485 results about "Quality assessment" patented technology

The invention relates to the technical field of engineering safety monitoring, in particular to a foundation pit deformation intelligent early warning system based on multi-modal fusion and a method thereof.According to the system, quality evaluation and weighting processing are conducted on multi-modal sensor data through a self-adaptive weight dynamic distribution module, and the data credibility is ensured; the multi-modal feature cross extraction module extracts and interacts features by using a specific sub-network and a multi-head attention mechanism, integrates information through a space-time diagram convolutional network, and generates accurate fusion feature representation; the multi-granularity abnormal mode identification module is combined with a mixed density network and time sequence analysis to accurately identify deformation anomalies; the causal reasoning and weight feedback module analyzes deformation reasons through a causal graph model and provides feedback for sensor weight adjustment; according to the system, the precision and reliability of deformation detection are remarkably improved, the detection precision is improved to the millimeter level, the accuracy is improved by 40%, and powerful technical support is provided for engineering safety monitoring.

The invention provides a meteorological element three-dimensional analysis method combining remote sensing and ground observation, and relates to the technical field of meteorological monitoring, and the method comprises the steps: obtaining remote sensing and ground observation data, and obtaining meteorological element data of a unified space-time reference; dividing the data into space-time grid units and performing quality evaluation to obtain a quality evaluation index; calculating a fusion weight coefficient according to the quality evaluation index based on an adaptive fusion algorithm of a dynamic weight, and performing weighted fusion on the meteorological element data in the grid units to generate an initial three-dimensional meteorological field; performing scale decomposition and reconstruction by adopting a spectrum analysis method to obtain an optimal three-dimensional meteorological field, constructing a self-adaptive tree-shaped composite analysis grid, analyzing evolution characteristics of a weather system in a multi-layer progressive mode, determining topological evolution parameters, and outputting a three-dimensional analysis result of meteorological elements.

The invention discloses an AI-based data asset quality assessment and automatic repair method and system, and the method comprises the steps: receiving a to-be-processed target data asset, inputting the target data asset into a multi-dimensional joint quality scoring engine, and generating a comprehensive quality assessment report containing an integrity defect map, a distribution abnormality thermodynamic diagram and a semantic conflict link; based on the comprehensive quality evaluation report, utilizing a rule and learning fused strategy generator to analyze repair requirements and constraint conditions contained in a defect map, an abnormal thermodynamic diagram and a conflict link, and dynamically generating repair operation instruction sets and priority sequences for different types of defects; and inputting the target data assets, the repair operation instruction set and the priority sequence into a multi-expert collaborative repair execution module, repairing defects in the target data assets according to the priority sequence, and outputting the repaired data assets. According to the embodiment of the invention, comprehensive and accurate data quality evaluation can be realized, and the automation level and accuracy of data restoration are improved.

The invention discloses a casting surface treatment defect detection and quality evaluation method and system, and relates to the technical field of casting quality evaluation, and the method comprises the steps: collecting the surface data of a to-be-detected casting, and carrying out the data preprocessing, and obtaining a standardized input data set and a standardized data subset; calling a corresponding analysis sub-model for each subset, outputting quality features and confidence coefficients, and summarizing the quality features and the confidence coefficients into a sub-source result; environment state information is acquired to determine sub-model weights, and weighted fusion is carried out on sub-source results to obtain an evaluation result and an overall confidence coefficient; calculating space / feature / time consistency and judging according to a combination rule; when a re-checking condition is met, obtaining a re-checking label backflow updating data set, and training an updating model and parameters; and dynamically adjusting the threshold value and the weight value according to the performance index for subsequent evaluation. According to the method, multi-source data and environment information can be fused, weight self-adaption and closed-loop updating are parallel, accuracy and stability are improved, misjudgment and missed judgment are reduced, and complex working condition adaptability and long-term reliability are enhanced.

The invention discloses an academic paper review service management system based on an AIGC technology, and belongs to the technical field of paper review. The system comprises a paper import module, a format specification detection module, a content quality evaluation module, an academic atlas analysis module, an AIGC content detection module and a collaborative review module, and through a multi-dimensional evaluation model trained by historical review data, quantitative analysis can be performed on paper quality from core dimensions such as research design rationality and achievement innovation; an academic database is constructed in combination with a knowledge graph technology, so that transverse technical route comparison and longitudinal academic venation tracking of core arguments of papers can be realized, and innovation points and research gaps can be accurately positioned; a double-layer detection mechanism fuses traditional duplicate checking and AI generation content recognition, and the recognition problem of the AIGC text is effectively solved, so that a complete system covering paper quality evaluation, innovation point mining and academic integrity detection is formed, and the review depth and scientificity are comprehensively improved.

The invention discloses an AI-based prefabricated member multi-modal visual quality intelligent detection system and method, and the system comprises a multi-modal visual data collection unit which is used for obtaining the multi-modal visual data of a prefabricated member detection region, calibrating the multi-modal visual data, and constructing a multi-modal data set. And the feature fusion and candidate region extraction unit is used for inputting the multi-modal data set into a multi-modal feature fusion network to extract multi-modal features, fusing the multi-modal features and generating a defect candidate region. And the defect type identification and quantitative analysis unit is used for calling a deep learning detection and segmentation model to carry out defect type classification and defect boundary segmentation on the defect candidate region, and calculating a quantitative index of the defect region in the segmentation boundary by using the three-dimensional point cloud and the infrared image. And the defect grade judgment and component quality evaluation unit is used for carrying out dynamic threshold judgment on the defect area according to the defect type and the quantitative index of the defect area, and outputting the defect severity grade and the quality evaluation result of the prefabricated component.

The invention provides a multi-dimensional quality evaluation method, device and equipment for carbon footprint data and a storage medium, and the method comprises the steps: obtaining multi-source heterogeneous data of a product, the multi-source heterogeneous data comprising basic information of the product and carbon footprint related data of the whole life cycle of the product; generating carbon footprint data of the product based on the multi-source heterogeneous data; and performing multi-dimensional quality evaluation on the carbon footprint data based on a pre-constructed carbon footprint knowledge graph, a time dimension detection model, a space dimension detection model and a cross-dimension detection rule. According to the invention, the carbon footprint knowledge graph, the time dimension detection model, the space dimension detection model and the cross-dimension detection rule are introduced to realize semantic, time, space and cross-dimension multi-dimensional quality evaluation of the carbon footprint data, and the detection dimension can cover the multi-dimensional features required by the carbon footprint calculation. Therefore, it is ensured that the carbon footprint data quality evaluation result can reflect the influence on the carbon footprint calculation reliability more accurately.

The invention provides a carbon footprint calculation-oriented multi-source heterogeneous data intelligent fusion method and device, and the method comprises the steps: obtaining the multi-source heterogeneous data of a product, the multi-source heterogeneous data comprising the basic information of the product and the carbon footprint data of the whole life cycle of the product; analyzing the data type of the multi-source heterogeneous data, and selecting an algorithm matched with the data type to convert the multi-source heterogeneous data into structured data to obtain a structure standardized data set; based on the loaded carbon footprint domain ontology, performing semantic extraction on the structure standardized data set to obtain a semantic standardized triple data set; performing quality evaluation on the semantic standardized triple data set, and constructing an initial carbon footprint data set including quality information based on a quality evaluation result; and performing conflict detection and conflict ablation on the initial carbon footprint data set to obtain a final carbon footprint data set of the product. Through the method disclosed by the invention, effective fusion of multi-source heterogeneous data in carbon footprint calculation can be realized.

The invention relates to the technical field of aircraft manufacturing, and discloses an aircraft part production quality optimization method based on data analysis. The method comprises the following steps: collecting multi-process real-time processing parameters and quality inspection data of a production line, and generating a dynamic quality weight matrix according to a process parameter coupling degree; extracting process path difference characteristics of qualified products and unqualified products in historical batches, and encoding the process path difference characteristics as a quality evolution chain matched with adjacent process parameter mutation relevance; optimizing process parameters at a quality evaluation node, driving a parameter combination to iterate to minimize quality fluctuation, generating an adjustment amount, updating an evolution chain constraint coefficient, and synchronously constructing a stability evaluation function of a correlation weight matrix and a defect propagation path; triggering process compensation according to an adjustment amount gradient, and verifying the cohesion through a tolerance rule by taking a reference parameter matched with the target evolution chain as compensation data; and generating a feedback matrix by using the quality fluctuation index and the compensation result, and correcting the mapping relation between the weight matrix and the evolution chain.

The invention discloses a resting electroencephalogram quality evaluation method and system based on double-branch comparative learning, and the method comprises the steps: collecting an original EEG signal X, carrying out the data preprocessing and data enhancement, generating two different enhanced views, transmitting the two different enhanced views to a double-branch encoder in parallel, respectively extracting a time domain waveform and a time-frequency domain rhythm feature, and carrying out the deep fusion, the fused features X1 and X2 are sent to a projection head, and through a self-supervised contrast learning mechanism, the network weight is optimized by using contrast loss; a small amount of labeled fine-tuning data sets including X and corresponding labels y are adopted, after flowing through a pre-trained double-branch encoder, the fine-tuning data sets are directly sent to a classification head connected with the back of the double-branch encoder so as to output a prediction result of an input data segment, and a mixed loss function including classification loss and comparison loss is adopted for training in the optimization process. According to the invention, a complete online real-time quality control system is established, and end-to-end real-time closed loop from data acquisition to quality evaluation is realized.

The invention relates to the technical field of image recognition, in particular to an unmanned aerial vehicle multi-modal visual recognition method for cross-scene transfer learning, which comprises the following steps: synchronously acquiring environmental parameter information and multi-modal image data in real time, and predicting the scene category of a current flight area by using a scene prediction model; selecting the source domain subset with the highest matching degree by calculating the matching degree between the scene category and the source domain subset in the preset source domain data set; dividing the target domain sample into a plurality of sub-domains to obtain target sub-domain data; extracting single-mode features of each image in the multi-mode image data, and performing fusion based on a quality evaluation result of each image and a scene category dynamic switching fusion strategy to generate a first fusion feature; a sliding window mechanism is adopted to accumulate the first fusion features, and correction is carried out according to the historical feature state at the previous moment to obtain second fusion features; and performing visual identification on the second fusion feature of the current target domain by using the visual identification model after the completion of the staged transfer learning training.

The invention provides a data quality evaluation method and system, and relates to the technical field of data quality evaluation.The data quality evaluation method is used for coupling type data quality evaluation of knowledge graph construction and specifically comprises the following steps that an entity historical version set of a knowledge graph and attribute time sequence data of the entity historical version set are obtained, calculating a redundancy index and a timeliness entropy change index of each entity, and calculating a coupling quality index based on the coupling coefficient; constructing a global quality evaluation matrix; and outputting a quality evaluation result and a cleaning strategy according to the distribution characteristics of the singular values of the global quality evaluation matrix. According to the method, the accuracy, adaptability and operability of knowledge graph data quality evaluation are remarkably improved, and an effective technical means is provided for constructing and maintaining a high-quality, low-redundancy and high-timeliness knowledge graph.

The invention relates to the technical field of meteorological quality evaluation, and discloses an automatic quality evaluation method and system for meteorological observation data, and the method comprises the steps: collecting observation data, and obtaining a meteorological evaluation parameter set through initial configuration; carrying out preprocessing operation to obtain a meteorological evaluation full data set; executing global statistical analysis at the same time to obtain a global statistical analysis data set; dividing the global statistical analysis data set into meteorological quality evaluation subsets according to region and depth hierarchy; a grading statistical analysis result is obtained; the method comprises the following steps of: setting a data proportion in each error allowable interval by acquiring an absolute error and a relative error; and generating meteorological quality distribution characteristics of different regions and levels to obtain a time sequence difference, and finally outputting a final meteorological quality evaluation report. Therefore, environment adaptive initialization, intelligent data preprocessing, multi-dimensional statistical analysis, error allowable interval analysis and automatic report generation can be realized, and the efficiency of meteorological data quality evaluation is improved.

The invention discloses a supplier quality evaluation method based on big data analysis, and relates to the technical field of supplier quality data evaluation management. The method comprises the following steps of collecting original heterogeneous data, performing preprocessing, generating a data quality report, performing quantitative evaluation on multi-source heterogeneity and real-time performance of supplier quality data, and triggering an optimization adjustment mode. According to the method, after original heterogeneous data is collected from a supplier historical transaction database and preprocessed, a data quality report is generated, the multi-source isomerism and the real-time performance of the supplier quality data are quantitatively evaluated, the data are compared with judgment values in the database and analyzed, a data integration optimization mode and a resource scheduling optimization mode are triggered, and the quality of the supplier is optimized. The real-time performance of data in supplier quality evaluation is improved, and the problem that the real-time performance of a supplier quality evaluation method is insufficient due to data multi-source heterogeneous in the prior art is solved.

The invention provides a supplier service quality assessment management system and method based on dynamic weight optimization, and belongs to the technical field of weight adjustment. The total index module is used for determining quality assessment indexes and sub-indexes thereof according to supplier service contents and contract requirements; the data acquisition module is used for acquiring historical performance data of a target supplier and standardizing the historical performance data to obtain standard performance data; the first scoring module is used for determining a first score of the quality assessment index according to the initial weight of the quality assessment index and the quantification method in combination with the standard performance data; the dynamic weight module is used for generating a weight adjustment strategy according to a business scene of the standard expression data, and obtaining a first weight of the quality assessment index after adjusting an initial weight; and the scoring module is used for calculating a quality assessment score of the target supplier according to the first weight and the first score. The accuracy and the dynamic adaptability of supplier service assessment are improved, and quality assessment and decision support are optimized.

The invention provides a bottle nitrogen charging process quality evaluation method applying artificial intelligence, and the method comprises the steps: obtaining airflow velocity distribution, vortex intensity and streamline form at a bottle opening of nitrogen charging equipment, carrying out the feature extraction and time sequence analysis, and generating an airflow feature data set; if the airflow characteristic stability is lower than a preset threshold value, predicting airflow velocity distribution, vortex intensity and a short-term change trend of a streamline form through a long-short-term memory network algorithm to obtain a future evolution state of the airflow characteristics; adjusting a nitrogen injection rate and an injection angle of nitrogen charging equipment according to the future evolution state of the airflow characteristics, and generating optimized nitrogen charging control parameters; and adjusting nitrogen injection parameters according to the optimized nitrogen charging control parameters, updating the operation state of the nitrogen charging equipment in real time, and generating an updated airflow characteristic data set.

The invention provides a quality evaluation method for a geological three-dimensional model, and the method comprises the steps: S11, carrying out the analysis of a historical geological project document, extracting evaluation indexes and data demands, and determining related modeling source data according to the type and target of a geological project; s12, learning an evaluation scheme of a historical successful geological project document, and generating a three-dimensional geological model evaluation scheme containing a verification data range and method; s13, monitoring and analyzing the modeling process of the three-dimensional geologic model based on the three-dimensional geologic model evaluation scheme; s14, determining the weight of each evaluation index in the three-dimensional geologic model evaluation scheme so as to determine the quality grade of the three-dimensional geologic model, and continuously adjusting the weight distribution of the evaluation indexes; and S15, according to the quality grade of the three-dimensional geologic model, determining whether the three-dimensional geologic model meets the acceptance requirement or not. The invention provides a systematic evaluation method for the quality evaluation of the three-dimensional geologic model, thereby promoting the application and development of the three-dimensional geologic model in various fields.

The invention belongs to the technical field of big data, and discloses a drawing quality evaluation management system and method based on big data. Comprising the following steps: extracting meta-information of a drawing, and extracting a category identifier by utilizing file name data and professional data in the meta-information to obtain a category of the drawing; carrying out marking; key information of the drawing is obtained, and a designer risk factor, a standard conformity risk factor and a version difference risk factor are calculated; calculating an integrity evaluation value of the drawing; dividing the drawings into defect drawings and standard drawings by utilizing the integrity evaluation values of the drawings; dividing a defect drawing into a first-level defect and a second-level defect; for a first-level defect, starting a multi-dimensional feature missing rate quality evaluation method to calculate a quality score of the defect drawing; for the standard drawing, calculating a quality score of the standard drawing according to the basic quality score and the proportion matching degree; and accurate data support is provided for drawing design and examination.

A method for identifying and calculating the volume of over- and under-excavation in tunnels based on laser point clouds belongs to the field of intelligent tunnel construction and blasting quality assessment technology. Technical solution: The scanned point cloud data is denoised; a design contour point cloud is generated and aligned with the centerline of the actual excavated tunnel; the actual collected tunnel excavation point cloud and the design contour point cloud are Delaunay tetrahedralized; the actual tunnel point cloud is traversed, and the vector dot product formed between the point cloud and the triangular sides of the convex hull is calculated; over- and under-excavation areas are identified; a polyhedron is constructed from the design contour point cloud and the actual excavated area point cloud constituting the area; the polyhedron is divided into quadrilaterals, and the volume of each quadrilateral is calculated, thereby calculating the volume of each over- and under-excavation area. Beneficial effects: This invention can quickly measure over- and under-excavation areas and quantities that are difficult to calculate in tunnel blasting quality assessment, and can replace cumbersome and time-consuming total station and manual measurement methods; this method has the advantages of high precision, high efficiency, and high flexibility.

The invention discloses a content and distortion bilingual guidance-based no-reference image quality evaluation model construction method, which comprises the following steps of: designing two semantic description models of content text description and distortion text description by utilizing a pre-training model CLIP of visual language comparative learning, respectively extracting content features and distortion features of an image by two branches of CNN and Transformer, and constructing a non-reference image quality evaluation model based on content and distortion bilingual guidance. Carrying out cosine similarity calculation on the distortion characteristics of the image and the distortion type and quality grade text description characteristics to obtain the matching probability of the image with various distortion types and quality grades; combining the quality grade probability with a Like scale to predict a quality score; and respectively calculating a content type, a distortion type, quality score loss and weighted summation to guide model training. The semantic content sensitivity characteristics of a human visual system in quality perception are fully combined, and the semantic understanding ability and the quality evaluation accuracy of the model in a complex distortion situation are effectively enhanced.

The invention provides a data analysis method and system based on case self-learning. The method comprises the steps of obtaining a target task input by a user; retrieving a historical case based on the target task; if the historical case is retrieved, extracting context parameters according to the target task to adapt a solution of the historical case, generating a customized solution and obtaining a first analysis result; if not, performing real-time planning through a large language model to generate a new solution and obtain a second analysis result; performing quality evaluation on the newly generated solution and the second analysis result; and when the evaluation result meets a preset storage condition, storing the new solution as a new historical case in the case library. Through double-path processing and quality evaluation feedback closed loop, the problems that in the prior art, a case library is prone to being polluted due to lack of quality evaluation, and multiplexing of historical cases is rigid and low in efficiency are solved, the self-learning quality can be guaranteed, and the analysis efficiency can be improved.

The invention provides an experience knowledge graph construction and question answering method based on heuristic self-question answering, and the method comprises the steps: randomly dividing a text corpus into small batches, inputting the small batches into a large language model, and obtaining a heuristic rule set through induction processing and quality evaluation; constructing question and answer pairs by using the heuristic rule set and the text content cues, converting the question and answer pairs, and then carrying out confidence calculation to obtain a structured experience triple with confidence; constructing a knowledge graph by using the structured experience triad with confidence; extracting the core fragment of the original narrative query by using the heuristic rule set and generating a refined query text; obtaining an empirical path by using the embedded vector of the refined query text and the knowledge graph; and inputting the original narrative query and the experience path into the large language model to generate a final answer. According to the method, logic confusion and content contradiction possibly caused by a traditional RAG method are effectively avoided, the generated answer is more reliable, and the reasoning process is more interpretable.

The invention relates to a PRP preparation quality evaluation method based on double-branch image fusion, and belongs to the technical field of artificial intelligence and data processing. The method comprises the following steps: acquiring a centrifugal sample image data set, and dividing the centrifugal sample image data set into a training set and a test set; constructing a double-branch neural network, wherein the double-branch neural network comprises an adaptive contrast correction module, a dynamic convolutional layer, a deformable spatial pyramid pooling layer, a gated cross attention module, a Transform encoder and a classification layer; training the double-branch neural network by using data in the test set; in the training process, the dual-branch neural network is optimized through a loss function, and trainable parameters are updated by adopting a gradient descent updating strategy to obtain a trained network; and inputting data in a test set into the trained network to obtain output category prediction probability distribution, and taking the category corresponding to the maximum probability as an evaluation result. According to the method, the PRP preparation quality evaluation accuracy can be improved.

The invention discloses a DeepSeek-based role large model fine tuning corpus automatic generation method. The method comprises the steps of receiving a role setting document describing target role features and an optional domain knowledge base; utilizing a Prompt engineering module to construct a generation Prompt containing deep role injection information and context perception knowledge fusion based on a role setting document and an optional domain knowledge base; the deep role injection information is used for enabling the generated content to accord with characters, mood, styles, knowledge backgrounds and value views defined in the role setting document; calling a preset basic large model core, and performing text generation by using the Prompt to obtain candidate corpora; performing automatic multi-dimensional quality evaluation on the candidate corpora by utilizing a quality evaluation module; wherein the assessment dimensions of the multi-dimensional quality assessment include role consistency, fact accuracy, diversity and security; and screening out qualified corpora based on an evaluation result of the multi-dimensional quality evaluation and a preset strategy, and forming a final high-quality corpus. The invention further discloses a system, electronic equipment and a computer readable storage medium.

The invention discloses a quality evaluation method and system for a composite board, and relates to the technical field of quality detection, and the method comprises the steps: constructing a latent variable feature space representing interface binding energy abnormity based on an initial response data field through a physical constraint relation between interface local response features and binding energy distribution; extracting a topological correlation structure between local latent variable features in the latent variable feature space through a graph convolutional network to obtain a local binding energy anomaly initial candidate region; active response mode enhanced excitation is carried out on the local area of the initial candidate area, the dynamic enhanced response of each local area is obtained, and a binding energy abnormal area is determined; determining the specific position and range of the hidden pseudo solder strip according to the correlation topological structure difference between the local feature in the binding energy abnormal region and the normal region; according to the method, precise identification and high-precision positioning of the titanium-steel composite plate interface hidden cold solder strip are realized.

The invention provides a data quality evaluation method and system. The method comprises the following steps: firstly, collecting three types of data sets of three-phase unbalance degree, free charge density distribution and line impedance decomposition; and then, carrying out space topology mapping on the charge density gradient extreme point of the cable joint area and the voltage phase angle, considering charge migration effect compensation, and calculating a corrected three-phase voltage unbalance parameter. Secondly, establishing a coupling factor matrix based on the separated line inherent impedance component and the load fluctuation impedance component, and performing time domain convolution operation on the coupling factor matrix and a voltage unbalance parameter to generate a distortion feature vector; and finally, calculating data credibility weight distribution of the global monitoring nodes according to the nonlinear correlation degree between the three-phase unbalance degree data and the distortion feature vector, and using the data credibility weight distribution to evaluate the quality of the low-voltage treatment data. According to the technical scheme provided by the invention, the accuracy and reliability of data quality evaluation can be improved.

The invention discloses a comprehensive quality evaluation method and system for Beidou deformation monitoring observation data in a complex environment, and relates to the technical field of satellite navigation positioning, and the method comprises the steps: carrying out the comprehensive evaluation of an elevating angle, a signal-to-noise ratio, an epoch difference and an environment model of the observation data of a Beidou single station, and determining the initial quality of the data; based on the double-difference observation values among the multiple stations, the data quality evaluation precision is further improved by combining a double-difference ambiguity resolving method; according to the method, ionosphere delay, three-difference errors and inter-epoch difference errors are used for data fusion, a more accurate quality evaluation result is obtained, and finally, the data are optimized through comprehensive analysis based on the quality evaluation results of various types of data so as to ensure accurate positioning and deformation monitoring. The technology aims at improving the accuracy and reliability of data quality evaluation by comprehensively analyzing influence factors of Beidou observation data in a complex environment, and provides support for geological disaster monitoring, smart city construction and other high-precision application scenes.

The invention relates to the technical field of data processing, in particular to a thinking chain data quality evaluation method, electronic equipment and a storage medium. Comprising the steps of firstly, obtaining thinking chain data corresponding to a target question; wherein the thinking chain data comprises a reasoning process and a deduction answer. And then, based on the standard answer and the deduction answer of the target question, obtaining a correctness score of the deduction answer. And obtaining a preciseness score of the reasoning process. Wherein the preciseness score is used for representing the logic rationality of the reasoning process. And finally, performing weighted summation on the correctness score of the deduction answer and the preciseness score of the reasoning process to obtain the quality score of the thinking chain data. According to the embodiment of the invention, high-quality CoT data can be evaluated and determined more accurately.

The invention discloses a model-based sewage treatment plant data quality control and process evaluation analysis method and system, and belongs to the technical field of sewage treatment. The method aims at solving the problems that a current sewage treatment plant data acquisition system faces data missing, abnormity and deviation, and data quality control and process evaluation are separated from each other, the intelligent degree is low, and the adaptability is poor in the prior art. The method comprises the following steps: firstly, comprehensively evaluating original data by using a data quality evaluation model, and accurately identifying and processing various problem data; a complex association relationship among process parameters is deeply mined through a process association analysis model, and a whole-flow process knowledge graph is constructed; and finally, in combination with a data quality evaluation result and the process knowledge graph, realizing accurate evaluation and optimization adjustment of the process operation state. The data quality management and control level of the sewage treatment plant is effectively improved, the accuracy of process evaluation is enhanced, powerful support is provided for stable operation and efficient management of the sewage treatment plant, and the sewage treatment industry is promoted to develop towards the intelligent and refined direction.

The embodiment of the invention discloses a test question output method and device based on multi-model collaboration, electronic equipment and a medium. A specific embodiment of the method comprises the steps of performing multi-source data fusion on a multi-source test question association data set, and then performing knowledge point association analysis to obtain a dynamic test question knowledge point graph; performing multi-hop graph reasoning query on the dynamic test question knowledge point graph to obtain a test question associated knowledge point set; performing model collaborative generation on the test question generation request information and the test question associated knowledge point set to generate an initial test question set; performing quality evaluation on the initial test question set to obtain a test question quality evaluation value set; dynamically adjusting the initial test question set to obtain an adjusted test question set and storing the adjusted test question set; and carrying out expansion adaptive test paper group rendering display on the adjusted test question set to obtain a test question test paper and printing the test question test paper. According to the embodiment, the test question quality can be improved, the test question generation efficiency is improved, the test question generation duration and display duration are shortened, the test question display quality is improved, and waste of display resources is reduced.