132 results about "Benchmark database" patented technology

The invention discloses an integrated collaborative management method for construction site risk operation data, and belongs to the technical field of data management. The method comprises the following steps: constructing and calibrating a space-time risk reference database; deducing a potential risk conduction link set, and generating an associated intervention knowledge base; during operation, through multi-sensor cooperative verification, an abnormal signal is confirmed as a risk event; matching the risk event with a conduction link to calculate a risk upgrade level and dynamically adjust an early warning threshold; and when the early warning is triggered, generating and issuing a dynamic collaborative response instruction, and feeding back a processing result to correct the reference database to form a management closed loop. According to the method, the technical means of constructing the space-time risk reference, deducing the conduction link, cooperatively verifying the risk and dynamically regulating and controlling the threshold are adopted, so that the predictability of project risk management and control, the efficiency of resource cooperative scheduling and the scientificity of overall management decision are improved.

The invention discloses a reservoir dam safety monitoring method and system based on edge calculation, and relates to the technical field of hydraulic engineering safety monitoring, and the method comprises the steps: obtaining monitoring data by each edge calculation node, carrying out the preprocessing, and generating a standardized data matrix; establishing a reference database, executing anomaly detection, and generating a labeling time sequence data matrix; performing multi-scale decomposition on the labeled time sequence data matrix, and constructing a node response feature matrix; the central processing unit receives data of each edge computing node, analyzes a multi-parameter spatial propagation mode by constructing a parameter-spatial correlation matrix, and obtains a spatial correlation feature matrix; establishing a Bayesian risk assessment model, predicting a dam risk level and outputting a risk evolution trend; and issuing a differentiated early warning instruction according to the risk assessment result. Through a distributed architecture combining edge calculation and central processing, real-time monitoring, intelligent analysis and accurate early warning of the dam safety state are realized, and the monitoring efficiency and the risk identification accuracy are improved.

The invention relates to the technical field of bridge health monitoring, and discloses a method for judging rigidity change of a bridge structure based on bridge health monitoring deformation data. The method comprises the following steps: establishing an initial data set of bridge deformation monitoring data and performing multi-scale decomposition processing to generate deformation component data of different time scales; inputting the deformation component data of different time scales into a pattern recognition engine, and recognizing a characteristic pattern data stream associated with the structural rigidity; constructing a rigidity influence factor sequence based on the characteristic mode data flow, and calculating a statistical characteristic quantity of the rigidity influence factor sequence through a sliding time window; performing multi-dimensional matching analysis on the statistical characteristic quantity and a historical reference database, and outputting a stiffness anomaly probability index; and activating a hierarchical verification mechanism according to the stiffness anomaly probability index, and confirming a stiffness change trend through a cross validation algorithm. Reliable data support is provided for bridge structure health condition evaluation.

According to an embodiment, a performance benchmark database is obtained, where the performance benchmark database at least includes structural data of one or more deep neural network models, time performance data and computing resource consumption data of a plurality of deep learning applications based on the one or more deep neural network models; a training dataset is extracted based on the performance benchmark database, where the training dataset has a plurality of parameter dimensions, the plurality of parameter dimensions including: structures of deep neural network models of the plurality of deep learning applications, resource configuration of the plurality of deep learning applications, and training time of the plurality of deep learning applications; and correspondence among the parameter dimensions of the training dataset is created so as to create an estimation model for estimating resources utilized by deep learning applications.

The invention discloses a welding robot self-adaptive wire feeding control method, a platform and a medium, relates to the technical field of welding automation process control, and is suitable for welding process management under complex working conditions. According to the method, an arc voltage sequence and a molten pool characteristic parameter sequence in the welding process are collected, a welding state characteristic value is obtained through calculation, a reference database containing welding process parameters, welding working condition parameters and the welding state characteristic value is constructed based on historical experimental data, and corresponding preset reference indexes are generated through a mapping relation function; and in real-time welding, the welding state characteristic value of the current wire feeding control period is obtained and compared with a preset reference index, the deviation value is calculated, the welding state is judged according to the grading threshold value interval, the wire feeding speed is dynamically adjusted, and self-adaptive matching of the wire feeding speed and the welding state is achieved. According to the method, instability caused by traditional adjustment depending on experience can be overcome, dynamic control over the welding process is achieved, and the stability of a welding pool and the welding seam forming quality are improved.

The invention provides a method for evaluating the crack resistance of tunnel body concrete, and belongs to the technical field of wind tunnels, and the method comprises the steps: uniformly arranging multiple types of sensors on the surface of a tunnel body concrete structure to form a high-density monitoring network, employing a concrete circular ring constraint test piece to establish an anti-crack reference database, and constructing a multi-working-condition temperature stress database; acquiring multi-dimensional data such as strain, temperature and acoustic emission in real time, inputting the monitoring data into a liquid adaptive neural network model to predict crack initiation probability and expansion risk, establishing a crack risk assessment function, formulating a corresponding processing strategy according to a risk coefficient range, and starting an intelligent spray maintenance system; a closed-loop control system from monitoring perception to risk early warning to active protection is formed, and the technical problem that crack initiation and expansion risks of a hole body concrete structure cannot be accurately predicted under complex working conditions is solved.

The invention discloses a processing method and device for sensing the position of an elevator, and relates to the technical field of elevator position sensing, which comprises the following steps: running in a full stroke under no-load and rated load conditions respectively, recording pulse signal data of a magnetic induction sensor when the elevator stops on each floor and the maximum value of a pulse signal when the elevator passes through each floor, establishing a dual-mode reference database; elevator regular no-load running full-stroke detection is carried out, and elevator real-time stopping deviation is analyzed; when the elevator is powered off or breaks down, counting of the pulse counters corresponding to the current top sensor and the current bottom sensor is obtained, elevator power-off or fault stopping floors are determined, elevator power-off or fault stopping deviation is analyzed, elevator power-off or fault actual stopping deviation is obtained after elevator real-time stopping deviation is subtracted, and the position of the elevator is determined. And the elevator trapped floor and the position of the elevator trapped floor in the hoistway can be quickly positioned, the response time is shortened, blind operation is avoided, and the secondary accident risk is reduced.

The invention relates to the technical field of unmanned aerial vehicle navigation control, and discloses an unmanned aerial vehicle visual navigation and positioning method, system and related equipment, and the method comprises the following steps: obtaining an image in real time; performing joint correction on the current image, and determining a corrected image; high-dimensional semantic features in the corrected image are obtained, and a candidate area is rapidly and roughly matched from the reference database; performing cross-modal comparison on the depth local feature of the corrected image and the depth local feature of each candidate region to determine a target region; and determining current positioning information of the unmanned aerial vehicle based on the target area and the attitude data of the unmanned aerial vehicle, and performing visual navigation based on the current positioning information. According to the method, the matching range is narrowed through joint correction and rapid rough matching, secondary matching is performed on the narrowed range through cross-modal matching, so that the positioning precision is improved, and finally, the matched target area is combined with the flight data (attitude data) of the unmanned aerial vehicle, so that the positioning precision is further improved.

The invention relates to the field of pollutant monitoring, in particular to an indoor decoration harmful substance monitoring and early warning method and system based on multi-sensor analysis. The method comprises the following steps that decoration whole-process parameter collection is carried out based on multiple sensor nodes, regional parameter distribution fitting is carried out, and an all-weather monitoring reference database is constructed; performing air pollutant identification marking on the all-weather monitoring reference database, and performing multi-time-point concentration change evolution analysis to obtain a multi-time-point concentration distribution evolution model; carrying out dynamic pollutant diffusion mining on the multi-time-point concentration distribution evolution model, carrying out combined pollution evaluation calculation, and constructing a multi-pollutant combined diffusion risk chain; dynamic interpolation modeling is carried out on the multi-time-point concentration distribution evolution model, micro-scale space-time disturbance reconstruction is carried out, and a pedestrian disturbance space-time diffusion model is constructed. According to the invention, through accurate and efficient risk assessment of indoor decoration harmful substances, dynamic early warning is carried out, and the safety of indoor decoration is improved.

The invention relates to the technical field of drilling machine monitoring and control, and discloses a state monitoring method and system for a drilling machine, and the method comprises the steps: detecting the stratum hardness in real time when the drilling machine is started, comparing the stratum hardness with data in a reference database, and judging the state of the drilling machine; if the state is abnormal, the contact condition of the drill bit and the stratum is checked, and if not, the drilling strategy is adjusted; if the environmental data is normal, checking whether the drilling machine is abnormal, and determining an early warning level according to the abnormal condition; and if the drilling machine is normal, updating the real-time hardness data into the reference database. According to the state monitoring method and system for the drilling machine, the working efficiency of the drilling machine can be improved, poor stratum contact is reduced, the environmental adaptability is enhanced, the safety is improved, reference data management is optimized, and the practical value is remarkable.

The invention discloses a heart tension index dynamic calculation and risk assessment method based on HRV, and the method comprises the steps: carrying out the filtering and denoising of an electrocardiosignal through a Butterworth filter, and obtaining a filtered electrocardiosignal; calculating interval data of adjacent wave crests; processing the interval data according to fast Fourier transform to obtain frequency domain features, and combining the interval data to obtain time domain features; performing normalization processing on the time domain and frequency domain features based on a maximum and minimum normalization method; calculating a cardiac tension index according to the normalized features and a pre-trained multiple linear regression model; matching a pre-constructed reference database according to the individual age, gender and body mass index to obtain a reference cardiac tension index; calculating a cardiovascular risk score in combination with the normalized frequency domain features and a reference cardiac tension index; and repeating the above steps regularly, smoothing through a moving average method based on continuous multiple cardiovascular risk scores, and analyzing the trend to send out cardiovascular risk early warning information.

The invention discloses a cross-basin water transfer project scheduling scheme evaluation method and system based on a cloud model, and the method comprises the steps: data preparation: collecting scheduling scheme data, and carrying out the normalization processing, and obtaining a reference database; standard cloud construction: defining a comment set, calculating standard cloud digital features and generating a standard cloud; comprehensive cloud construction: evaluating the scheduling scheme by using an expert scoring method, obtaining expert scoring data, calculating through a reverse cloud generator to obtain an evaluation cloud, determining a weight coefficient of an evaluation index by using an entropy weight method in combination with the reference database, and generating a comprehensive cloud; and scheme evaluation: establishing quantitative association of the standard cloud and the comprehensive cloud through dynamic scheme mapping, and determining a scheduling scheme evaluation grade through the similarity of the comprehensive cloud and the standard cloud. The method effectively processes the interactive influence of hydrological randomness and engineering regulation fuzziness, realizes qualitative and quantitative conversion, and improves the scheduling efficiency. The scheduling scheme can be evaluated objectively and scientifically, and a reliable basis is provided for a cross-basin water transfer decision.

The invention discloses a geotechnical engineering monitoring and early warning method and system, and relates to the field of geotechnical engineering, and the method comprises a construction module which is used for constructing an initial reference model for geotechnical engineering deformation instability monitoring according to geotechnical engineering design parameters and an initial structure state, extracting reference parameters, and storing the reference parameters to create a reference database; the sensing module is used for deploying a distributed sensing unit in a geotechnical engineering monitoring area, collecting real-time state data of a geotechnical structure, comparing the real-time state data with the reference model parameters in real time and outputting structure state deviation data; according to the method, real-time data is collected through a distributed hybrid sensing architecture, structural state deviation is accurately recognized by combining space-time coupling deviation calculation, multi-domain deviation characteristics are extracted, structural weakness is quantified, weak part orientation is defined, environment dynamic parameters and operation loads are fused to construct a risk coupling model, and the risk coupling accuracy is improved. And quantifying an instability risk coupling degree by using a dynamic index.

The invention provides an auxiliary rock core geological logging method based on drilling parameters, and relates to the technical field of geological investigation, the method is applied to a logging control terminal, and the method mainly comprises the following steps: obtaining regional typical rock core samples and five types of drilling parameter signals, classifying and extracting static characteristics of the rock core samples, and carrying out parallel noise reduction analysis on the parameter signals; obtaining a matching relationship between the rock core and the parameter signal, and constructing a lithologic parameter bidirectional association reference library; on the basis of the matching relation of the lithologic parameter bidirectional association reference library, the model is embedded into an incremental learning module to update the weight, and a subsection lithologic pre-judgment result is output; and calling a preset catalog template to load a pre-judgment result, generating a catalog report after man-machine interaction recheck and correction, collecting correction data, reversely transmitting the correction data back to the reference library to update a matching relationship, and triggering incremental learning of the model to complete a closed loop. The technical problems that in traditional rock core logging, regional lithology adaptation is poor, parameter interference is large, a model is not dynamically optimized, the deep operation risk is high, and efficiency is low are effectively solved.

The invention provides a ship tail abnormal noise source separation method and system, and the method comprises the steps: employing a ship noise reference benchmark database, combining ship water navigation features, and sequentially collecting noise source correlation features when a ship is in an independent start-stop state, a steady state working condition and an unsteady state working condition of each main and auxiliary equipment; performing preset combined analysis of total-level contrastive analysis, 1 / 3oct frequency band analysis, line spectrum analysis, DEMON spectrogram analysis, frequency multiplication analysis and LOFAR spectrum analysis on the noise source correlation characteristics to obtain a plurality of ship tail abnormal noise source correlation results; and determining a noise comprehensive proportion of a plurality of ship tail abnormal noise source correlation results, and generating a ship tail abnormal noise source separation analysis result. According to the method, the problems of incomplete testing, inaccurate positioning and unclear abnormal reason analysis in the ship tail abnormal noise processing process can be solved, and technical support is provided for formulating an abnormal noise troubleshooting and repairing scheme.

The invention relates to the technical field of building construction, and discloses an attached vibrator vibration parameter matching decision-making system, which comprises a perception execution dual-mode vibrator network, a vibration acoustic fingerprint generation and analysis module, a fingerprint evolution time sequence inference module and a multi-target dynamic decision-making engine, generating a vibration acoustic fingerprint formed by multi-dimensional physical characteristics according to the response signal; the method comprises the following steps: constructing a fingerprint evolution sequential sequence, comparing the fingerprint evolution sequential sequence with a reference database, deducing the performance of a concrete material on line, and further calling a mapping function matched with the performance to evaluate a real-time compactness state; and finally, based on the evaluated compactness, the multi-target dynamic decision engine adopts a model prediction control strategy to generate an optimal dynamic operation instruction for each vibrator node. Closed-loop self-adaptive control of detection, evaluation, decision making and execution is constructed, the optimal vibration parameters can be matched in real time, the vibration quality and uniformity of concrete are remarkably improved, energy consumption is reduced, and dependence on artificial experience is reduced.

The invention provides a credible manuscript review decision-making method, system and equipment and a readable storage medium. The method comprises the following steps: receiving a manuscript to be reviewed, analyzing a document structure of the manuscript to be reviewed, and identifying a paper type; based on the identified paper type, calling a data processing agent, extracting a performance index corresponding to the paper type from at least one of a text, a table and a chart of the to-be-reviewed document, and converting the performance index into standardized structure data; traversing a preset database, comparing the data in the reference database with the structured performance indexes, and determining the similarity and the relative sequence of the performance indexes of the to-be-reviewed manuscript and the data in the database; and based on the similarity and the ranking percentage, outputting a quantitative score of the to-be-reviewed manuscript and the confidence of the quantitative score. Compared with the prior art, the method has the advantages that the professionality, objectivity and credibility of review are remarkably improved; and interference caused by training data prejudice and text subjective description is effectively reduced.

The invention discloses a digital transformation maturity evaluation system and method based on a multi-dimensional quantitative index in the technical field of building industry digitization. The method comprises the following steps: acquiring various system data of an enterprise; the method comprises the following steps of: normalizing original data of sub-indexes of each dimension in data of each system of an enterprise through an evaluation engine deployed in a cloud server, weighting and aggregating, and calculating a comprehensive maturity index; calculating a dynamic evolution rate by using an evaluation engine in combination with historical comprehensive maturity index data, an enterprise resource investment ledger and an industry reference library; calculating bottleneck factors by using an evaluation engine in combination with enterprise self-evaluation data and an industry benchmark database; the enterprise bottleneck dimension is evaluated based on the bottleneck factor and then an improved scheme is fed back, the enterprise efficiency level is evaluated based on the dynamic evolution rate and then resource investment is adjusted, and the enterprise maturity is evaluated based on the comprehensive maturity index and then a stage route map is generated. According to the invention, the digital level of the building enterprise can be scientifically diagnosed, and transformation path planning is guided.

The present invention relates to the technical field of data processing, and relates to a method for trusted management of supply chain carbon data based on blockchain. By deploying an edge computing gateway, the energy consumption of suppliers' production equipment is collected in real time. Among them, the suppliers' production data collects the energy consumption in real time through the edge computing gateway deployed on the suppliers' production equipment, combines the on-vehicle terminal to obtain the real-time operating parameters of the logistics transportation vehicle, integrates the material disassembly data in the recycling and processing link, and constructs a multi-source data collection system for the entire supply chain link. The blockchain network performs trusted processing on the data, calls the regional carbon emission factors in the industry benchmark database for deviation verification, and the intelligent contract engine executes audit decisions based on preset compliance rules. When it is detected that the energy consumption deviates from the benchmark threshold, a data correction process confirmed by multi-party signatures is triggered. The present invention realizes the trusted storage and dynamic optimization of carbon data throughout the entire link, and improves the accuracy of supply chain carbon emission accounting and the efficiency of cross-border audits.

The invention discloses a sky-base collaborative tower camera image high-precision positioning method, which comprises the following steps of: carrying out virtual squint projection on a satellite image based on a high-resolution satellite orthoimage of an area around a tower camera, and mapping the satellite image to a tower camera squint coordinate system; performing multi-scale phase fusion terrain enhancement feature extraction and cross-view matching on the satellite reference image and the tower camera image to obtain high-precision homonymy points and meter-scale control points; block adjustment and absolute positioning are carried out on a reference database image collected by a tower camera; and based on the positioned reference database, carrying out high-precision geographic positioning on the natural resource illegal behaviors. According to the method, high-precision positioning of the tower camera can be efficiently and automatically realized, the positioning efficiency of the tower camera image is remarkably improved, and protection of natural resources is facilitated.

The invention relates to the technical field of transformer winding fault detection, in particular to a preventive maintenance method and device for transformer winding faults. The method comprises the following steps: acquiring current signals of a three-phase winding of a transformer and vibration acceleration signals in different directions; obtaining an excitation-response spectrum matrix based on the current signal and the vibration acceleration signal, and constructing a health state vibration mode base; projecting the excitation-response spectrum matrix to a health state vibration mode base to obtain a vibration mode contribution coefficient vector; constructing a frequency-mode association graph structure according to similar conditions of different frequencies in a health state vibration mode base at the initial stage of equipment commissioning; endowing nodes of the graph structure with features to obtain winding structure state feature vectors, and establishing a reference database of each temperature interval; and matching the winding structure state feature vector and the vibration mode contribution coefficient vector in the current time period with a reference database, and determining a fault reason. According to the invention, the timeliness of transformer winding fault early warning is improved.

The invention provides a road bridge displacement monitoring method and system based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: obtaining an electrical vibration signal generated by a road bridge, forming an original vibration data set, packaging the original vibration data set, obtaining a packaged data package, carrying out the integrity verification, and obtaining a verified data package; according to a pre-stored bridge vibration measurement benchmark database, noise reduction and time sequence alignment processing are carried out on the verified electrical vibration signals in the verified data packet, a vibration sequence is generated, then the displacement trend is analyzed, a displacement trend data set is obtained, feature parameters are extracted, candidate displacement modes are extracted from historical displacement records according to the feature parameters, and the candidate displacement modes are stored in the bridge vibration measurement benchmark database. Generating a pattern recognition result; and comparing the displacement change parameter of the result with the vibration baseline range parameter list, and generating a structure safety early warning instruction containing an abnormal position and an abnormal degree, thereby realizing real-time and accurate monitoring and abnormal early warning of the road and bridge displacement.

The invention discloses a honey walnut kernel raw material two-dimensional quality screening system, and relates to the technical field of food quality detection, and the system comprises a raw material reference database module; a raw material two-dimensional detection module; a dynamic parameter calibration module; a multi-dimensional quality determination module; a reference data system of multiple production areas and multiple seasons is established through the raw material reference database module, basic indexes and interface interaction data are synchronously obtained in cooperation with the raw material two-dimensional detection module, and the problem that traditional detection only pays attention to the quality of a single raw material and ignores the combination quality of honey and walnut kernels is solved; meanwhile, the dynamic parameter calibration module calculates the comprehensive quality deviation and the equipment adjustment amount, self-adaptive optimization of detection parameters is achieved, insufficient precision caused by fixed parameters is avoided, and raw material quality abnormity can be recognized from multiple aspects of raw material basic attributes and interface interaction states by constructing a two-dimensional screening system of raw material basic quality and interface combination degree.

The application discloses a kind of project-oriented full life cycle intellectual property risk collaborative management method and system, it is related to project full life cycle management data processing technical field, the present application obtains the multi-source relevant data of each stage of target project full life cycle, constructs project intellectual property risk management and control benchmark database, completes data standardization processing and risk characteristic mapping, establishes the intellectual property risk grading management and control system covering project full stage, completes project full cycle risk identification, grade division and early warning by intellectual property risk intelligent identification model, issues risk disposal task to corresponding responsibility subject to realize multi-subject collaborative management, iteratively optimizes risk management and control strategy and outputs adapted management and control optimization scheme.The present application realizes project full life cycle intellectual property risk full coverage management and control, improves risk identification accuracy and multi-subject collaborative disposal efficiency, provides stable support for project intellectual property risk management and control.

The invention discloses a particle size channel correction method based on a capture efficiency evaluation system, and relates to the field of aerosol measurement, and the method comprises the steps: constructing a static box and a capture efficiency evaluation unit of an aerodynamic particle size spectrometer, and in a calibration state of the capture efficiency evaluation unit, carrying out the calibration of the capture efficiency evaluation unit; establishing a particle size channel reference database by measuring the monodisperse standard substance; the method comprises the following steps: preparing mixed aerosol of polydisperse test dust and monodisperse standard microspheres with known particle sizes, and respectively measuring particle size distribution original data of the mixed aerosol passing through the upstream and downstream of a cutter to be evaluated by utilizing a trapping efficiency evaluation unit; and inputting standard microsphere channel information in the particle size distribution original data of the particulate matters into a transfer learning model pre-trained based on a historical data set in the particle size channel reference database. According to the method, by analyzing the comprehensive correction quality factor calculated in the transfer learning model, quantitative evaluation of the reliability of the correction process is realized, and black box operation is converted into transparent and measurable quality control.

This invention discloses a method and system for real-time prediction of laser strike effects for rapid deployment. The system includes: defining standard reference environmental parameters, calibrating standard attenuation coefficients and standard power-to-target (PTO) values; constructing a benchmark database; collecting target environmental parameters and calculating the power attenuation ratio between the target environmental parameters and the standard reference environmental parameters; calculating the PTO power under the target environmental parameters based on the power attenuation ratio, and calculating the PTO power density based on the PTO power; finding the benchmark minimum effective power density of the target target material based on the benchmark database; and, if the PTO power is greater than or equal to the benchmark minimum effective power density, estimating the estimated penetration time of the target target material based on the PTO power, the power density at which the target material penetrates the target material under the standard reference environmental parameters, and the penetration time. This invention features extremely low computational complexity, fast response speed, and guarantees laser strike effects superior to empirical estimates.

This application discloses a visual laser composite monitoring method and system for disconnector switch contacts, relating to the field of power system monitoring technology. The method includes: establishing a corresponding reference database based on the target disconnector switch; acquiring real-time image information, real-time laser ranging information, and real-time control information of the target disconnector switch after completing the closing action, and analyzing these to determine if the switch is in place, obtaining closing determination information; and issuing a closing warning result based on the closing determination information. This application addresses the problem that existing disconnector switch position monitoring relies on external indicators and cannot intuitively and accurately determine the insertion depth of internal contacts. By combining visualization and laser ranging principles, it solves the difficulties of existing methods in determining whether the switch is in place and quantifying the insertion depth, aiming to fundamentally avoid power safety accidents caused by incomplete closing.

This application relates to the field of semantic analysis technology, specifically to a method and system for extracting audit anomaly semantics from unstructured data. The method includes: acquiring a benchmark graph and a benchmark database and their entity mentions; obtaining the strings of entity mentions based on their positions; acquiring an audit candidate set of strings from the benchmark database; determining a matching score based on the consistency of audit roles, and then filtering the candidate entity set; encoding the candidate entities, their audit role sets, entity mentions, and strings to obtain the probability distribution of entity mentions and candidate entities, and determining the audit role consistency score based on the probability distribution; classifying the association between entity mentions and candidate entities based on the overall magnitude of the audit role consistency score, and applying different audit rules to each category; and extracting audit anomaly semantics using the audit rules. This application improves the accuracy and efficiency of semantic extraction.

Methods, non-transitory computer readable media, and property analysis server devices are disclosed that generate a feature dataset including property and metric data for properties includes an address and an actual lease value. A machine learning model (MLM) trained on the feature dataset is selected from different types of MLMs. A determination is made that the selected MLM exceeds an accuracy threshold based on a cross-validation using predicted lease values. The property data is stored in a lease benchmarking database with the addresses replaced in with corresponding geographic coordinates and geohash values. The properties are associated in the lease benchmarking database with the predicted lease values. One of the predicted lease values is returned in response to a received lease pricing request that includes an address. The returned predicted lease value is identified in the lease benchmarking database based on a geographic proximity of the address to one of the properties.

The invention relates to the technical field of block chain power supply management, and discloses a distributed power supply transaction management method of block chain nodes. According to the method, historical normal power supply transaction data is collected to establish a reference database, and the transaction detection period duration is determined according to the data size of a real-time power supply transaction data set. And monitoring node transaction records in a detection period, calculating a transaction frequency average value as a detection threshold value, and identifying suspected abnormal transactions in combination with the reference database. After the suspected abnormal transaction is confirmed, extracting the total transaction amount and the time distribution characteristic to judge whether the transaction is the abnormal transaction, and counting the occurrence frequency to calculate an initial risk coefficient; and the category of the abnormal transaction, the participation node identifier and the behavior pattern are extracted and converted into feature vectors, and malicious transaction node groups are grouped, analyzed and detected through a neural network classification algorithm. And if the malicious group exists, calculating an adjustment coefficient to correct the initial risk coefficient, and dynamically adjusting parameters of the next detection period.