensive disturbance intensity of the production lines in the set; and verifying the vibration spectrum collected by the core equipment in the path and the historical baseline, matching the verification result with the historical maintenance record, and realizing the self-adaptive updating of the rule according to the matching result and the subsequent tracking. According to the invention, the fault positioning precision of the industrial equipment is improved.

The invention provides a vehicle NVH comfort assessment method and device based on sound and vibration fusion and electronic equipment. The method comprises the following steps: performing in-vehicle interference noise identification processing on window-level data in a candidate window set to obtain a candidate effective window set; calculating a sound and vibration consistency index between the in-vehicle audio signal and the in-vehicle vertical acceleration signal for the window-level data in the candidate effective window set, and screening an external noise dominant window to obtain an effective window set; respectively calculating an acoustic index and a vibration index based on the effective window set, and performing calibration compensation on the acoustic index and the vibration index based on the sensor calibration information; and performing fusion calculation based on the data volume information of the effective window set, the working condition coverage information, the uncertainty information corresponding to the sensor calibration information and the proportion information of the effective window set in the candidate window set to obtain a comprehensive confidence index. According to the method, the evaluation accuracy and stability can be improved, the cross-equipment comparability is enhanced, and the result credibility is improved.

The invention relates to the technical field of fire fighting, in particular to a fire fighting safety technical-connection early warning and task trajectory tracking system, which comprises a safety technical-connection early warning subsystem and a task trajectory tracking subsystem, and is characterized in that the safety technical-connection early warning subsystem and the task trajectory tracking subsystem realize bidirectional linkage through a data middle table; through bidirectional linkage of the safety technical linkage early warning subsystem and the task trajectory tracking subsystem, an intelligent cooperative closed loop of early warning and rescue is constructed, millisecond-level data interaction between the multi-mode monitoring unit and the multi-mode tracking unit is achieved through the data middle platform, and when the early warning system recognizes fire point coordinates and a three-level risk level, the safety technical linkage early warning subsystem and the task trajectory tracking subsystem are combined to achieve early warning and rescue. An optimal internal attack path can be pushed to a trajectory tracking system in real time; on the contrary, when the vital signs of the rescuers are abnormal, emergency equipment such as a smoke exhaust fan can be reversely triggered to be started, and a full-link response mechanism of risk pre-judgment, path planning and dynamic intervention is formed.

The invention relates to the technical field of database backup, and particularly provides a MySQL database incremental encryption backup method and device based on MySQLdump, and the device comprises a total backup module, an incremental backup module, an encryption module and a backup management module; the full backup module adopts a MySQL official tool mysqldump to realize complete backup of the database; the incremental backup module is realized on the basis of a MySQL binary log binlog, and data changes are captured by analyzing event records in the binlog; the encryption module is constructed based on an OpenSSL library and provides enterprise-level data protection capability; and the backup management module realizes backup full-life-cycle management. Compared with the prior art, the method has the advantages that the problem of high consumption of traditional total backup resources and the safety problem can be solved, and meanwhile, the complexity and extra cost of a third-party backup tool are avoided.

The invention relates to a multi-level data collection method, system and device and a storage medium. The method comprises the steps that an engineering project cost file is received; performing structured analysis on the construction cost file of the engineering project, and extracting an original data field; performing term mapping processing on the original data field to generate a standardized data entity; and according to a preset project cost index system, carrying out hierarchical collection on the standardized data entities to generate a structured knowledge base, and storing the project cost file and the corresponding structured knowledge base into a data collection case library. The method has the effect of improving the data collection efficiency and accuracy of the engineering project cost file.

The invention discloses a row-level data permission authorization method and device, equipment and a medium, and relates to the technical field of computers. Comprising the following steps: registering basic data into a preset database; configuring a corresponding field management user with a field management authority for the preset service field; creating or updating a record in a user data permission table based on the user permission application to determine a permission relationship between the user identifier and the entity data; registering a service data table and a mapping relation between an entity column and an entity type in the service data table in a preset service field based on a user instruction of a field management user; and dynamically generating a permission filtering condition based on the query SQL and the user identifier so as to generate an SQL statement with permission filtering, and querying corresponding service data from the service data table based on the mapping relationship so as to generate a corresponding query result based on the service data. Therefore, the efficiency and flexibility of line-level data permission authorization can be improved on the premise of ensuring data security.

The invention discloses a method and a system for verifying the reliability of remotely acquired data of an electric meter box, particularly relates to the technical field of communication and data transmission, and aims to solve the problem that the reliability of the remotely acquired data of the electric meter box is verified without changing an existing netmeter and a communication protocol. The invention aims to solve the problem of how to execute cross-layer reviewable reliability verification and quantitative credibility evaluation on a single reading result remotely acquired by an electric meter box so as to identify and eliminate the abnormity that communication is successful but data is not credible in real time, and support source end positioning. By introducing fusion of a cross-layer evidence set and an active detection mechanism, the credibility of each copying and reading result can be comprehensively evaluated. Multi-level data of a physical layer, a link layer, an application layer and the like are verified, and data exception is identified through active detection. Through the mechanisms, the method can effectively eliminate reading results which succeed in communication but have errors in actual data, thereby ensuring that the collected data are more reliable and accurately reflect the actual state of the electric meter.

The invention relates to the technical field of intelligent manufacturing and industrial big data application, in particular to a production and manufacturing information management system and method based on big data integration. The method comprises the following steps: carrying out multi-dimensional characteristic parameter acquisition on wood in a production and manufacturing process, and constructing a wood digital characteristic model; importing the wood digital characteristic model into an enterprise big data platform to carry out enterprise-level data integration, and carrying out distributed storage and retrieval system construction to form a wood characteristic parameter map; wood deformation data are obtained and extracted from the finished product use data, characteristic-deformation correlation analysis is conducted, and a characteristic deformation correlation table is obtained; and carrying out deformation risk analysis according to the characteristic deformation association table to obtain a wood deformation sensitivity index. According to the method, the big data system throughout the full life cycle of the wood is constructed, so that high stability and high quality of products are guaranteed from the source while the resource utilization rate is maximized.

The invention relates to the field of industrial Internet of Things data processing, in particular to an industrial Internet of Things data acquisition and elastic transmission method. The method comprises the following steps: acquiring a process unit topology and monitoring target configuration, performing process unit code registration, probe layout scheme generation and acquisition session configuration structure registration processing, and generating an acquisition session configuration structure; time anchor point unified processing, change node alignment sequence generation and contact value grading threshold registration processing are executed, and a data importance grading table structure is generated; executing fingerprint detection session list generation, network state fingerprint matrix construction and network state category judgment processing, and generating a network state category structure; and carrying out elastic transmission strategy graph construction and strategy version record registration processing to generate an elastic transmission execution configuration structure. According to the invention, through multi-level data processing and intelligent state perception, accurate alignment of data acquisition and elastic self-adaption of transmission are realized, and the reliability and efficiency of an industrial Internet of Things system are effectively improved.

The invention discloses a real-time data receiving and transmitting system and method based on a kernel bypass, and the method comprises the steps: processing the received data when the data receiving and transmitting system receives the data, and achieving the high-level data with complete semantics; updating a write pointer through atomic operation, and writing high-level data into a lockless annular queue in the shared memory module; the upper-layer application module reads the lock-free annular queue in the shared memory; when the data receiving and transmitting system transmits data, upper-layer data to be transmitted is transmitted to the shared memory module, and a write pointer is updated; the upper layer data in the shared memory is read in a polling or interruption mode, a read pointer is updated, the upper layer semantic data is segmented according to the format, a UDP data packet is obtained, the UDP data packet is written into a network card in the polling or interruption mode, and data sending is achieved. Through the data receiving and transmitting system disclosed by the invention, microsecond-level ultra-low delay of data certainty is realized, and efficient decoupling and stable throughput are realized.

A simulation method for characterizing the spatiotemporal changes of groundwater levels, which belongs to the technical field of computer machine learning; includes the steps of: obtaining long-term groundwater level data based on long-term groundwater monitoring wells; superimposing multiple hydrogeological parameter information based on groundwater level data; and constructing a regional neural network model with physical mechanism by using a two-dimensional groundwater seepage equation serving as a physical mechanism, thereby simulating the temporal and spatial evolution of regional groundwater levels. The present invention can strengthen the rational use of groundwater resources, implement scientific and effective supervision, and provide a scientific basis for protecting regional groundwater safety and promoting the sustainable development of groundwater resources.

The invention relates to the technical field of big data storage, and discloses a multi-level data storage and mixed query method and system, a storage medium and equipment, and the method comprises the steps: collecting original buried point data, carrying out real-time stream processing, storing the processed real-time data in a hot storage layer, carrying out timing offline batch processing on historical buried point data, and storing the processed real-time data in a hot storage layer; converting into an optimized storage format and storing in an object storage system; an extended metadata management system is constructed, and structure information, partition information and access frequency information of data in the object storage system are automatically extracted and managed; executing query, and reducing the data scanning amount through a query optimization strategy; automatically migrating data among different storage layers through a dynamic cold and hot data layering mechanism; a unified query interface is provided, a query request is distributed to a corresponding real-time processing system or an offline query system based on an intelligent routing strategy, and a query result is cached and returned, so that high-performance and low-cost storage and second-level query of data are realized.

The invention discloses a shield tunnel-oriented duct piece dislocation detection method, which comprises the following steps of: acquiring an original multi-source data set, and constructing a ring-level data packet set indexed according to ring numbers according to pre-stored propulsion encoder information; extracting local features of the circular seam, constructing self-calibration optimization data by utilizing the spatial deviation of an actual circular seam and a designed circular seam, jointly solving external parameters of a sensor and a ring-level attitude correction amount, and generating a circular seam alignment result set containing an accurate circular seam spatial position; mapping the circular seam alignment result set to a polar coordinate domain, and extracting an attention enhancement feature set through a pre-configured parallel patch sensing network; and in combination with geometric information in the circular seam alignment result set and visual features in the attention enhancement feature set, calculating a slab staggering geometric vector decomposed into multidirectional components, and generating a slab staggering classification result set. According to the method, the problem that sensor drifting and tiny slab staggering characteristics are difficult to recognize in long-distance tunneling is effectively solved, and full-process automatic accurate control over the segment assembling quality is achieved.

The invention provides a detection method and system for an automatic drain valve, and relates to the technical field of equipment testing. The method comprises the following steps: collecting working data of a drain valve to be detected; determining a flow peak change trend and a drainage efficiency attenuation degree based on flow data of continuous multiple drainage records, and generating an impurity deposition index; generating an impurity stripping index based on the instantaneous change rate of the flow velocity data and the variable quantity of the flow data, and determining a first impurity deposition degree in combination with an impurity deposition index; determining a vibration anomaly factor and an acoustic anomaly factor based on the vibration data and the acoustic data, and determining an impurity anomaly score in combination with the first impurity deposition degree; determining the working intensity based on the vibration data, the pressure data and the liquid level data, obtaining the drainage frequency of the drainage valve to be detected after last maintenance, and determining the fault probability based on the working intensity, the drainage frequency and the impurity anomaly score; and predicting the fault condition of the to-be-detected drain valve based on the fault probability. The fault detection accuracy is improved.

The invention provides a low-power-consumption wireless ad hoc network dynamic configuration method and system based on adaptive routing, and relates to the technical field of network protocols, and the method comprises the steps: determining a data transmission path through obtaining node working state parameters and calculating an energy level value, and setting a multi-level data cache occupation threshold value; calculating a network load distribution state, and carrying out differentiated scheduling on nodes; selecting a low-power-consumption forwarding channel based on node routing adaptability; and monitoring the network state in real time and updating parameters. According to the invention, the network adaptability is improved, the communication power consumption is reduced, the network life cycle is prolonged, and the data transmission efficiency is improved.