106 results about "Multi dimensional analysis" patented technology

The invention relates to the field of power system planning, in particular to a micro-grid boundary quantitative evaluation method and system for multi-dimensional analysis and dynamic verification. The method comprises the steps that power distribution network and micro-grid scheme data are acquired, and scene recognition and decoupling modeling are carried out after standardization processing; the method comprises the following steps: extracting end supply-preserving scene data, analyzing cost elements, and generating a critical cost threshold table through normalization processing and a threshold approximation algorithm; performing multi-dimensional parameter correlation analysis and integrated learning training based on the table, and constructing a multi-dimensional boundary index model; performing benefit matching calculation and green value accounting according to the result to generate an economic benefit decomposition structure; real-time streaming data processing and stability verification are combined to generate an economical efficiency boundary index set; and finally, generating a standardized evaluation file through matrix mapping and weight dynamic adjustment. According to the method, dynamic quantitative evaluation of the economy boundary of the micro-grid is realized, the capacity substitution benefit and the green value are effectively integrated, and an accurate basis is provided for planning decision.

The invention discloses a marine ecological protection and restoration project comprehensive evaluation method based on multi-dimensional analysis, and relates to the field of marine ecological analysis, and the method comprises the steps: obtaining multi-dimensional monitoring data through constructing a target region map model and setting a monitoring point, and generating an ecological feature vector; calculating a relative distance and a deviation between the monitoring point and the comparison feature vector to obtain a repair expected goodness of fit; screening monitoring points with low goodness of fit and integrating the ecological data set; training and generating an optimized simulation feature vector based on a GAN model; and analyzing the simulation parameters and evaluating the ecological evolution trend to complete comprehensive evaluation. According to the method, multi-dimensional and high-precision ecological assessment is realized, the accuracy of restoration effect prediction and the scientificity of an assessment result are improved, reliable support is provided for a marine ecological protection restoration decision, and the problems of one-sided monitoring, insufficient prediction and the like of an existing method are solved.

The invention discloses an automatic mural protection and repair area identification method and system based on image analysis, and the method comprises the steps: generating degradation states of a mural at different time nodes through an image simulation algorithm, converting a two-dimensional damage image into a three-dimensional space model through the combination of a three-dimensional mapping technology, and precisely calibrating a high-risk damage point. The clustering analysis algorithm further integrates and disperses damage points, the property and spatial features of a damage group are extracted, and the curve feature detection algorithm automatically identifies damage boundaries by analyzing texture continuity and line trend. Dynamic sequence analysis tracks inter-frame differential change, a degradation extension track is disclosed, damage information is efficiently compressed and stored by a coding technology, and a matched degradation mode is mined and a potential damage area is updated by historical comparison analysis. According to the method, three-dimensional space modeling is taken as a core, multi-dimensional analysis and efficient storage are fused, accurate positioning, dynamic monitoring and preventive protection of mural damage are realized, and the scientificity and efficiency of cultural relic protection are remarkably improved.

The invention relates to the technical field of production quality control, and discloses a production data hierarchical processing and quality tracing system. The system comprises a production data acquisition module, a data hierarchical processing module, a quality fluctuation index module, a multi-dimensional quality difference analysis module and an abnormal source positioning module. The production data acquisition module acquires a real-time parameter data sequence of each production link; the data layering processing module is used for layering the data into a plurality of data hierarchies; the quality fluctuation index module obtains a quality fluctuation index based on the fluctuation condition of the data hierarchy; the multi-dimensional quality difference analysis module analyzes the multi-dimensional quality difference based on the index; and the abnormal source positioning module positions a production abnormal source based on the multi-dimensional quality difference. According to the system, through hierarchical data processing, quality fluctuation quantification and multi-dimensional difference analysis, accurate positioning of a production abnormal source is realized, and systematicness and accuracy of production data processing and quality tracing are improved.

The invention provides a dam safety monitoring data analysis method and system based on a multi-modal knowledge graph and a large language model, and belongs to the technical field of calculation models. The method comprises the following steps: collecting multi-modal data of dam monitoring, and preprocessing the multi-modal data; constructing a dam safety analysis knowledge graph by adopting a water conservancy object multi-modal knowledge graph construction mode; an intelligent positioning function is used for finding an associated object for the monitored abnormal signals, information of the associated object is obtained, and a dam analysis model is used for association analysis; generating an abnormal associated sub-graph; and forming a multi-dimensional analysis report conclusion by using a pre-trained water conservancy professional knowledge large language model in combination with cue words and user questions. Multi-aspect information generated in the dam safety management process is integrated by utilizing the knowledge graph to form a comprehensive information network, efficient query and reasoning are realized by virtue of a graph computing technology, and a monitoring analysis result for explaining the dam is clearly, intuitively, accurately and comprehensively presented.

The invention provides a method for predicting the working failure rate of a railway vehicle relay, and the method comprises the steps: classifying data according to a preset dimension to generate a structured feature set, constructing a time sequence feature matrix, and defining a multi-dimensional analysis coordinate system based on the classification dimension of the feature set; determining an initial prediction constraint condition according to the relay life distribution model and the fault mode library, executing dynamic prediction simulation on the feature set based on the time sequence feature matrix, simulating degeneration behaviors of the relay under various working conditions through a state transition simulation engine, generating an initial fault rate curve, and calculating the fault rate of the relay according to the initial fault rate curve. And finally, carrying out iterative correction on the initial fault rate curve by adopting an integrated optimization algorithm to generate a multi-dimensional fault rate prediction model. The invention further provides a system for predicting the working failure rate of the railway vehicle relay. According to the method, the adaptability and accuracy of prediction are greatly improved, and the degeneration behavior of the relay under the actual complex working condition can be simulated more truly.

The invention relates to a multi-modal data fusion 3C assembly process quality accurate prediction method, and belongs to the field of 3C intelligent manufacturing, and the method comprises the steps: carrying out the multi-modal data fusion, and forming a unified feature space containing process parameters and quality characterization; performing multi-dimensional analysis on process quality, performing hierarchical analysis on structured process parameters and unstructured images, and establishing cross-modal feature mapping; predicting the process quality, evaluating the contribution degree of process parameters to the quality by utilizing an integrated learning model based on a fused feature set, screening key parameters to reduce the complexity of the model, dynamically adjusting the prediction model, designing a'sliding window + multi-dimensional index 'monitoring system, and evaluating the model performance and the data distribution drift in real time. Establishing a time-performance-distribution three-dimensional trigger mechanism, and starting an adjustment strategy according to different priorities; a'cleaning-complementation-mechanism-mapping-self-adaption 'closed loop is realized, and the timeliness and accuracy of 3C assembly quality prediction are remarkably improved.

The invention relates to the technical field of large models, in particular to a reading recommendation large model generation method and system based on big data resources. The method comprises the following steps: acquiring a to-be-assessed text, collecting reading materials with multiple learning segments and multiple styles, labeling the reading materials, carrying out aggregation and consistency verification on labeling results, and constructing a verified recommendation database; performing low-rank adaptive fine tuning on the general pre-training large model based on the recommendation database to obtain a domain model, and performing performance and deviation monitoring and correction in the training process; performing multi-dimensional analysis on a to-be-evaluated text to generate a text feature vector, and performing mixed retrieval on a recommendation database in combination with semantic vector similarity retrieval; according to the invention, through the construction of the large reading recommendation model, the reading recommendation is more accurate and efficient.

The invention discloses a data aggregation processing method and device, computer equipment and a storage medium, belongs to the technical field of big data, and is applied to data aggregation in the financial field. The method comprises the following steps: firstly, grouping dimension fields by utilizing a mapping rule and a hierarchical clustering algorithm; and secondly, dynamic grouping mapping is established, one-time scanning is performed on the source data table, and each data record is directly attributed to a corresponding grouping structure, so that the data processing efficiency is improved. Meanwhile, a correlation index of the index and the data partition is constructed based on a Hash mapping algorithm. Under the support of an indexed data structure, the aggregation calculation function can efficiently execute operations such as summation and averaging, and rapid aggregation of multi-dimensional data is realized. Generally speaking, according to the method, the correctness of an aggregation result is ensured, meanwhile, the calculation and storage pressure of the system is remarkably reduced, the timeliness and expandability of data analysis are improved, and the method is particularly suitable for real-time or quasi-real-time multi-dimensional analysis requirements in a large-scale business scene.

The invention relates to an electric power resource adjustability resource multi-dimensional analysis and visualization method, which comprises the following steps of: selecting four dimensions of response time, service time, adjustable capacity and credibility in electric power resource adjustability characteristic indexes as a rating system, and drawing an electric power adjustability resource adjustability characteristic radar map; and constructing a power adjustable resource characteristic cube by taking the power adjustable resource adjustment characteristic radar map as a bottom surface and taking the corresponding scale capacity as a high surface to represent the adjustable characteristic characteristics of the source network load power resources. Compared with the prior art, the multi-dimensional visualization of the adjustment performance of the power adjustable resource is realized by constructing the characteristic cube, the adjustable characteristic characteristics of the source network load power resource can be comprehensively and accurately represented, and a large number of, multiple and dispersed flexible resources can be aggregated to participate in system adjustment.

The invention discloses a fund portfolio analysis method and device and a storage medium, and relates to the technical field of data analysis, and the method comprises the steps: based on fund product metadata, fund underlying asset data and market reference data, executing fund deep analysis including performance index analysis, position holding analysis and attribution analysis, and obtaining a deep analysis result; based on the user fund portfolio position data and the deep analysis result of each fund product, performing combination-level multi-dimensional analysis on the fund portfolio to generate an overall analysis result of the fund portfolio; according to the fund portfolio analysis method, multiple target expert agents corresponding to the fund portfolio are determined, a preliminary analysis report output by each target expert agent is obtained, the preliminary analysis reports are integrated and summarized, a corresponding fund portfolio analysis report is generated, and the technical problem that an existing fund analysis method is lack of intelligent integration due to data piling is solved. Depth, integration and generalization of fund portfolio analysis are realized, and a customized analysis report with a low interpretation threshold is provided for a user.

The invention discloses an unmanned vending equipment state monitoring method and system based on streaming and batch integration, and the method comprises the steps: collecting the multi-dimensional state data of equipment through a data collection layer, and carrying out the unified structural processing based on a data model; the streaming processing layer acquires a structured equipment state event stream from a Kafka data source, performs real-time processing through timestamp allocation, data grouping and a window aggregation strategy, and calls a multi-dimensional health scoring algorithm to calculate a real-time health score; the storage layer adopts a streaming-batch integrated architecture, stores the data to corresponding data tables respectively, and meets the requirements of real-time query and historical analysis; the analysis query layer performs data aggregation calculation based on the aggregation table to obtain an analysis result; and the intelligent decision-making layer calls an anomaly detection algorithm to carry out anomaly judgment, and equipment health assessment and predictive maintenance are realized. The problems that an existing monitoring system is insufficient in real-time processing, poor in storage adaptability, lack of intelligent prediction, weak in multi-dimensional analysis and limited in expansibility are effectively solved.

This invention relates to a method, apparatus, and medium for automatically generating test cases by integrating multi-source human programming experience. The method includes: a preparation step: based on a target programming problem P, searching for existing correct human programming code for the target programming problem P, as well as human error submission code and human correction code for each error submission; a multi-dimensional analysis step: obtaining a first analysis result generated by a large language model; a difference analysis step: obtaining a second analysis result generated by the large language model; and a test case generation step: based on the first and second analysis results, generating executable test script construction prompts to obtain a test script generated by the large language model, and running the script to generate test cases for the programming problem P. Compared with existing technologies, this invention has advantages such as significantly improving the discriminative power of test cases.

The invention provides a method and device for carrying out cost optimization prediction through multi-dimensional business travel behavior analysis, and belongs to the technical field of enterprise business travel management.The method comprises the steps that business travel data are collected, cleaning, duplicate removal and entity alignment operation are carried out, and a business travel behavior data set is constructed; executing a preset query script on the business travel behavior data set, extracting characteristic indexes from preset dimensions, executing descriptive analysis, diagnostic analysis and clustering analysis, and generating a business travel behavior analysis report; and inputting the business travel behavior data set and the business travel behavior analysis report into a cost optimization prediction model to carry out business travel demand prediction, business travel price prediction and business travel travel optimization, generating an optimal travel travel scheme set, pushing the optimal travel travel scheme set to the employee user, collecting a feedback result of the employee user on the optimal travel travel scheme set, updating the business travel behavior data set, and obtaining a business travel result. And incremental training and iterative optimization are carried out on the cost optimization prediction model. Through multi-dimensional analysis and prediction, the travel cost is optimized, and the management efficiency is improved.

The application relates to the technical field of direct current transmission systems, in particular to a direct current transmission system operation risk optimization method based on multi-dimensional analysis, wherein the direct current transmission system comprises a rectifier and an inverter, and the specific method comprises the following steps: performing harmonic interference risk assessment on the rectifier; performing power quality risk assessment on the inverter based on the result of harmonic risk control optimization; calculating inverter risk quality indexes based on the result of the power quality risk assessment; and performing inverter risk compensation optimization based on the inverter risk quality indexes. The application realizes the risk assessment of harmonic interference in the rectification process and the power quality risk assessment of the inversion process after interference control optimization, and further realizes more accurate assessment of the operation risk of the direct current transmission system, effectively solving the problem that the harmonic interference risk in the rectification process and the power quality risk in the inversion process are insufficiently related in the prior art.

This invention discloses a multi-dimensional analysis system and method for regional economic development potential, aiming to solve the problems of scattered data, single dimensions, and subjective and lagging assessments in traditional regional economic analysis. It adopts a three-layer architecture: a data layer, a model layer, and an application layer. The data layer integrates multi-source data from statistical yearbooks, government information disclosure, the internet economy, and geospatial data, forming a unified analytical foundation through standardization and outlier processing. The model layer constructs a six-core dimension indicator system, including economic scale and industrial structure, and uses entropy weighting, TOPSIS, and regression analysis to achieve quantitative assessment, supporting dynamic updates and adjustments. The application layer outputs analysis results through visualization and customizable functions. This invention achieves deep integration of multi-source data, comprehensive characterization of multi-dimensional potential, and objective, accurate, and timely assessment results, adapting to various scenarios such as government decision-making and corporate investment, providing scientific and efficient decision support for high-quality regional economic development.

The application discloses a feature warehouse feature value monitoring method and system, relates to the technical field of feature warehouse monitoring, and comprises the following steps: analyzing historical feature warehouse record data, and acquiring the review label of each historical feature value group; the label records the situation of the feature value group and explains; multi-dimensional feature performance analysis is carried out on the historical feature value group, different dimensional feature performances are determined, a multi-dimensional feature performance group is formed, and a feature performance baseline model is constructed based on the group and the corresponding review label, which is used for representing the normal performance mode of the historical feature value; real-time multi-dimensional analysis is implemented on a real-time feature value group, data comparison is carried out with the baseline model output data, a feature deviation index is calculated, and data anomaly early warning is sent for the real-time feature value group with the deviation index greater than or equal to a preset value; and the application realizes automatic monitoring of feature data, and improves the robustness and abnormal detection precision of a machine learning pipeline.

The invention relates to the technical field of power grid project cost management and business intelligence, and discloses a project cost multi-stage intelligent analysis and examination method, system, equipment and medium, and the method comprises the steps: building a power grid project cost data warehouse, and achieving the integration and standardization of multi-source cost data; establishing an OLAP multi-dimensional analysis model, and supporting general cost comparison and key index analysis in a budget calculation stage, feasibility research estimation, preliminary design budget calculation, completion settlement three-calculation comparison, first supply material check and review list consistency check in a settlement stage; designing an intelligent decision engine based on data mining, and generating review opinions through association rules, a classification algorithm and an anomaly detection technology; and a BI visual instrument panel is developed, and multi-dimensional display, real-time monitoring and interactive analysis of the power grid cost data are realized. Through systematic application of the BI technology, deep mining, intelligent decision and visual display of engineering cost analysis are realized, and the efficiency and precision of cost review are remarkably improved.

The invention relates to a data analysis technology, and discloses a city governance data quality multi-dimensional analysis method, device and equipment, and the method comprises the steps: obtaining a to-be-analyzed data block, carrying out the quantitative analysis of the to-be-analyzed data block based on preset data dimensions, and obtaining a standardized sub-score corresponding to each data dimension; obtaining a performance feedback index of the downstream large model, dynamically adjusting the distribution weight of each data dimension by using a predefined mapping rule base according to the performance feedback index, and performing normalization processing on the adjusted weight to obtain a normalized weight of each data dimension; performing weighted summation on the standardized sub-score of each data dimension and the corresponding normalized weight to generate a final comprehensive quality score of the to-be-analyzed data block; and obtaining a quality multi-dimensional analysis result according to a preset quality partition interval in which the comprehensive quality score is located, and distributing the to-be-analyzed data block to a corresponding processing path for data processing. According to the invention, the efficiency of quality multi-dimensional analysis of urban governance data can be improved.

The invention discloses a scene multi-dimensional reconstruction method fusing data operation and space intelligence, and relates to the technical field of space-time semantic reconstruction, and the method comprises the steps: fusing a panoramic image, business data and a spatial point cloud, and constructing a semantic enhancement three-dimensional point cloud and a business process knowledge graph; and marking a space operation area corresponding to the service node in the point cloud under the guidance of the map. And coding the node timestamps and the service states into time-varying attribute vectors of the spatial points, and reconstructing to generate a dynamic three-dimensional space-time voxel field. And a knowledge graph topology is embedded in the voxel field to form a space-time semantic field, and multi-dimensional subdivision is performed to obtain associated slices of space, time and business dimensions, so that a scene multi-dimensional reconstruction model is constructed. According to the method, unified representation of service logic, physical space and time evolution is realized, and multidimensional analysis and insight based on a world model are supported.

The invention discloses a fruit tree pest recognition method and system based on image recognition in the technical field of image recognition and agricultural pest detection, and the method comprises the steps: 1, collecting an original image, environment illumination intensity and humidity data of the surface of a fruit tree, and correcting the original image; 2, identifying the position of a hole in the original image, performing segmentation extraction on a specific area where the hole is located, performing image preprocessing to obtain a judgment image, and respectively extracting the shape feature of the hole and the color and texture features of powder around the hole; and step 3, judging whether the hole is a worm hole or not, and if so, judging the type of the fruit tree pest and disease damage. According to the method, the accuracy and efficiency of identifying the pests in the tree trunk are improved by constructing the multi-dimensional analysis system of the hole and powder features based on image identification, and technical support is provided for precise pest control.

The invention belongs to the technical field of talent management development, and relates to a staged progressive talent growth path planning method and system, and the system collects and integrates the static and dynamic data of talents through a talent portrait construction module to generate an initial talent portrait; the growth stage division module divides progressive growth stages and sets capability thresholds based on the initial talent portraits and the organization development objectives; the stage target setting module is used for disassembling a stage target into quantifiable sub-target schemes with time limit; the resource matching and pushing module is used for matching resources according to the target and the portrait and automatically pushing the resources to the talent end; the capability evaluation feedback module is used for evaluating a stage target achievement condition and generating an evaluation report; the path dynamic adjustment module progressively or triggers path adjustment based on the evaluation report judgment stage; and the data storage and analysis module is used for storing the whole-process data, carrying out multi-dimensional analysis and optimizing parameters. According to the invention, personalized planning, dynamic adjustment and whole-process closed-loop management of talent growth paths can be realized.

The invention provides an anti-money laundering intelligent monitoring method and device and electronic equipment, and aims to solve the problem that the false report and missing report rate of an anti-money laundering monitoring system is relatively high. The method comprises the following steps: acquiring original data from a plurality of data sources with different formats, and preprocessing the original data to obtain preprocessed data; calculating a normal behavior baseline value according to the multi-dimensional features of different account addresses in the preprocessed data, and determining an anomaly detection threshold according to the normal behavior baseline value; obtaining block chain transaction data, and screening out suspicious transaction data from the block chain transaction data according to an anomaly detection threshold; determining a structured transaction graph according to the suspicious transaction data; and calling a multi-dimensional analysis tool through a large language model LLM planner to generate an anti-money laundering risk assessment report according to the suspicious transaction data and the structured transaction graph.

The invention discloses a method for rapidly completing competitive product advertisement analysis based on an AI model, and relates to the technical field of competitive product advertisement data processing. The method comprises the following steps: acquiring competitive product advertisement video data, extracting multi-modal information in the competitive product advertisement video data, and filling a predefined structured template with the multi-modal information to form a preliminary analysis result; dynamically outputting a confidence score of each key field in the preliminary analysis result based on a confidence evaluation parameter, and carrying out dynamic sample marking based on the confidence scores to realize data distribution processing; performing multi-dimensional analysis on the advertisement style obtained by dynamic evaluation in combination with the putting time and channel source information of the advertisement, and dynamically generating a putting customer trend report; and combining the field data subjected to man-machine collaborative auditing processing with the high-confidence field to generate a final precise structured data set, and generating a visual analysis report based on the data set and the on-investment customer trend report.

The invention relates to the technical field of data life cycle management, and discloses an insurance coupon life cycle data tracking management system, which comprises a data acquisition module, a data processing module, a tracking monitoring module, a risk assessment module, a report generation module and an optimization management module, multi-source coupon data are collected in real time through a life cycle event recording unit and a distributed collection unit in the data collection module, and a data format is standardized by using a data preprocessing unit, so that the problems of information loss and distortion caused by transmission delay and interface abnormality can be discriminated in real time, the accuracy and integrity of coupon state data are ensured, and the data processing efficiency is improved. According to the method, data acquisition errors are reduced, external environment variables and user behavior data are integrated in combination with a multi-dimensional analysis unit, risk model parameters are dynamically updated by utilizing an adaptive learning unit of an optimization management module, the card and coupon risk levels can be dynamically corrected in real time in combination with change factors, and timeliness and accuracy of risk assessment are guaranteed.

The invention relates to the technical field of grounding system conduction, in particular to a grounding conduction measurement and control integrated tester which comprises a conduction response module, a current density module, a neural learning module, a multi-wave excitation module and a feature optimization module. According to the invention, alternating current excitation of different amplitudes is applied under the same frequency and frequency domain decomposition is carried out, so that multi-dimensional analysis and feature extraction of conduction response are realized, the conduction drift trend is identified in multi-time-period current density comparison, deep feature correlation learning is carried out, excitation parameters are adaptively corrected according to phase difference and response features, and the current density comparison accuracy is improved. And response delay and change rate information under multi-waveform excitation are fused to form a conduction measurement result with more time domain and frequency domain characteristics, so that the accuracy and stability of conduction state recognition are improved, and dynamic quantitative judgment on continuity and safety of a grounding loop is realized.

The invention relates to a stock average line similar mode searching method. The method comprises the following steps of 1, extracting and preprocessing data; 2, establishing a feature model; step 3, calculating a single mean line feature; step 4, multi-mean line feature calculation; step 5, feature similarity calculation; and step 6, feature result weighted fusion. The method has the advantages that the multi-dimensional analysis system covering the single-mean-line form, the data features and the multi-mean-line cross features is constructed, and the weighted fusion strategy and the deep cross mode analysis model are combined; compared with a traditional method, the influence of the problems that single feature analysis robustness is poor, multi-feature integration is insufficient, cross mode mining is shallow and the like is effectively relieved. Experimental verification on stock historical average line data shows that by means of the searching method, the similarity degree of different stock average line systems can be quantified more accurately and comprehensively, and the accuracy and practicability of similar fragment searching are further improved.

The invention discloses an engineering monitoring project automatic report generation method and system. The method comprises the following steps: establishing a standardized report template library taking the type of a measuring instrument as a benchmark; a multi-dimensional analysis model established based on an analytic hierarchy process and a fuzzy comprehensive evaluation method is utilized to carry out automatic safety risk grade evaluation on actual monitoring data; a safety risk assessment grade result is automatically extracted, early warning statistics and early warning disposal suggestions are generated, and trend prediction is carried out on monitoring data; and integrating the acquired actual monitoring data and the processing result, and automatically filling a corresponding standardized report template to generate a complete structured report. According to the invention, automation and intelligentization of the whole process from collection, processing and analysis of monitoring data to report generation are realized, informatization construction management can be effectively guided, security risk dynamic control and grading early warning decision based on real-time data are supported, and report generation efficiency, data analysis accuracy and security management intelligentization level are significantly improved.

The invention discloses a pigging period monitoring method and system based on pipeline effusion and hydrate, and the method comprises the steps: collecting data, and carrying out the preprocessing of the data, and obtaining a data set; performing multi-dimensional analysis on the data set, and extracting data features; constructing a pigging period prediction model based on the data features; acquiring historical pigging data to train the pigging period prediction model; and constructing an expert scoring model to obtain an expert score, verifying and optimizing the trained pigging prediction model based on the expert score, and performing pigging real-time monitoring based on the optimized pigging period prediction model. The pipeline cleaning data is monitored in real time through the pipeline cleaning period prediction model to obtain task feedback data, the pipeline cleaning prediction model is verified and optimized in combination with expert scores, pipeline cleaning management is carried out based on the optimized pipeline cleaning period prediction model, monitoring and safety evaluation on the health state of a pipeline are more facilitated, and the pipeline cleaning efficiency is improved. And the accurate prediction period of the pipe cleaning operation is given.

The invention relates to the field of natural language processing, and discloses a Chinese language and literature text topic analysis system based on big data, and the system comprises a core semantic primitive library construction module which is used for extracting and constructing a knowledge base containing semantics, culture and timestamps from a meta-poeia corpus offline; the text multi-dimensional analysis module is used for extracting four-dimensional features of grammar, core semantics, intertext and emotion time sequence of the to-be-analyzed text in parallel; the feature collaborative analysis module is used for fusing multi-dimensional features, performing context dynamic activation on primitives, and innovatively quantifying and detecting deep semantic conflicts by calculating the dispersion of feature vectors in a semantic space; and the topic report generation module is used for synthesizing a macroscopic topic, integrating all analysis results and generating a structured multi-level report. The system can objectively and deeply reveal complex and even contradictory theme connotation in literature works through modular cooperation, and the accuracy and depth of text analysis are remarkably improved.