820 results about "Result set" patented technology

The invention discloses a railway station facility full life cycle information query method based on digital twinning. The method comprises the steps that a unified facility object with a unique logic identifier is generated based on multi-source heterogeneous data aggregation; constructing a facility tense version model containing time sequence nodes by using an event-driven rule; in combination with an engineering constraint knowledge base and statistical characteristics, evaluating physical feasibility and statistical association strength between events, introducing life cycle stage risk factors, and constructing a facility full life cycle causal chain model; and based on the model, responding to the explanatory query, executing a constrained path search in the facility full life cycle causal chain model, and generating a life cycle query result set containing a causal path. According to the method, the pseudo-correlation problem generated by pure data mining can be effectively solved, the cumulative influence of early-stage risks in the design and construction stage on later-stage operation is quantified, and accurate tracing and explanation of cross-stage fault causes are realized.

The invention discloses a power transmission line state simulation and prediction method based on digital twinning, and the method comprises the following steps: accessing meteorological data, electrical data, space and asset data and historical state data, carrying out the preprocessing, and generating a multi-source spatial-temporal feature sequence set; constructing a double-domain cross-coupling time convolution twinborn model, and obtaining corresponding feature representation through a quick response path and a slow response path; physical consistency state representation is generated through a physical constraint cross gating layer; outputting a prediction result set through a simulation engine; online observation data are obtained, and the model is corrected based on virtual and real residual errors; and generating a current prediction result set by using the corrected model, and converting the current prediction result set into a scheduling and operation and maintenance strategy set. According to the method, the double-domain cross-coupling time convolution twin model and a virtual-real closed-loop correction mechanism are adopted, multi-scale state simulation prediction of the power transmission line is achieved, and the method has the advantages of being high in precision, robustness and performability.

The invention discloses a large-model knowledge retrieval method based on knowledge graph enhancement, which comprises the following steps: S1, collecting multi-source data, constructing an original knowledge triple set, and generating a knowledge graph; s2, inputting natural language query data, executing semantic analysis and intention recognition, and forming a query semantic vector and a structured query template; s3, performing combined convolution operation on the target entity node and the relation vector through an improved combined graph convolution network CompGCN to generate a candidate knowledge list and distribute confidence; s4, calculating the similarity between the query vector and the knowledge vector by utilizing a semantic embedding mechanism and DistilBERT acceleration to obtain a first retrieval result set and a credibility score; s5, fusing the two types of retrieval results to obtain a knowledge result set; and S6, generating an answer text according to the fused knowledge result set, and outputting a reasoning path, entity reference and a relation link. According to the method, the accuracy, the response speed and the knowledge reasoning transparency of complex query processing are improved.

The invention belongs to the technical field of fault detection, and relates to a high-voltage transmission line fault detection and identification method. The method comprises the following steps of: dividing candidate fault sections according to a topological structure by synchronously acquiring steady-state and transient-state traveling wave signals of key nodes of a power grid; identifying a fault by using a multi-feature fusion criterion and extracting traveling wave features; executing mode recognition-based steady-state section judgment and multi-terminal ranging and polarity verification-based transient positioning in parallel to generate two types of positioning result sets; and finally, carrying out intelligent fusion judgment on the two types of results under topological constraints. According to the method, the problems that a traditional single-point signal analysis method cannot determine a fault section, lacks space positioning capability and is insufficient in adaptability are solved, section-level accurate positioning of the fault is realized, and the positioning accuracy, robustness and fault processing efficiency are remarkably improved.

The invention relates to the technical field of computer information processing, and discloses a high-fidelity terrain surface interpolation method and system for large-scale point cloud data, and the method comprises the steps: carrying out the multi-level cleaning of original point cloud data, and generating a cleaned point cloud data set; performing adaptive tile grid division based on the cleaned data to generate a tile set covering the target area; in each tile, taking the to-be-interpolated grid point as a query point, constructing and optimizing a local Gaussian process model, and obtaining a real elevation predicted value of the query point; processing all query points in the tiles in parallel to obtain a tile elevation interpolation result set; performing weighted average on each tile result to generate a global seamless terrain surface; according to the method, the problems of poor expandability and surface discontinuity caused by local interpolation in large-scale point cloud data processing are effectively solved, and high-precision and high-fidelity terrain surface construction is realized.

The invention discloses a financial abnormal event analysis method, electronic equipment, a medium and a product. The method comprises the steps of obtaining at least one financial clue event, and performing structured element extraction and standardization processing on each financial clue event to obtain a standardized event behavior sequence; performing semantic feature and behavior pattern analysis based on the normalized event behavior sequence, performing event reasoning rule recall in combination with a pre-constructed event reasoning library, and determining a recalled rule set; generating an abnormal event analysis execution plan based on at least one inference rule in the rule set; querying original data in the data system according to an inference rule included in the abnormal event analysis execution plan, generating a data view, executing the abnormal event analysis execution plan based on the data view, and generating an inference execution result set and execution path information; and an event reasoning analysis result is generated according to the reasoning execution result set and the execution path information, and the problems of complex abnormal event analysis modeling, low interpretability and the like are solved.

The invention discloses a knowledge graph-based software material consistency automatic checking method, which comprises the following steps of: collecting and processing a software declaration material to form a standardized original material set; forming an initial entity set and a candidate relation set based on the normalized original material set; constructing a heterogeneous knowledge graph based on the initial entity set and the candidate relationship set; executing cross-modal semantic anchoring on the heterogeneous knowledge graph to generate a semantic anchoring mapping table and an anchoring confidence value set; generating an enhanced constraint graph based on the semantic anchoring mapping table and the anchoring confidence value set; outputting a consistency detection result set and a minimum conflict subgraph set based on the heterogeneous knowledge graph and the enhanced constraint graph; and based on the consistency detection result set and the minimum conflict subgraph set, generating a difference check report and a repair suggestion set. According to the method, heterogeneous knowledge graph construction and semantic anchoring are adopted, and automatic consistency checking of the soft material is realized.

The invention relates to the technical field of large language models, and discloses a zero-illusion large language model output method and system, and the method comprises the steps: sequentially carrying out the deterministic lexical analysis and standardized mapping of a received original query, and generating a standard term set; locking to-be-reasoned items through hierarchical matching to form a deterministic retrieval result set; executing five rounds of progressive verification processing on the result set to obtain an integrated result set with an LLM verification mark; and outputting a layered zero illusion result through multiple verification and auditing of structure compliance, semantic consistency and knowledge base double recheck. According to the method, probabilistic reasoning is replaced by full-link deterministic operation, so that each code can be traced back to a credible knowledge source, illusion is thoroughly eliminated, absolute reliability and complete traceability of an output result in a professional field are realized by a verifiable deterministic path, and a foundation is laid for zero-illusion application.

The invention discloses a long document intelligent retrieval method and system based on hierarchical analysis and multi-modal fusion, and the method comprises the following steps: carrying out hierarchical analysis on an input long document, and obtaining a physical layout structure, a logic structure and a division and association relationship of multi-modal elements of the long document; respectively extracting text features of the text elements and visual features of the multi-modal elements; performing deep fusion on the text features and the visual features through a cross-modal fusion model to generate a unified multi-modal semantic representation vector; constructing a hybrid index; receiving user query, and performing preliminary retrieval by using the hybrid index to obtain a candidate result set; reordering the candidate result set; and outputting the retrieval result after reordering. Through visual-logic collaborative analysis, gating enhanced bilinear attention fusion and graph attention-based context reordering, a collaborative gain effect is generated in a long document intelligent retrieval task, and the retrieval precision and the user experience are remarkably improved.

The invention discloses an industrial drawing analysis method and system combined with a multi-modal large model and OCR, and relates to the technical field of drawing analysis, the method comprises the following steps: carrying out layout area segmentation, text recognition and geometric element extraction on an industrial drawing to obtain a structured information set; constructing a multi-relation structure chart set of the industrial drawings; performing structure embedding and feature bias enhancement to obtain a structure feature set; performing semantic understanding on the industrial drawing to obtain semantic features, and performing multi-channel coding to obtain a multi-modal feature set; obtaining a preliminary analysis result set of the industrial drawing; and performing rule verification, and outputting an industrial drawing analysis result. The technical problems of low industrial drawing analysis efficiency, inaccurate automatic scheme analysis and limited processing capacity in the prior art are solved, and the technical effects of realizing full-process automatic analysis of the industrial drawing by combining the multi-modal large model and the OCR, improving the precision and efficiency of industrial drawing analysis and standardizing output information are achieved.

Retrieval of structured tabular data in a retrieval-augmented generation (RAG) platform is described. An example method includes receiving a tabular file including structured data, extracting short and long metadata from the file, and generating a dataset agent object using the extracted metadata. A human-readable description is associated with the structured data, and the dataset agent object and metadata are stored in a metadata index. A natural language query is received through a query agent object rendered within a canvas including interconnected agent objects. A dataset agent object is identified based at least in part on metadata filtering using the natural language query and short metadata. A structured query conforming to structured query language syntax is generated using a large language model (LLM) and executed on the structured data. A response including the result set and dataset agent identifier is generated and transmitted to a downstream agent object for further processing.

The invention provides a multi-dimensional operation parameter analysis fault detection method and system, and relates to the technical field of fault detection, and the method comprises the steps: building an inspection task system and a fault triggering system, and forming a task basic data set; generating an inspection task instance object; analyzing to obtain monitoring index data; effective fault report information is extracted, and a software system inspection result set is formed; operating parameters of the hardware system are collected, threshold detection and fluctuation analysis are executed, and a hardware system inspection result set is generated; and extracting multi-dimensional features, inputting a fault scoring function, calculating a comprehensive fault score of each device or system, and automatically generating an inspection report and a fault report sheet. According to the method and the device, the problems that fault evaluation depends on manpower and judgment standards are not uniform in the prior art can be solved, automatic risk grading and result generation based on comprehensive scores are realized, and thus the accuracy and the diagnosis efficiency of fault detection are improved.

The invention relates to a multi-dimensional intelligent cooperative detection method, system and device based on a tunnel inspection robot and a medium. The method comprises the steps that initialization is carried out based on tunnel environment parameters and task requirements, and a standardized initialization reference data set is generated; based on the data set, starting an inspection robot to carry out data acquisition, preprocessing original data, and generating a synchronized multi-source data acquisition stream; then abnormal event monitoring, structure health detection and environment perception detection are executed in parallel, and a standardized detection result set is generated; and performing multi-source data fusion processing on the standardized detection result set to obtain a core detection result and a comprehensive confidence coefficient, and performing matching in combination with a preset decision rule base to generate a standardized decision instruction set. According to the method, through multi-module collaborative data acquisition and multi-source data fusion processing, the comprehensiveness, accuracy and efficiency of tunnel inspection can be improved, and then the safety and efficiency of tunnel operation and maintenance management are effectively improved.

Disclosed in the present application are a data processing method and apparatus, and a device and a readable storage medium. The method comprises: obtaining M candidate results which are sorted and displayed on a page, wherein the M candidate results are retrieved on the basis of an initial query statement; if an interaction operation meets a re-retrieval condition, acquiring historical interaction information, which comprises N historical query statements and a set of historical interaction results, wherein the N historical query statements comprise the initial query statement, and the set of historical interaction results comprises a candidate result which is included in an interaction record corresponding to an object; and generating context content vectors corresponding to the historical interaction results in the historical interaction information, and generating an updated query statement by means of the context content vectors and the N historical query statements, wherein the updated query statement is used for being displayed in a retrieval prompt box, and the retrieval prompt box is used for guiding the object to perform re-retrieval by means of the updated query statement. By means of the present application, the accuracy and efficiency of content retrieval can be improved.

The invention discloses a unified search and page configuration rendering method and system based on a data model. The method comprises the following steps: S1, data acquisition and model mapping; s2, indexing and synchronizing data; s3, unified search request processing; s4, processing and returning a result set; and S5, dynamically rendering the page. The system comprises a data source layer, a data unification layer, an index layer, an application service layer and a display layer. The invention provides a set of end-to-end integrated solution for solving the problems that multi-source heterogeneous data is difficult to integrate, the cross-system search efficiency is low, page rendering lacks flexibility and the like. According to the scheme, by constructing a standardized data model, a unified search engine index system and a dynamic page rendering mechanism driven by configuration, a complete technical link from data source access to front-end display is achieved, and the efficiency and expansibility of the system in the aspects of data management, information retrieval and page display are remarkably improved.

The invention provides a small and medium-sized enterprise digital transformation intelligent question answering method and device, equipment and a storage medium, and the method comprises the steps: carrying out the semantic recognition and entity extraction of a question input by a user, and generating a question semantic vector; retrieving a corresponding target text fragment in a preset vector database according to the question semantic vector; searching a sub-graph structure associated with the question semantic vector in a preset knowledge graph according to the target entity of the question semantic vector; reordering the text segments and the sub-graph structures based on correlation scores to obtain an ordering result set; and inputting the sorting result set into a retrieval enhancement generation model, and generating question and answer output content. Through the implementation of the scheme of the application, association matching of question semantics and enterprise knowledge can be realized by utilizing the text semantic information and the knowledge graph structure information at the same time in the question and answer generation process, the consistency of question and answer contents in logic structure and semantics is ensured, and the accuracy of digital transformation question and answer of small and medium-sized enterprises is improved.

The invention discloses a charged particle injection stability analysis method based on a double-flow neural network, and the method comprises the following steps: S1, constructing a multi-modal collection platform, obtaining image data and signal data, and generating an original data pair; s2, taking the original data pair as input, and outputting an image tensor; s3, outputting a signal tensor based on the original data pair in combination with normalization processing; s4, generating double-flow feature output according to the image tensor and the signal tensor; s5, outputting the double-flow features, executing an alignment mechanism, and outputting joint representation; s6, forming a stability analysis result set based on the joint representation; s7, inputting the stability analysis result set into an information maintenance type distillation pruning module to generate a lightweight deployment model; and S8, deploying the lightweight deployment model to an edge reasoning platform, and generating a feedback control instruction according to the stability analysis result set. According to the method, charged particle injection stability analysis is realized by fusing and mixing Bayesian phase unwrapping processing and a pseudo bidirectional modal entropy alignment mechanism.

The invention relates to the technical field of hoisting apparatus simulation, and discloses a hoisting apparatus simulation and test verification method based on digital twinning. The method comprises the following steps: constructing a virtual twinborn body of the hoisting apparatus, wherein the virtual twinborn body comprises a geometric model, a kinematic model and a dynamic model corresponding to a physical hoisting apparatus; collecting real-time operation monitoring data of a physical hoisting instrument, and synchronizing the real-time operation monitoring data to the virtual twinborn body; in the virtual twin body, performing standardization processing on the received real-time operation monitoring data to generate a standardized data set; inputting each multi-source measurement data set into a pre-configured feature extraction engine, and outputting a corresponding feature group; performing feature integration operation on each feature group to generate integrated feature representation; and mapping the integrated feature representation to the virtual twin, and executing a simulation test in the virtual twin based on the integrated feature representation and a preset evaluation model library to generate an evaluation result set.

Techniques for a natural language interface for searching and retrieval of digitally-stored information are disclosed. In one embodiment, a computerized method is disclosed comprising receiving, at a computing device, natural language input from a user; generating, by the computing device, a prompt using the natural language input; submitting, by the computing device, the prompt to a large language model (LLM); receiving, by the computing device, from the LLM, a response to the submitted prompt, the response comprising a logical expression of the natural language input generated by the LLM using the prompt; generating, by the computing device, for a data source, a query using the logical expression; retrieving, by the computing device, a query result set from the data source using the query; and generating, by the computing device, a response to the natural language input using the query result set.

The invention discloses a drainage basin real-time flood control scheduling risk dynamic early warning and emergency scheme making method, which comprises the steps of obtaining a real-time total element information tensor and an engineering flood control design parameter set, and calculating to obtain a real-time hierarchical risk entropy time sequence front section based on safety margin distribution, and inputting real-time data and pre-constructed entropy flux Markov graph structure data into the trained entropy perception double-flow prediction model for reasoning to obtain a future risk prediction set. And a dynamic risk early warning result set is generated in combination with a preset entropy level threshold and a future failure chain probability, and an emergency scheduling scheme set is generated on the basis of meeting safety margin constraints and entropy reduction targets. According to the invention, dynamic early warning and decision making of strong physical-risk coupling are realized.

The invention discloses a finance and taxation data analysis method, device and equipment based on a directed acyclic graph and an intelligent agent and a medium, and relates to the technical field of finance and taxation inspection, and the method comprises the steps: constructing a target directed acyclic graph which takes knowledge blocks as nodes and dependencies as directed edges based on a historical finance and taxation data set and natural language description of the knowledge blocks; performing hierarchical division according to the in-degree of each knowledge block in the graph, and generating a reference task plan by using a target tool list based on each knowledge block in different hierarchies, a thinking chain guide instruction and a cue word generated by an example; controlling the intelligent agent to execute the planning, and packaging the reference task planning according to the obtained execution result set and the log to obtain a basic task planning template; utilizing the current finance and tax data and the current to-be-analyzed demand to finely adjust the context parameters and the analysis range in the template to obtain a target task plan; and analyzing the current finance and tax data set based on the plan to generate a target analysis result. And the finance and tax data analysis consistency and reliability are improved.

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

The invention discloses a report system testing method and device and electronic equipment. The method relates to the field of big data, and comprises the following steps: inputting test data into a to-be-tested report system to obtain a first data processing result; comparing the first data processing result of the test data with the first standard data processing result to obtain a first test result; obtaining a sub-data processing result of the test data in each detection node in a preset processing path in the to-be-tested report system, and obtaining a second data processing result; obtaining a second standard data processing result set of the test data in the preset processing path, and determining a second test result of the test data according to the second standard data processing result set and the second data processing result; and determining a target test result of the to-be-tested report system according to the first test result and the second test result. Through the method and the device, the problem of relatively low efficiency of testing the report system by adopting a black box test method in related technologies is solved.

The invention relates to a power fee accounting method based on intelligent management, and the method comprises the following steps: S1, obtaining multi-source heterogeneous data, carrying out the preprocessing, and carrying out the collection, data standardization, time calibration and automatic tagging of the multi-source heterogeneous data, and obtaining a standard structured data stream; s2, constructing a dynamic accounting rule data set based on the standard structured data stream and transaction data, market rules, contract terms, regions and type price standards; s3, constructing an intelligent prediction model based on LSTM and ProphetAI models, automatically detecting reasonability and correcting a prediction interval according to the dynamic accounting rule data set, and obtaining a pre-accounting result set; s4, according to the pre-checking result set, in combination with the price standard and the amount fee strategy, configuring subsidy and refunding policies and parameter processing, automatically matching a settlement unit and a settlement rule, and flexibly applying different checking templates according to distributed, direct-purchase and non-direct-purchase power plants to obtain a checking intermediate table; and S5, generating a final settlement packet according to the accounting intermediate table and the user configuration parameters.

The invention discloses a big data measurement asset supply and demand matching and inventory optimization method, and relates to the technical field of big data processing and supply chain management. The method comprises the following steps: segmenting supply chain asset data through a hash function according to multi-dimensional attributes to generate a data fragment set, and constructing a global index tree according to the data fragment set; and monitoring the load state of the leaf nodes of the global index tree in real time, and migrating and verifying data during unbalance. And querying the global index tree, positioning fragment data associated with the query request from the uniformly distributed leaf nodes, generating a preliminary matching asset set, calculating the matching degree of each asset and the query request, and generating a resource allocation result. And updating the global index tree, and generating the latest data view representation. And adjusting the attribute weight coefficient of each dimension in the hash function, and generating optimized storage layout configuration. Irrelevant data are filtered through a neighbor algorithm, and a final matching result set is generated. Balanced storage, efficient query and accurate matching of asset data are realized, and the overall response speed and the resource utilization rate are improved.

The invention discloses a question answering method and device for credit review knowledge based on a large model and a medium, and the method comprises the steps: obtaining a to-be-processed credit review question, extracting a core keyword from the question through a natural language processing algorithm, and converting the question into a deep semantic vector through a semantic vector model; inputting the questions into an intention classification model, generating filtering labels, inputting the questions into a distributed full-text search engine, screening a target knowledge base according to the filtering labels, and performing retrieval in the target knowledge base based on the core keywords and the deep semantic vectors to obtain a preliminary retrieval result set; calculating the score of each preliminary retrieval result in the result set according to the core keyword, the deep semantic vector and the weight factor, and screening out final reference content in a descending order; and on the basis of credit and loan business compliance requirements, risk control rules and question and answer output specifications, the credit and loan prompt words are constructed, and the final reference content is input into the large model to obtain structured credit and loan review answers, so that the question and answer efficiency and accuracy are improved.

The invention discloses a statistical data downloading method for supply chain finance, which relates to the technical field of finance and comprises an acceptance permission judgment module, a query resource control module and a desensitization packaging evidence storage module. The acceptance permission judgment module is used for uniformly accessing the statistical application, completing identity verification, main body range binding, index range binding, time range binding and area range binding, and generating an acceptance bill and a judgment bill; the query resource control module is used for generating a query plan under the index aperture mapping dictionary, the partition information and the resource quota, binding a concurrent quota, a throttling quota and a priority queue, and outputting an executable instruction stream; and the desensitization packaging evidence storage module is used for executing differential desensitization according to the main body type and the sensitivity level, performing aggregation calculation and deduplication execution, completing result set packaging, digital watermarking and downloading delivery, generating a downloading certificate and writing a certificate abstract and an evidence index into an evidence storage chain. The method has the characteristic of high consistency.

The invention relates to an anti-reflux control method based on time interval distribution. The method comprises the following steps: acquiring a multi-source fused operation data set; obtaining a trend prediction result set according to the operation data set; according to the trend prediction result set, dividing the prediction time window into a plurality of continuous control time periods, and based on the power generation prediction value, the load prediction value and the uncertainty index of each time period, calculating a corresponding time period countercurrent risk level, and generating a time period risk assessment result; obtaining a time period budget allocation table according to a time period risk assessment result; according to the time period budget distribution table, performing time-division power adjustment on the photovoltaic inverter, the energy storage unit and the controllable load, and realizing smooth transition between adjacent time periods through a cross-time period energy balance mechanism; and monitoring the grid-connected point power and the equipment operation state in real time, and when power deviation or load sudden change is detected, dynamically correcting the current output according to the residual capacity of the time period budget allocation table. According to the invention, the photovoltaic anti-reflux control effect can be improved.

The invention provides an intelligent customer service query system and method based on natural language understanding, and relates to the technical field of natural language processing. A query intention and a core entity of a natural language query request input by a user side are extracted; performing hierarchical deconstruction on the query intention, and constructing a query semantic tree according to a hierarchical deconstruction result and a semantic association relationship between core entities; generating a decision tensor representing a query path priority according to a node semantic weight of the query semantic tree and an access delay feature of the multi-source database; further adjusting an access sequence and a connection strategy of the multi-source data through a decision tensor and a dynamically updated query cost model to obtain a query execution link adaptive to the current query request; and calling a corresponding data source interface according to the query execution link to execute data retrieval and integration operation to obtain a target result set responding to the query demand of the user. By the adoption of the scheme, multi-source data optimal cost retrieval based on the natural language can be achieved.

The invention discloses a multi-dimensional bid evaluation intelligent optimization method based on deep learning, and the method comprises the following steps: obtaining historical and current bid evaluation data, carrying out the preprocessing, and generating a multi-dimensional input feature set; constructing a hierarchical Pareto leading-edge learning network, and outputting a target sub-representation set of each bid evaluation dimension by a bottom-layer target sub-network; executing a multi-task gradient equilibrium and hyper-volume maximization strategy in a middle-layer leading edge aggregator; performing frontier point screening and interpolation fusion on the Pareto frontier point candidate set at a top decision-making layer to generate a comprehensive scoring result and contribution mapping; and outputting a bid evaluation sequence and selection suggestion according to the comprehensive scoring result, and generating a bid evaluation decision result set. According to the method, intelligent optimization of multi-dimensional bid evaluation is realized by adopting the hierarchical Pareto leading-edge learning network, and the method has the advantages of weight self-adaption, result interpretability and evaluation decision stability.