52 results about "Attribute weight" patented technology

Apparatuses, systems, and techniques to cause weights assigned to properties of chatbot answers to be dynamically adjusted. In at least one embodiment, one or more neural networks are used to characterize one or more chatbot queries and cause weight values assigned to properties of answers to the one or more chatbot queries to be dynamically adjusted, based, at least in part, on the characterization of the one or more chatbot queries.

The invention relates to the technical field of intelligent scheduling, and discloses an intelligent scheduling control method and system based on multi-objective optimization, and the method comprises the steps: obtaining an attribute set of different tasks, obtaining a disturbed task attribute initial value, and carrying out the calculation to obtain an importance score of the tasks during initialization; based on the task attribute initial value and the attribute weight, calculating the conflict degree between the tasks through the task scheduling conflict degree; defining an optimization target according to the importance score of the task and the conflict degree between the tasks, and automatically generating an association rule; and after optimization, calculating the task importance score of the final time step based on the task attribute value and the attribute weight of the final time step, and generating a final scheduling scheme according to the final importance score of each task. According to the method, through dynamic conflict evaluation, adaptive weight adjustment and a closed-loop feedback mechanism, the environmental adaptability, global optimization and stability of a scheduling system are remarkably improved, and resource competition and time delay are effectively reduced.

The invention discloses an attribute reduction method and device based on neighborhood separability, equipment and a storage medium. The method comprises the following steps: determining dual-neighborhood information grains of each object based on a decision attribute set; based on the dual-neighborhood information particles of each object and the to-be-optimized attribute weight matrix, quantifying the internal aggregation degree and the external dispersion degree of the universe about the condition attribute set; and constructing an objective function based on the internal polymerization degree and the external dispersion degree, and solving the objective function to perform attribute reduction. The local structure information of the object is fully utilized, the internal aggregation degree and the external dispersion degree of the discourse domain about the condition attribute set are quantified, and the target function is established by combining the internal aggregation degree and the external dispersion degree, so that the problem that in the feature selection process, the feature selection efficiency is high is solved. The problem of low algorithm efficiency caused by repeatedly calculating various binary relationships or information particles and selecting attributes one by one is solved, the calculation efficiency of feature selection is improved, and the calculation efficiency of machine learning is further improved.

The invention provides a multi-attribute score fusion recommendation method based on user preferences and attention, and the method comprises a data collection and preparation module which is used for obtaining and cleaning user, project and score data; the attribute coding module is used for carrying out numerical coding on user and project attributes; the multi-attribute feature fusion module is used for obtaining user-project comprehensive representation by splicing attribute vectors and utilizing a deep neural network to extract interaction features; the fusion attention mechanism module is used for dynamically distributing each attribute weight and realizing weighted fusion of multi-attribute features; the comprehensive evaluation score calculation module is used for predicting the score of the user on the project according to the fusion feature and the attention distribution; the global preference modeling module is used for fusing historical behaviors of the user and improving the modeling capability of the overall preference; the recommendation result output module is used for generating a personalized recommendation list; and the method evaluating and updating module is used for evaluating recommendation performance and dynamically optimizing the model.

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 relates to the technical field of artificial intelligence and talent management, in particular to a talent evaluation model and method based on GNN and a talent selection method. The talent evaluation model comprises a dimension prediction model for training each attribute weight; the talent score is the weighted summation of each attribute and the attribute weight; the training process of the dimension prediction model comprises the following steps: acquiring full-attribute data as a training set; acquiring full-attribute data marked with expert scores as a verification set; the dimension prediction model is used for deleting a target attribute from the training set and speculating the target attribute and the weight thereof in combination with the residual attributes; traversing all attributes through the target attribute to obtain the weight of each attribute; on the verification set, the attribute scores of the talents and the corresponding weights are multiplied and summed to serve as predicted talent weights, and the attribute weights are verified through comparison of the talent weights and the expert weights; and training of the dimension prediction model and verification of the attribute weight are cycled until the attribute weight reaches a convergence condition. According to the method, the feature column needing to be learned is masked by using the cyclic mask method, and the masked column is learned by using other feature columns, so that the problems of insufficient data and non-uniform data can be efficiently solved.

The invention provides an optical cable fault rapid positioning method, system and equipment, and the method comprises the steps: obtaining multi-dimensional attribute parameters of a target optical cable section, specifically including a static attribute, a dynamic attribute and an environment attribute for evaluating the operation state of an optical cable; inputting the multi-dimensional attribute parameters into the weighted analysis model and the trained machine learning prediction model in parallel; wherein the weighted analysis model calculates and outputs a first candidate fault position set based on an attribute weight data set, and the attribute weight data set comprises weight scores defined for different types of attribute parameters; analyzing the current multi-dimensional attribute parameters by using the trained machine learning prediction model, and outputting a second candidate fault position set; and fusing overlapped data of the first candidate fault position set and the second candidate fault position set to generate comprehensive fault positioning information. Based on the method, the invention also provides an optical cable fault rapid positioning system and equipment. According to the method, the accuracy of fault positioning and the decision robustness are remarkably improved.

The invention discloses an industrial design component library management and automatic matching method and system, and relates to the technical field of XXX industrial design, and the method comprises the following steps: obtaining the dimension attribute of a component, and carrying out the fusion modeling of the dimension attribute of the component; obtaining design requirements, extracting keywords according to the design requirements, matching the keywords with semantic tags in the component library, and obtaining semantic similarity based on a cosine similarity algorithm; constructing a matching model according to the semantic similarity in combination with the dimension attributes of the components, and comprehensively evaluating the dimension attributes of the components by using a weighted multi-dimensional data fusion technology to obtain matching degree scores of the components and the design requirements; and comparing the matching degree score with a preset matching threshold value, evaluating an automatic matching effect according to a comparison result, and dynamically updating the attribute weight of the component according to the automatic matching effect. According to the method, the problem of separation of semantic understanding and attribute matching is solved, rapid matching of components and design requirements is realized, the design period is remarkably shortened, and the design efficiency is improved.

The invention discloses a multi-stage human-machine attribute value alignment method and system based on a large model, the method comprising: using multiple large models to obtain the decision attribute weights of the #imgabs1# stage according to the situation information of the #imgabs0# stage, and then using weighted factors to fuse to obtain the machine intelligence weights of each attribute of the #imgabs2# stage; using a group decision method to obtain the human decision weights of each attribute of the #imgabs3# stage; based on the machine intelligence weights and human decision weights of each attribute of the #imgabs4# stage, a loss function is established; by calculating the partial derivative of the loss function with respect to the large model weight factor, using the gradient descent method and updating the weighted factors of each large language model on each attribute in the #imgabs5# stage based on the partial derivative calculation value, for the attribute weights output by the fusion of each large language model in the #imgabs6# stage; looping the above process to finally realize the gradual alignment of human-machine attribute weights. The invention can reduce decision-making risks and effectively improve the accuracy and reliability of artificial intelligence in complex decision support.

The invention discloses a classification data clustering method and system based on double-space learning, and the method comprises the following steps: S1, receiving and preprocessing a data set containing a plurality of classification attributes, carrying out the unified format coding conversion of classification attribute values, and initializing a clustering affiliation matrix and a measurement weight; and S2, constructing a new metric function, dynamically adjusting the weight of a metric space, minimizing a clustering objective function, and optimizing the weight and clustering division through alternate iteration. S3, optimizing attribute subspace weights, evaluating importance of the attributes in clustering, defining clustering compactness, measuring clustering separability of the attributes by using a Hellinger distance, and calculating attribute weights; and S4, in combination with metric space learning and attribute value subspace learning, cooperatively updating a metric weight and an attribute weight, optimizing an objective function, and realizing clustering of classified data. Compared with the prior art, the method can adaptively adjust the measurement and attribute weight, overcomes the limitation of a fixed rule, and remarkably improves the clustering performance and generalization capability.

The invention discloses an NDN (Named Data Networking) caching strategy based on offline-online deep reinforcement learning, which relates to the field of named data networks and comprises the following steps: adding an interest request table in a router of an NDN; extracting cached log data; dynamically distributing each attribute weight by using a self-attention mechanism; performing offline training, and extracting an optimal cache strategy; adjusting and optimizing the strategy by using an IQL model online fine adjustment module; and the on-line fine tuning module is executed periodically. According to the method, large-scale simulation is carried out for the real topology TISCALI, and compared with an existing typical caching strategy, the method can obviously improve the overall caching hit rate in the network, reduce the time delay, reduce the link stretch rate and improve the user experience.

The invention discloses an identification intercommunication analysis method and system for heterogeneous science and technology resources, and relates to the technical field of identification analysis, and the method comprises the steps: collecting heterogeneous science and technology resource data, carrying out the hierarchical definition of triple nodes, analyzing the attribute association degree, and carrying out the weak association elimination through a SimHash screening technology, and analyzing the attribute similarity among the resources based on the attribute vector distance of the triple node data, and performing matching analysis of function vectors according to the support information of the resources to the functions to establish the function association of the resources. According to the method, a multi-dimensional supplementary association mechanism is provided for function association values based on task function support conditions among resources through definition and matching of function vectors, attribute weights of attribute similarity are adjusted based on dynamic loads, the real-time states of the resources have a dynamic adjustment effect on the association weights, and the dynamic load adjustment effect of the resources is improved. And the adaptation flexibility of the system is improved.

The invention relates to the technical field of intelligent locks, in particular to an intelligent lock with an intelligent acquisition function and a control method thereof.Importance levels of corresponding acquisition functions are represented through attribute weights, then the attribute weights are corrected through data calling records, and personalized weights better conforming to user habits are obtained; then a fitness function is determined based on the personalized weight, so that an optimal acquisition function calling strategy can be found through an optimization algorithm, and the calling strategy not only can select and reject a plurality of acquisition functions according to the residual electric quantity so as to prolong standby time, but also can match user habits and reserve common and important acquisition functions of a user; the purpose of balancing energy consumption and functions of the intelligent lock is perfectly achieved, it is ensured that safety and convenience are provided, high efficiency and energy efficiency are kept, user experience is greatly improved, and good prospects are achieved.

The invention relates to a large-scale heterogeneous multi-attribute equipment scheme clustering decision-making method and a medium. The method comprises the following steps: uniformly converting heterogeneous attribute data in a digital twin decision-making information system into binary connection numbers; determining an attribute weight by adopting an MEREC method, and constructing an attribute non-compensable value based on the attribute weight and the binary connection number; generating a scheme similar network according to the attribute non-compensable value; performing network clustering operation on the scheme similar network, and outputting a scheme clustering result; and based on the scheme clustering result, calculating the comprehensive target distance of the scheme in the cluster by adopting the grey target theory, and fusing the global value of the scheme and the comprehensive target distance to generate a final scheme score. According to the method, the problems of heterogeneous information fusion distortion, attribute non-compensatory mechanism deficiency and low large-scale scheme clustering efficiency in a digital twinborn decision scene are solved by constructing heterogeneous data unification-attribute non-compensatory mechanism modeling-network clustering-intra-cluster decision.

The embodiment of the invention discloses a content pushing method and device, equipment and a storage medium, and relates to the technical field of computers, and the method can comprise the steps: obtaining a target operation information table of a target account for historical pushing content; wherein the target operation information table comprises each piece of target operation information in a preset historical time period; based on an operation attribute field of each piece of target operation information, a content type field of each piece of target operation information and an attribute weight corresponding to each candidate operation attribute, determining at least one preference content type corresponding to the target account from each candidate content type; and performing content pushing on the target account based on each preference content type. By adopting the technical scheme of the embodiment of the invention, the accuracy of the content pushed to the user by the APP can be improved, so that the waste of pushed resources can be reduced, and the pushing efficiency is improved.

The application provides a material similarity analysis method based on a semantic library and a knowledge graph, comprising the following steps: S1: judging whether the decisive attributes of two materials are similar, if any of the decisive attributes is not similar, then the two materials are non-redundant materials, otherwise, entering S2; S2: calculating the similarity of the field part according to the non-decisive attribute weight and the distance of each field; S3: combining all the knowledge graph paths and weight values to calculate the similarity of the graph part; S4: adding the similarity of the field part and the similarity of the graph part to obtain the final material similarity, and judging whether the two materials are high similarity through the final material similarity. The material similarity analysis method provided by the application optimizes the accuracy of the similarity algorithm.

The invention discloses a weighted analysis method for multi-attribute data in advanced geological forecast, belongs to the technical field of detection data optimization, and can solve the problems that redundant and mutually exclusive data are difficult to eliminate and missing and abnormal data processing effects are poor when the multi-attribute data are processed by an existing method. The method comprises the following steps: S1, normalizing various attribute data obtained by each grid point in a target area in advance geological forecast to obtain normalized data of each attribute in each grid point; s2, determining a single attribute weight coefficient of each attribute in each grid point according to the standardized data of each attribute in all the grid points, and determining a comprehensive attribute weight coefficient of each grid point according to the single attribute weight coefficient; and S3, determining update data of each attribute of each grid point by using the normalized data and the single attribute weight coefficient of each attribute in each grid point and the comprehensive attribute weight coefficient of each grid point. The method is used for multi-attribute data analysis.

The invention discloses a zero sample fault detection method based on weighted semantic consistency embedding, and belongs to the field of industrial fault diagnosis. The method comprises the following steps that firstly, industrial equipment is used for collecting multi-mode sensor data, and standardized data are obtained through normalization, wavelet denoising and time sequence segmentation preprocessing; determining a fault attribute dimension based on domain expert knowledge, assigning each fault class to form a semantic attribute vector, and constructing a semantic attribute weight matrix through mutual information gain between attributes and fault tags; embedding data and semantic attributes into a shared space by using a data encoder and a semantic attribute encoder, decoding and calculating reconstruction loss by combining a data decoder and a semantic attribute decoder, and introducing weight to construct weighted semantic consistency loss; combining the two types of loss to train an encoder and a decoder, and extracting training data features by using the trained encoder and training a multi-attribute classifier; and finally, in a zero sample scene, extracting embedded features without fault data, inputting the embedded features into an attribute classifier, and matching fault categories through a maximum posterior probability. According to the method, the equal-weight hypothesis defect of an existing semantic consistency embedding method is broken through, key semantics are strengthened through mutual information gain weights, consistency loss is weighted to resist noise interference, and the precision, robustness and generalization ability of zero sample fault diagnosis are remarkably improved.

Embodiments of this application disclose a node selection method performed by a first node in a blockchain. In the embodiments of this application, nodes in a blockchain are grouped in an order of attribute weights of the nodes, to obtain a plurality of node groups. A node group whose attribute weight meets a preset condition is selected from the plurality of node groups, to obtain a target node group, and a preset quantity of candidate consensus nodes are selected from the target node group. A non-candidate consensus node in the target node group is stored into an initial node group. The initial node group is used as a new target node group, and the operation of selecting a candidate consensus node from the target node group is performed. Candidate consensus nodes are broadcast to one or more second nodes in the blockchain for verification.

The invention discloses an access control method and system based on a WIAA-ZTAC model, and relates to the technical field of network security, and the method comprises the steps: receiving an application access request of an access subject; the method comprises the following steps: verifying an application access request through a zero-trust access control WIAA-ZTAC model combined with a Web identity identifier and an attribute score to obtain a main body trust value, and dynamically authorizing minimum permission access when the main body trust value reaches a trust threshold value of an access resource; wherein the WIAA-ZTAC model carries out strategy verification according to a preset judgment attribute, trust evaluation is carried out on a request passing the judgment attribute verification based on an evaluation attribute, a fuzzy analytic hierarchy process is adopted to dynamically allocate each attribute weight, and a subject trust value is calculated. According to the invention, each request is evaluated and authorized, so that the method has higher timeliness and is safer and more reliable.

The invention discloses an IT asset health degree assessment method, system and device and a computer storage medium, and relates to the technical field of asset security, and the method comprises the steps: obtaining asset data of IT assets, and carrying out the standardization processing; respectively calculating a protection attribute weight, a service attribute weight, a compliance loss score, a risk attribute loss score and an attack attribute loss score according to the conditions of the protection index, the service index, the compliance index, the risk index and the attack index; calculating the asset health degree, wherein the calculation formula of asset health degree evaluation is as follows: asset health degree = total score-business attribute weight * protection attribute weight * risk attribute loss score-attack attribute loss score-compliance loss score; and evaluating the health condition of the assets based on the score of the health degree of the assets.

The invention provides a sampling point attribute characteristic representativeness measurement method and device, and belongs to the technical field of data processing, and the method comprises the steps: determining a target sampling point based on a target attribute, and obtaining an auxiliary attribute data set of the target sampling point and a measurement unit of the auxiliary attribute data set; and performing spatial superposition analysis on the auxiliary attribute data set and the measurement unit, and determining an attribute feature representative measurement value of the target sampling point by integrating the attribute category number proportion of each auxiliary attribute in the measurement unit, the category pattern spot area proportion of the target sampling point in the measurement unit and the attribute weight of the auxiliary attribute. According to the method, the Thiessen polygons of the sampling points are used as measurement units, the contribution degree of each auxiliary attribute related to the target attribute data to the target attribute is quantified, the attribute feature representativeness of each sampling point is obtained, the quality and availability of the sampling point data can be evaluated through the attribute feature representativeness of the sampling points, and the accuracy of the sampling point data is improved. And the uncertainty of the sampling point data is reduced, so that the accuracy and reliability of specific analysis and practical application of the sampling point data are ensured.

The application discloses a kind of based on multiple confidence medical data missing value batch repair method, the method includes: using attribute weight updates sample confidence, missing sample set is introduced into model training;Using sample confidence optimization loss function, fill the missing value of data set;The sample confidence is calculated according to the attribute relationship between samples, according to the data attribute to be predicted and the number of missing values in sample, different confidence is given to sample;The influence degree of sample in model training process is adjusted by dynamic selection of confidence.Optimize model architecture, so that the network can fill the missing value of multidimensional data in batches at a time, can also eliminate the identity mapping problem of network transfer function, and enhance the intercorrelation between nodes.The application improves the utilization of data, improves the filling accuracy and filling efficiency of data.

The invention provides a PLC isomer dynamic access control method based on attribute identification, which solves the problem that the access authority of an industrial PLC system is difficult to dynamically control in real time in a complex heterogeneous environment, and remarkably improves the safety protection capability and the control efficiency of the system. Firstly, attribute data such as user identities and device types are collected for attribute importance evaluation and weight preprocessing, and attribute weight distribution is achieved; secondly, constructing a deep trust model based on a full-connection neural network, training the model according to the preprocessed data, outputting a dynamic trust level, and executing a hierarchical access decision; and finally, designing a neural network self-organizing mechanism, pruning and optimizing redundant nodes of the network by calculating comprehensive importance scores of neurons, and realizing dynamic simplification of a model structure. According to the method, the security access level of the industrial-grade PLC can be calculated through different attribute data, so that a necessary basis is provided for the security operation control of the PLC.

The present invention provides a method and system for automatic process processing of accounting data. Data is collected by connecting to the internal business system of an enterprise through an adapter interface, and the data is cleaned by combining OCR technology and a professional vocabulary library. An improved random forest algorithm is used to construct a classification model based on attribute weights according to accounting professional knowledge, so as to achieve accurate classification and standardization of data. According to preset financial process rules, reimbursement, procurement payment and other processes are automatically processed and monitored in real time, and timely warnings are issued in case of abnormalities. The processed data is encrypted and stored, and a backup and authority management mechanism is established. Financial statements can also be automatically generated and displayed visually. This invention solves the problems of low efficiency and easy errors in traditional accounting data processing, improves data processing efficiency and accuracy, ensures data security, and provides efficient support for corporate financial management.

The present invention discloses a method for locating a firmware buffer overflow vulnerability patch reinforcement node, and belongs to the field of information security technology. The locating method comprises the following steps: Step 1, vulnerability exploitation path perception: parsing the binary data of the firmware, restoring the calling relationship between functions, performing mixed symbolic execution on the buffer operations involved in the vulnerability, and capturing the vulnerability exploitation process; Step 2, buffer attribute weight allocation: using an objectively weighted entropy weight method to integrate and sort a variety of buffer operation data, and improve the exploitation details of the overflow vulnerability from the perspective of multiple attribute aggregation; Step 3, patch reinforcement node positioning: establishing a multi-attribute decision model based on probabilistic hesitant fuzzy elements and prospect theory to locate the patch reinforcement node of the firmware buffer overflow vulnerability. The present invention is conducive to quickly and efficiently positioning the key nodes of patch reinforcement.

To specify a cause of a change to an abnormal state when the change occurs alone.SOLUTION: A cause analysis device comprises an acquisition unit; and a calculation unit comprising a deviation score calculation unit, a relation weight calculation unit, a property weight calculation unit, and a cause score calculation unit. The acquisition unit acquires a reference data group including object variables and explanatory variables, and object data including object variables and explanatory variables. The deviation score calculation unit calculates deviation scores showing deviation degrees of the explanatory variables of the object data to distribution of the explanatory variables of the reference data group. The relation weight calculation unit calculates a relation weight representing strength of a relation between the object variables and the explanatory variables of the reference data group. The property weight calculation unit calculates a property weight representing coincidence degrees of values of the object variables and the explanatory variables of the object data to distribution of the object variables and the explanatory variables of the reference data group. The cause score calculation unit calculates cause scores of the explanatory variables to variation of the object variables of the object data on the basis of the deviation scores, the relation weight and the property weight.SELECTED DRAWING: Figure 1

A price analysis method and system based on a multi-attribute template, comprising: constructing a multi-attribute template, generating attribute weights and coding rules through association rule mining, and fusing commodity descriptions and business fields into attribute vectors; calculating similarity based on weighted Euclidean distance, normalizing attention weight through Softmax, and weighting and summing historical prices to obtain reference price; calculating deviation rate and comparing with threshold value to perform hierarchical disposal; constructing a knowledge graph to detect associated abnormalities and price avoidance paths; and implementing batch backtracking based on a version-identified feature cache table. The present application realizes fine-grained price analysis and hidden abnormality mining, and supports dynamic template updating, coding rule adaptive calibration and multi-level rollback mechanisms, significantly improving system robustness and cold start capability.

The invention discloses an intelligent automatic distribution method, system and device for college paper marking experts and a medium. The method comprises the steps of calculating a first attribute weight, a second attribute weight and a third attribute weight; inputting the historical data corresponding to each fourth attribute into a linear regression equation to obtain a fourth attribute weight of each fourth attribute; selecting a preset number of target experts; based on the multiple first attributes, the first attribute weights, the multiple second attributes, the second attribute weights, the multiple third attributes, the third attribute weights, the multiple fourth attributes and the fourth attribute weights, the test questions and the target experts are matched, and a matching success record set is obtained; and performing matching effect evaluation on the successful matching record set to obtain an evaluation result, and if the evaluation result is greater than or equal to a preset threshold value, taking the successful matching record set as a target matching result, and allocating paper marking experts according to the target matching result. The accuracy of matching between the expert and the test paper to be read is improved, and the matching efficiency is improved.

The application discloses an industry chain knowledge question and answer interaction method and system based on a large model, groups each process node contained in a target industry chain based on attribute weight values and continuity between process nodes and upstream and downstream relationships, so that the obtained node set contains process nodes that not only reflect the relationship between the upstream and downstream, but also can realize the solidification of the relationship between the process nodes based on the process sequence through the arrangement order between the node sets, and then realize the solidification of the knowledge structure of the target industry chain, reduce the phenomenon that the knowledge is too loose and it is difficult to find the logic and rules contained therein due to the weak knowledge structure, and provide conditions for the model to generate targeted and clear answers. The application is applicable to the application of electronic digital data processing technology in the field of question and answer interaction related technology.