123 results about "Case-based reasoning" patented technology

The present invention provides systems and methods for dynamic detection and prevention of electronic fraud and network intrusion using an integrated set of intelligent technologies. The intelligent technologies include neural networks, multi-agents, data mining, case-based reasoning, rule-based reasoning, fuzzy logic, constraint programming, and genetic algorithms. The systems and methods of the present invention involve a fraud detection and prevention model that successfully detects and prevents electronic fraud and network intrusion in real-time. The model is not sensitive to known or unknown different types of fraud or network intrusion attacks, and can be used to detect and prevent fraud and network intrusion across multiple networks and industries.

A case-based reasoning system is disclosed that includes a case database and a search engine. The case database is capable of storing a plurality of cases that each include one or more attributes that each have an associated match weight. Match weights of attributes in different cases are separately specified. In response to receipt of an incident including one or more input terms, the search engine scores the relative closeness of a selected case to the incident utilizing the match weights of attributes in the selected case that match input terms in the incident.

The invention discloses an intelligent urban construction examining and approving method based on a case-based reasoning technology. The intelligent urban construction examining and approving method based on the case-based reasoning technology comprises the following steps of constructing an examining and approving case library; inputting new examining and approving case and model parameter information; submitting jogs to a Hadoop cluster to search KNN (k-nearest neighbor algorithm) Map Reduce cases; statistically analyzing a searching result on the basis of a 'weighted integral model'; evaluating and correcting the cases; and performing distributed full-text searching on examining and approving data. The intelligent urban construction examining and approving method has the advantages that by the method, the circumstance of manual examination and approval application at present can be changed, the work efficiency is improved, the basis on examining and approving is increased, and an examining and approving process is intelligent. Distributed searching can be carried out by using a Hadoop frame and a MapReduce frame through a cloud computing center, and a distributed case searching model based on the case-based reasoning technology is established. The 'weighted integral model' is creatively raised to statistically analyzing searched similar cases, and a guidance which is beneficial to new examining and approving cases is obtained.

The invention provides an automobile intelligent fault diagnosis and maintenance auxiliary method and system combining subjective and objective information and a cloud model. The method comprises steps of acquiring automobile objective original diagnosis data through an OBD diagnosis port; carrying out preprocessing on the data, judging whether fault codes are in the preprocessed data, and if yes, analyzing the fault codes and providing automobile maintenance assistance; if no, carrying out quantitative conversion on acquired automobile subjective uncertainty fault sign qualitative value through a cloud model method, and comparing the qualitative value with a standard value in an automobile system standard database, wherein the system automatically diagnoses automobile fault parts initially; diagnosing the automobile fault reason through a case-based reasoning method based on a normal cloud mode; and using the rule-based reasoning to finish diagnosis and elimination of automobile faults. According to the invention, automobile faults caused by a complex condition that an automobile has a fault without a fault code or by damage of non-electronic elements are solved; automobile fault diagnosis can be highly efficiently and rapidly finished; maintenance steps are normalized; and maintenance price is properly set.

The invention relates to a robot welding technology parameter optimization method based on CBR and RBR, and belongs to the technical field of robot welding. The robot welding technology parameter optimization method involves a welding technology input module, a welding technology expert system and a welding technology output module. The welding technology input module receives welding tasks and welding resources and further submits the welding tasks and the welding resources to the welding technology expert system. A query engine in the welding technology expert system reuses historical welding cases, and it enters a reasoning engine for case-based reasoning on the basis of rules if no similar welding technology cases exist. A range value exists in the reasoned welding technology, particleswarm optimization in a welding technology parameter optimization module is adopted for multi-objective optimization of welding technology parameters, the optimal welding technology parameter combination is determined, an exact value is acquired for a welding robot to execute, and robot welding intelligence is realized. Production efficiency can be improved and the effect is remarkable.

The invention relates to prediction and decision support method, in particular to a small-signal stability prediction and decision support method. In the invention, the case-based reasoning theory and the OAPID (Oscillatory Active Power Increment Distribution) calculation are creatively adopted, data is measured in real time based on a WAMS (Wide Area Measurement System), and small-signal stability prediction is realized according to the level of similarity between the existing case (especially the operating manner prior to oscillation corresponding to a frequent low-frequency oscillation event recorded by the WAMS) and the current operating manner. On the other hand, if the prediction results show that the weak damping mode exists in the current operating manner or low-frequency oscillation has been occurred, the small signal stability of the system is improved by scheduling according to the operating manner indicated by the case with higher level of matching similarity and the damping ratio meeting conditions by targeting at increasing the damping ratio of the corresponding mode (corresponding weak damping mode or low-frequency oscillation corresponding mode).

The invention discloses a method for diagnosing engine integrated faults based on a fuzzy semanteme network. The method is characterized by comprising the following steps: establishing an embedded platform based on an ARM embedded microprocessor and a data acquiring module based on a field programmable gate array (FPGA), and establishing a fault knowledge acquiring method based on a fuzzy semanteme network model, a case-based reasoning subsystem and a guided interactive reasoning subsystem on the embedded platform, wherein the fault knowledge acquiring method based on a fuzzy semanteme networkmodel constructs an engine fault knowledge base; the case-based reasoning subsystem is used for quickly diagnosing frequent faults; and the guided interactive reasoning subsystem adopts a man-machineinteraction mode to carry out fuzzy searching and reasoning and can carry out deep diagnosis of the faults. After the data acquiring module automatically detects a fault symptom or a user inputs a fault symptom, an subsystem can be started independently, and two subsystems can also be started and operated together. Each subsystem can realize fault diagnosis independently, and diagnosis results oftwo subsystems can also be combined to carry out integrated diagnosis so as to improve the accuracy of the diagnosis.

A method of establishing a process decision support system. Decision support systems of the kind are used in manufacturing processes, particularly industrial manufacturing processes, to monitor the performance of the processes in view of controlling the processes in order to optimise process production and quality. The method includes collecting process data of a process, collecting operational data of a process, and fusing the process data and operational data to create a fused data set (such as a consolidated rule set) of the process upon which process decisions (such as control decisions) may be taken. The process data and operational data may be fused according to methods of rules-based knowledge fusion, mathematical knowledge fusion, or case-based reasoning knowledge fusion.

An intelligent plant development library environment is presented. Contemplated environments comprise an EPC knowledge system capable of incorporating know-how of one or more construction firms in the form of assembly objects. Assembly objects represent construction components (e.g., bolts, cable trays, pipes, processing units, deliverables, etc.) that can be incorporated into a plant design. Assembly objects are stored in an assembly database and include available contexts considered relevant to the assembly objects. An inference engine is utilized to derive a specified context related to a plant design from one or more design tools. The inference engine applies rule sets to infer which assembly objects to instantiate as construction objects. The inference engine can further configure the design tools to incorporate the instantiated construction objects into a plant construction project model. Example rule sets include forward chaining rules, backward chaining rules, case-based reasoning rules, inductive reasoning rules, or abductive reasoning rules.

With frequent occurrence and severe harm of vegetable insect pests, agricultural benefits are reduced and consumer health is threatened because of improper insect pest diagnosis and control measures. The improvement of insect pest control level can guarantee large-scale plantation, high yield and high quality of vegetables, and an insect pest diagnosis expert system can assist in diagnosing insect pests and relieving the contradiction of low quality of agricultural planting personnel and insufficient plant protection experts. Aiming at vegetable insect pest occurrence characteristics, by simulating an analysis mode of insect pest diagnosis of plant protection experts, fully considering ways of thinking of users and adopting a case-based reasoning mechanism, an insect pest diagnosis expert system constructing method comprising contents of new case generation, case library construction, insect pest symptom weight calculation, case retrieval and match, human-machine interactive interface and the like is created and a vegetable insect pest diagnosis expert system taking the method as a core is constructed. Moreover, due to an online edition, a standalone version and a personal digital assistant (PDA) version of the vegetable insect pest diagnosis expert system, multi-channel insect pest diagnosis information technical service is provided for users with different requirements.

The invention discloses a gear hobbing method which is characterized in that during hobbing, optimization decision on gear hobbing process parameters comprises the following specific steps: (1) building a gear hobbing process ontology base; (2) expressing a gear hobbing process parameter decision target space; and (3) performing comprehensive optimization decision on the gear hobbing process parameters. The method disclosed by the invention has the advantages that by using the gear hobbing process domain ontology base stored in a database, the method can realize sharing and reuse of the knowledge about the domain of the gear hobbing, and not only can consider the gear hobbing process parameters as a system but also can solve new optimization decision problems of the gear hobbing process parameters by combining case-based reasoning with analytic hierarchy process during use.

The invention belongs to the technical field of coal preparation plant's slime water treatment link and mainly relates to an automatic addition system and a method of slime water treatment equipment thickener drugs. According to the thickener automatic drug dosing system and the method and mainly in the coal preparation plant's slime water treatment link, a PLC, a reasonable sensor and an execution mechanism and a feedforward+feedback control strategy based on case-based reasoning are adopted. dosage can be modified and optimized in advance through thickener feed concentration and flow signals monitored at real time, and thickener overflow and underflow concentration signals are utilized reasonably. System destabilization caused by feedback signal lag is avoided; problems, such as quality reduction of circulating water from a coal preparation plant, high drug consumption, high water consumption, etc., which are caused by inaccurate measurement of thickener overflow and underflow concentration during slime water treatment process in an existing coal preparation plant, poor reliability of a drug dosing execution mechanism, backward automatic control strategy, etc., are solved; and product quality and benefit of the coal preparation plant are enhanced.

The invention relates to a post-rolling cooling long-termed self-learning method based on case-based reasoning, which belongs to the technical field of rolling. The method comprises the steps: Step 1: case construction; Step 2: case searching; Step 3: case reuse; and Step 4: case correction. The post-rolling cooling long-termed self-learning method based on case-based reasoning has the advantages that: based on a large amount of on-site production data, the method makes decisions for a long-termed self-learning coefficient in a control cooling mathematic model through case construction, case searching, case reuse, case correction and other case-based reasoning technologies beginning with how to effectively utilize empirical knowledge. For rolled steel specifications, the method can effectively prevent heads from a super-cooling phenomenon, and simultaneously can significantly improve the set precision of sheet and strip head final-cooling temperature models. The post-rolling cooling long-termed self-learning method based on case-based reasoning can make post-rolling cooling models possess the self-adaptive capability of changing with working conditions, and can significantly improve the head setting precision of the models.

The invention belongs to the field of power transformer fault diagnosis, and particularly relates to an improved power transformer fault diagnosis method. The general thought of a fault prediction model is that a CBR (Case-Based Reasoning) and RBR (Rule-Based Reasoning) serial reasoning way is used, the number of feature values of a transform is limited due to the particularity of a case, an overhauling strategy can be quickly obtained if retrieval matching can be carried out, reasoning time is saved, the historical experience case is effectively utilized, and the fault diagnosis efficiency ofthe system is improved. When no proper cases can be matched with the cases in a factbase, a rule reasoning way is adopted, a rule conclusion which is generated by reasoning and proves to be accuratecan be taken as the overhauling strategy result of the case, and a new case is formed. By use of the method, the problems in the prior art that elaborative diagnosis can not be carried out, diagnosisfault time cost is high, faults can not be quickly positioned and the like are mainly solved.

The embodiment of the invention discloses a method and a device for automatically positioning fault points. The method comprises the following steps: carrying out dialling test height and width parallel routing traversal for service; obtaining the result of the height and width parallel routing traversal; according to the result of the height and width parallel routing traversal, selecting a fault point positioning mode, wherein the fault point positioning mode comprises: case-based reasoning and/ or rule-based reasoning; according to the selected fault point positioning mode, positioning the fault point, and obtaining the fault point. The invention realizes that the fault points can be accurately and automatically positioned.

The invention discloses a voltage and power flow combined control method considering running cost for an active power distribution network. The method comprises the following steps of: (1) automatically detecting/forecasting voltage and power flow in the network, and judging whether an overrun phenomenon occurs or not; (2) building a case library applicable for voltage control of the active power distribution network; (3) retrieving the case library by a nearest neighbor method in a case-based reasoning when the voltage of arbitrary one bus in the power distribution network is recognized to exceed the limit by the system, and searching out N voltage control methods with highest similarity, wherein N is an integer more than 1; (4) calculating a power flow constraint method by a constraint satisfaction algorithm when the power flow of arbitrary one circuit in the power distribution network is recognized to exceed the limit value by the system; (5) carrying out on-line simulation test on the control method; and (6) optimizing the running cost. By the embodiment of the invention, the safety margin requirement for running of the power distribution network is satisfied in real time, and the running reliability and activeness of the power distribution network is effectively improved; and meanwhile, guidance is provided for running and short-term planning of the power distribution network, and favorable economic benefit and social benefit are achieved.

The present invention describes a system and method for using agent-based distributed case-based reasoning to manage a computer network. In particular, the system includes interface agents, distributed case-based reasoning agents, and response agents, which run on hosts in the network. An interface agent monitors a resource in the network and reports an event to an appropriate distributed case-based reasoning agent. The distributed case-based reasoning agent, using one or more case bases, determines a response to the event. An appropriate response agent implements the response.

The invention discloses an intelligent optimal control method and device for the sintered brick tunnel kiln roasting process. The method comprises the steps that A, an extreme learning machine (ELM) is trained based on a genetic algorithm (GA), and a sintered brick tunnel kiln roasting intelligent prediction model with input as tunnel kiln offline and online data and output as quality of sintered bricks is established; B, according to the initial case of operating experience summary of the sintered brick tunnel kiln production process, an optimal case library comprising multiple sets of historical roasting cases of good finished sintered bricks is established; C, the historical roasting case with the highest similarity to the current working condition is worked out according to a case-based reasoning method, and the optimal temperature value of each zone of a tunnel kiln on the current working condition is obtained; and D, according to the intelligent prediction model and the optimal temperature valve of each zone of the tunnel kiln on the current working condition, the corresponding frequency of a frequency converter is calculated, and the frequency of the frequency converter on the current working condition is adjusted according to the calculation result.

The invention discloses an intelligent learning method based on description logic and case-based reasoning. The method comprises the steps of respectively calculating text property and data type property of the property of a case by adopting a similarity calculation method based on description logic, and giving out a preliminary result of similarity matching; building a relationship by using pheromone pheromone self-adaption updating and allocation strategy in an ant colony algorithm and case recommendation; giving out the case recommendation to learning subjects at different learning stages in the learning subjects' grades by partitioning the learning grades and problem grades of a learning subject; respectively endowing different weights to the computing results of the above methods, and comprehensively calculating the similarity among the cases; giving out a list of optimal solutions for the existing problem case in a case library through the comprehensive similarity for the selection of a learning subject. Therefore, the learner can learn from mistakes met by the learner quickly.

The invention provides a method for object recommendation. The method comprises the steps of obtaining a dataset of a plurality of users relative to a plurality of object likes, and obtaining a first object recommendation list corresponding to designated users according to a collaborative filtering method; through a case-based reasoning method and by combination with a preset case-based reasoning library, obtaining applicability of a subsolution of objects in the first object recommendation list relative to the designated users; generating a second object recommendation list according to the calculated applicability, and recommending objects to the designated users. Based on the collaborative filtering method, recommendation results are adjusted further through case recommendation, the shortcoming of collaborative filtering is overcome, and the method is favorable for improving object recommendation precision and user satisfaction by combination with user short-term preferences.

The invention discloses an oil and gas reservoir comprehensive assessment oriented spatial case-based reasoning method. The method comprises the following steps: (1) aiming to a position of a to-be-assessed area of the oil and gas reservoir, determining types of various drilled reservoirs, interpolating basic parameters of the drilling, and exporting the result; (2) partitioning the to-be-assessed area into grids and generating a grid pattern layer, wherein each of the grids is a case expression object; (3) extracting the basic parameter attribute, a spatial feature value and the reservoir type (a part of grids have) corresponding to each of the grids in the pattern layer; (4) respectively exporting all and provided grid information of the reservoir types in the grid pattern layer, and constituting a reservoir to-be-assessed case set and a case library; (5) setting weights, finding the case with the highest similarity from the case library according to the spatial similarity and/or attribute similarity of each case in the to-be-assessed case set, and attaching the reservoir type to the to-be-assessed case; (6) correlating all the obtained cases of the reservoir type to the grid pattern layer, coloring the grids according to the type, and at last obtaining a reservoir comprehensive assessment classification chart.