20 results about "Match method" patented technology

The invention relates to the technical field of artificial intelligence, and provides an intelligent insurance matching method and system combining a user portrait and a clause knowledge base. The method comprises the following steps: constructing a first portrait of a user based on a user data set and optimizing to generate a second portrait; extracting features from the insurance clause knowledge base in a multi-dimensional manner to construct a product portrait; performing demand coverage risk evaluation on the product portrait based on the second portrait of the user to generate a demand coverage risk matrix; performing guarantee conflict risk evaluation to generate a guarantee conflict risk matrix; introducing an underwriting risk analysis channel, and generating an underwriting risk matrix in combination with the second portrait of the user; and fusing the three types of risk matrixes to perform multi-level optimization matching, and generating an insurance optimization matching graph, so as to solve the technical problem of significant deviation between the recommendation result and the actual risk preference and guarantee demand of the user, realize dynamic and accurate matching of the insurance product and the user demand, reduce the guarantee conflict risk, improve the correlation of the recommendation result, and improve the user experience. And the technical effect of personalized insurance configuration requirements is met.

The invention discloses a multi-dimensional scientific and technological achievement and innovation resource matching method based on big data, and relates to the technical field of science and technology finance, and the method comprises the steps: obtaining enterprise working condition data and insurance and financial terms, inputting a dynamic classification model, outputting an enterprise type, and associating to an achievement transformation stage; the insurance scheme priority is rendered on the insurance interface, the corresponding insurance scheme is set according to the enterprise type, format contract terms are embedded into the insurance description, and insurance decision data are generated; and insuring decision data is fed back to the entropy risk assessment mechanism, and the weight coefficient and the trial period parameter of the difference factor are updated. According to the method, historical performance data, real-time trial period parameters and matching decisions of credit institutions are fused through an entropy risk assessment mechanism, migration risk values are dynamically generated, risk quantification of multi-source heterogeneous data is realized, and enterprise risk migration trajectories are accurately captured.

The invention relates to the technical field of data processing, and discloses an industrial chain investment attraction target matching method and system based on big data analysis, and the method comprises the steps: extracting the industrial characteristics of each target enterprise data, taking the industrial characteristics as a starting point, carrying out the extension based on all target enterprise data, and constructing an industrial chain atlas, determining the similarity between the to-be-matched industrial characteristics and the industrial chain atlas, determining a matched enterprise based on the similarity and the selection orientation, determining a model operation score of the matched enterprise based on a recommendation model, historical operation information and historical enterprise behaviors, and determining an evaluation operation score of the matched enterprise based on the historical operation information and the historical enterprise behaviors, the enterprise comprehensive operation score of the matched enterprise is determined according to the relation between the model operation score and the evaluation operation score, whether the matched enterprise is determined as the target matching result or not is judged according to the enterprise comprehensive operation score, and the target matching stability and reliability are ensured.

The invention relates to the technical field of power customer service and automatic matching, in particular to an automatic matching method for power customer engineering. Comprising the following steps: screening out candidate service units meeting qualification conditions by using a multi-attribute decision analysis method; based on a content-based recommendation algorithm, extracting features of customer engineering, and recommending electric power engineering service units matched with the features; according to a recommendation algorithm based on matrix decomposition, power customer engineering and power engineering service units are expressed as rows and columns of a two-dimensional matrix respectively, and service units with potential matching are recommended by calculating the incidence relation between matrixes; the recommendation results of the two algorithms are fused, other factors are considered, sorting is carried out, and the final recommendation result is comprehensively generated. According to the design, automatic matching between the customer and the electric power engineering service unit can be realized, the matching success rate is improved, the transaction cost is reduced, and the operation efficiency of the electric power market is improved.

The invention relates to the technical field of financial science and technology, and discloses an intelligent financial product matching method and system, and the method comprises the steps: analyzing occupational attributes and income levels in user identity data, and constructing an exclusive financial demand portrait of a financial user; screening a candidate financial product set meeting a fund flow constraint condition; calculating a matching preference coupling degree between the risk preference tag and the risk level identifier, calculating a cooperation index between the financial planning reference and the income cycle parameter, and generating a multi-dimensional matching index of the candidate financial product set; calculating a settlement prediction value of the candidate financial product set, and generating a financial product recommendation sequence of the candidate financial product set; and performing optimization processing on the financial product recommendation sequence to obtain an optimized product recommendation sequence, and generating a product recommendation set of the financial users. The accuracy of financial product matching can be improved.

The application claims a data integrity audit matching method based on psychological perception and preference, comprising the following steps: S1: calculating the subjective and objective weights of each evaluation index, and calculating the subjective and objective combination weights of the index based on the weights, and then calculating the weight of each audit scheme by using the improved Topsis comprehensive evaluation method; S2: calculating the user preference weight by using the triangular fuzzy number and Topsis method according to the user preference set, and integrating the weights of the two; S3: quantitatively calculating the comprehensive preference value of both parties according to the demand preference of the audit task and the execution preference of the audit resource, and calculating the preference utility value matrix of both parties; S4: calculating the perception utility value matrix of both parties based on the improved disappointment theory; S5: standardizing the perception utility value matrix, taking the maximization of the standard perception utility value of both parties as the optimization goal, constructing a double-objective optimization model, and solving the model. The application effectively improves the comprehensive satisfaction of both parties in the case of considering the psychological factors and preferences of both parties.

The application relates to the technical field of artificial intelligence, and provides an intelligent insurance matching method and system combining a user portrait and a clause knowledge base. The method comprises the following steps: constructing a first portrait of a user based on a user data set and optimizing a second portrait; constructing a product portrait by multi-dimensionally extracting features from an insurance clause knowledge base; performing demand coverage risk evaluation on the product portrait based on the second portrait of the user, generating a demand coverage risk matrix; performing guarantee conflict risk evaluation, generating a guarantee conflict risk matrix; introducing a risk analysis channel, combining the second portrait of the user to generate a risk matrix; and fusing the three types of risk matrices to perform multi-level optimization matching, generating an insurance optimization matching graph, so as to solve the technical problem that there is a significant deviation between a recommended result and actual risk preference and guarantee demand of a user, realize dynamic and accurate matching of an insurance product and user demand, reduce guarantee conflict risk and improve the relevance of a recommended result, and achieve the technical effect of meeting the demand of personalized insurance configuration.

The invention belongs to the field of talent market analysis, and particularly relates to a professional and industrial talent demand matching method and system based on AI identification. The method comprises the following steps: acquiring published student employment data in each university, classifying the acquired student employment data, adjusting the employment rate of student recruitment events in combination with previous student employment data classification, analyzing and matching posts for previous graduates, and outputting post matching degrees. And visual employment recommendation data is output in combination with the employment rate of the previous students and the employment matching degree of the previous students. Students waiting for employment can make employment preparation more quickly and can be helped to be refined, and meanwhile talents can be conveyed to enterprises more accurately.

The invention discloses a team precise matching method and system based on a large model, and aims to solve the problem of precise matching between industrial technology research and development requirements and research and development personnel of college scientific research institutions. By constructing a multi-dimensional portrait model fusing an industrial chain and a scientific research chain, the industrial demand, scientific research capability and achievement transformation related data are collected and deeply analyzed, and deep analysis and modeling are performed on multi-dimensional features by using a large model. The core of the method comprises the following key steps: researcher ability portrait construction, data resource fusion and feature extraction, and matching model training and dynamic optimization. Based on interdisciplinary semantic understanding and a self-adaptive learning mechanism, the system can accurately identify and match new technical fields and interdisciplinary industry requirements, and the limitation of a traditional keyword matching method is effectively overcome. According to the method, the production and research link efficiency is remarkably improved, collaborative fusion of an industrial chain and a scientific research chain is deepened, and comprehensive technical support and service guarantee are provided for industrial technology development and conversion of scientific research achievements of colleges and universities.

The invention discloses a method and a system for integrally importing information collection data and automatically matching quota, and relates to the technical field of engineering cost data processing, and the method comprises the following steps: obtaining information collection data and quota data, and carrying out the characterization processing of the information collection data and the quota data, and forming information collection feature information and quota feature information; constructing a matching relationship between the capital collection feature information and the quota feature information, and generating a corresponding matching relationship object; establishing dependency description information for the matching relation object; when the information collection data or the quota data are changed, whether the change affects the existing matching relation or not is judged based on the dependency description information; and when judging that the existing matching relationship is influenced, only executing re-matching processing on the influenced matching relationship object. According to the invention, by introducing a matching dependency description mechanism, accurate management and control of the information collection data and the quota matching relationship are realized, and only necessary rematching processing is triggered when the data is changed, so that the matching accuracy is improved, and the system calculation overhead is reduced.

This invention provides a big data-based intelligent matching method for upstream and downstream enterprises in the chemical industry chain, belonging to the technical field of matching upstream and downstream enterprises in the chemical industry chain. The method includes collecting multi-dimensional feature data of the chemical industry chain; analyzing basic enterprise data using an industry chain attribute matching algorithm; analyzing the dynamic changes in supply and demand relationships with market cycles; constructing a quantitative model of supply and demand impact, analyzing the influence weight of dynamic supply and demand data on initial enterprise matching data; and building an intelligent matching model of the industry chain to evaluate the matching process of upstream and downstream enterprises in real time. By integrating multi-dimensional feature data of enterprises and utilizing industry chain attribute matching algorithms, dynamic supply and demand analysis methods, quantitative supply and demand impact models, and intelligent matching models of the industry chain, this invention achieves accurate matching and dynamic optimization of upstream and downstream enterprises in the chemical industry chain, solving problems such as reliance on manual experience in matching, untimely response to dynamic changes in supply and demand, and insufficient risk management capabilities in existing technologies.

The application provides a government-enterprise matching method and system based on a large model and a storage medium, and the method comprises the following steps: performing character recognition on a policy document to obtain a policy text, inputting the policy text into a pre-trained large model to perform policy splitting and obtain policy content; performing conditional extraction on the policy content according to an extraction prompt word to obtain policy extraction information, obtaining enterprise information to be matched and government-enterprise matching requirements; determining a matching prompt word according to the government-enterprise matching requirements, and controlling the pre-trained large model to perform government-enterprise matching on the enterprise information to be matched and the policy extraction information according to the matching prompt word to obtain a government-enterprise matching result. According to the embodiment of the application, the pre-trained large model can automatically perform government-enterprise matching on the enterprise information to be matched and the policy extraction information, and it is not necessary to perform policy analysis and government-enterprise matching in an artificial manner, so that the government-enterprise matching efficiency is improved.

The application relates to the technical field of data processing, and discloses an industry chain investment targeting matching method and system based on big data analysis, which comprises the following steps: extracting the industry characteristics of each target enterprise data, taking the industry characteristics as a starting point, extending and constructing an industry chain graph based on all target enterprise data, determining the similarity of the to-be-matched industry characteristics and the industry chain graph, determining the matched enterprises based on the similarity and selection orientation, determining the model operation score of the matched enterprises based on a recommendation model, historical operation information and historical enterprise behavior, determining the evaluation operation score of the matched enterprises based on the historical operation information and the historical enterprise behavior, determining the enterprise comprehensive operation score of the matched enterprises according to the relationship between the model operation score and the evaluation operation score, and judging whether the matched enterprises are determined as the targeting matching results according to the enterprise comprehensive operation score. The application ensures the stability and reliability of the targeting matching.

The invention relates to a concrete area productivity collaborative scheduling and resource dynamic matching method, which belongs to the technical field of data processing, and comprises the following steps: S1, setting a matching rule by a platform according to settled data; s2, selecting whether there is a contract according to a user, if there is no contract, determining that the demand is a new demand, and entering the matching process of S3; if the contract exists, not entering the matching process; s3, the platform carries out automatic matching on the user demands according to the matching rule, after matching succeeds, the two transaction parties carry out confirmation according to the matching result, and a subsequent platform transaction process is carried out; s4, when the automatic matching fails or the matching results of the two transaction parties are not consistent, the platform performs manual matching; and S5, the platform regularly adjusts the factors for setting the matching rules according to the settled data, so that the adaptability of the matching rule model is improved. According to the invention, the information isolated island of the concrete industry is broken, and the two demanding parties can quickly complete the transaction by establishing the digital market infrastructure.

The invention discloses a multi-cycle gradient preferential settlement scheme matching method and system, and the method comprises the steps: obtaining a settlement request of a to-be-settled product in a receiving order, and obtaining the delivery time and the performance of the to-be-settled product; receiving a division criterion selection instruction, and querying a preferential accumulation period division criterion related to the division criterion selection instruction from the database; according to the queried preferential accumulation period division criterion, determining a preferential accumulation period to which the delivery time of the to-be-settled product belongs; querying a total performance amount of the preferential accumulation period and a settlement scheme corresponding to the total performance amount from a database; and determining the settlement price of the to-be-settled product according to the settlement scheme and the performance amount of the to-be-settled product. According to the invention, automatic matching of settlement schemes during combination and discount of multiple products is realized, and the determination efficiency and accuracy of the settlement schemes are improved.

The invention belongs to the technical field of complex heavy equipment collaborative design, and particularly relates to a heavy equipment collaborative design task and personnel matching method considering proficiency, which comprises the following steps: S1, constructing a task information table and a personnel information table; s2, calculating the similarity among the tasks, and constructing a task similarity matrix; s3, constructing a proficiency prediction model; s4, taking the minimum total time and the minimum total cost as optimization objectives, and constructing a multi-objective optimization model; s5, solving the multi-objective optimization model to obtain a personnel-task matching scheme; in the solving process, based on a task information table, a personnel information table, a task similarity matrix and a proficiency prediction model, the proficiency of each personnel engaged in each task is solved; and S6, the obtained personnel-task matching scheme is used to carry out personnel scheduling and distribution. According to the method, the defects of an existing static evaluation model can be overcome, and the rationality, efficiency and cost effectiveness of human resource allocation in collaborative design of complex heavy equipment are improved.

The application discloses a large model intelligent matching method for serving financial scene requirements, comprising the following steps: 1. evaluating the financial capability of the large model: obtaining a capability feature vector based on evaluation indexes and scores of each capability dimension; 2. analyzing the financial business scene requirements: combining three methods of field keyword mapping, semantic vector matching and large model evaluation, and using a dynamic weight modulation and multi-level semantic fusion method to convert the results of the three methods into requirement feature vectors; 3. automatically matching the financial business scene and the large model: obtaining a matching score by matching the capability feature vector and the requirement feature vector through a double-layer weight matching method with an additional reward and punishment mechanism, and selecting the best scene option based on the matching score result. The application also discloses a large model intelligent matching device for serving financial scene requirements. The application changes the traditional manual selection method, greatly improves the efficiency of large model matching in the financial business scene, and can be widely applied in the field of financial technology.

This application discloses a method, apparatus, storage medium, and computer equipment for matching reviewers, relating to the field of office automation. This application calculates the capability scores of all reviewers corresponding to the current unit, then calculates the required capability score of the current unit, and finally calculates the matching rate between each reviewer and the current unit. The reviewer with the highest matching rate is selected as the optimal reviewer for the current unit. This method of selecting the optimal reviewer based on the matching between the capability scores of the reviewers in a unit and the required capability score of the current unit reduces the workload of selecting the optimal reviewer for each unit, greatly improving review effectiveness and efficiency.

The invention provides a fault case intelligent matching method and system applied to operation and maintenance technology services, and relates to the field of operation and maintenance technology services, and the method comprises the steps: building a current fault space-time evolution model through collecting related data of chain store intelligent equipment; extracting complete intervention process data of historical fault cases, and encoding to generate an efficiency encoding packet; performing resource effect dynamic balance adaptation to calculate an intervention efficiency matching degree; generating a deduction result report based on intervention efficiency matching degree simulation deduction; and finally, elastically adjusting and testing the cases conforming to the threshold value to generate a target matching fault case set. According to the method, fault spatio-temporal evolution and intervention efficiency are comprehensively considered, fault cases can be accurately matched, and the operation and maintenance efficiency and quality are improved.