277results about How to "Find out quickly" patented technology

The invention discloses a power equipment infrared image fault positioning, identification and prediction method. The power equipment infrared image fault positioning, identification and prediction method comprises the following steps: 1) collecting power equipment infrared thermal image data; 2) classifying the infrared images to form a data set; 3) constructing a convolutional neural network model; 4) separating out faulty power equipment; 5) monitoring faulty power equipment in real time, and longitudinally collecting temperature data; 6) positioning a fault part, segmenting the infrared image of the power equipment, and extracting a fault area; 7) diagnosing a fault area, and judging a fault level; 8) predicting an equipment state trend; 9) uniformly outputting and displaying the information; 10) storing the fault level; 11) making four types of infrared image data sets; 12) building a target detection model and training; 13) directly detecting an infrared image of power equipmentto be detected through a target to obtain a fault position and a fault level; 14) repeating the step (5); 15) repeating the step (8); and 16) repeating the step (9) facilitating positioning of the fault position, fault level judgment and prediction of the fault equipment and giving a maintenance suggestion.

The invention discloses a multi-feature cyclic convolution significance target detection method based on an attention mechanism. The method comprises the following steps: ; the method comprises the following steps of: 1, analyzing common characteristics of a salient target in a natural image, including spatial distribution and contrast characteristics, using an improved U-Net full convolutional neural network, performing pixel-by-pixel prediction by adopting an encoder-decoder structure, and performing multi-level and multi-scale characteristic fusion between an encoder and a decoder by adopting a cross-layer connection mode; secondly, a large number of clutters can be introduced to interfere with the generation of a final prediction graph by carrying out concentage fusion on coding end features and decoding end features, so that an attention module is introduced to calibrate full-pixel weights from two angles between channels and between pixels, the task-related pixel weights are enhanced, and the background and noise influence is weakened; and 3, a multi-feature cyclic convolution module is used as a post-processing means, the spatial resolution capability is enhanced through iteration, the edge of an image region is further refined and segmented, and a finer significant target mask is obtained.

The invention relates to a method and a device for identifying association relation between network users. The method comprises the following steps of determining a user pair according to the identification attributive characters of users; judging whether the user behavior occurs between the users of the user pair for a data object with the specific attributive character; and when the user behavior occurs between the users of the user pair for the data object with the specific attributive character, utilizing parameters of the user behavior to compute a weight of the association between the users of the user pair in order to identify whether the users of the user pair have the association. According to the technical scheme of the invention, the association between the users can be identified from all users quickly and accurately.

A transformer station network intrusion detection and analysis method based on a character fingerprint is configured to detect and analyze the transformer station industrial control network intrusion. The method comprises the following steps: (1) establishing a transformer station network topology structure chart through analysis of the transformer station communication network; (2) establishing a character fingerprint bank layering model through the stratification analysis method; (3) performing extraction and analysis of the flow through adoption of the DPI and DFI technologies, extracting the character fingerprint and improving the character fingerprint bank; and (4) establishing a character fingerprint bank assessment system based on the established character fingerprint bank, and performing detection and analysis of the network character fingerprint. The transformer station network intrusion detection and analysis method based on the character fingerprint can perform comprehensive detection and analysis of the transformer station industrial control network intrusion and can rapidly determine the abnormal nodes.

The invention relates to a circuit for detecting the dead pixel of an LED display screen and a method thereof. The circuit comprises an LED display unit, a control circuit and a drive circuit, wherein the drive circuit comprises a line drive circuit and a column drive circuit; the LED display unit consists of M*N LED(s), wherein M is more than or equal to 1, N is more than or equal to 1, and M and N are integers; the drive circuit is internally provided with a sampling resistor and an A/D converter; and the two ends of the sampling resistor are connected with the input end of the A/D converter. The working principle of the circuit comprises the steps of detecting the voltage of the circuit by the A/D converter through the sampling resistor which is arranged in the drive circuit of the LED display screen to compute the electric current which pass through each LED, and comparing the computed electric current with the normal work electric current of the LED to judge that the LED is the dead pixel when some LED electric current value is deviated from the normal work electric current. Compared with the prior art, the invention is simple and reliable in circuit, can fast and exactly find out the dead pixel, is suitable for not only the production process of the LED display screen but also the remote on-line detection of the installed and used LED display screen, and greatly improves the detection efficiency.

The invention provides a retrieval method, a device and a monitoring system of monitoring video images, wherein, the method comprises the steps as follows: posture parameters of a cloud terrace when in shooting of one picture are added in coded data of the picture for each picture shot by a video camera on the cloud terrace; a map is established for recording the corresponding relationship between the posture parameters and the framing ranges of the shot pictures under the posture parameters; and the framing range is searched according to the map to cover the picture of the scene to be searched. The posture parameters of the cloud terrace are embedded in video bit streams of each picture, as the posture parameters can determine the framing range of the picture, the framing range of each picture is accordingly marked, and all the pictures which cover the scene to be retrieved can be retrieved through the retrieval of the posture parameters.

Disclosed is a method for analyzing a metal sample. The method comprises: an electrolysis step of electrolyzing a metal sample containing a reference element and an element of interest in an electrolysis solution; a collection step of collecting a portion of the electrolysis solution; an analysis step of analyzing the collected electrolysis solution; a concentration ratio calculation step of calculating the ratio of the concentration of the element of interest to the concentration of the reference element in the electrolysis solution based on the results of the analysis; and a content calculation step of calculating the content of the element of interest present in a solid solution state by multiplying the calculated concentration ratio by the content of the reference element in the metal sample.

The invention discloses a CNNLSTM fan fault prediction method based on an attention mechanism, and the method comprises the steps: firstly collecting the time sequence data of a fan through an SCADA system; secondly, a random forest algorithm is used for carrying out feature correlation degree analysis on the fan time series data, features with high fan fault correlation degree are extracted, a new feature data set is formed, the new feature data set is input into a CNNLSTM model to be trained, and an Attention mechanism is introduced into the model, so that the model pays more attention to the features related to fan faults; interference of non-related features is reduced, so that the accuracy and generalization ability of the fan fault prediction model are improved; and finally, inputting a to-be-predicted fan data set into the trained model to obtain a fault prediction result. The model provided by the invention is a fan fault prediction model which is suitable for processing time series data, high in accuracy, strong in generalization ability and outstanding in comprehensive performance.

The invention relates to a learning device that can record and provide a learning process and a method. The learning device includes a database, a controller, a display unit and an input unit; and the database, the display unit and the input unit are all connected with the controller. By the use of the learning device that can record and provide the learning process and the method, a time schedule can be preset according to individual requirement and ability, a problem that is answered wrong can be shown; and answering process record ,recordable analysis and learning process can be provided.

The invention provides a quality control chart pattern recognition method based on improved genetic algorithm optimization. The quality control chart pattern recognition method comprises the followingsteps: simulating various pattern characteristics of a control chart by using a Monte Carlo method; generating data of a corresponding mode through the parameter values; adopting the PCA principal component analysis method to carry out dimension reduction and denoising on the original data, main features of the data are extracted, shortening the training time of the model and improvintg the recognition accuracy; establishing a probabilistic neural network model, and carrying out pattern classification recognition by utilizing the characteristics of simple structure and convenient training; optimizing a main parameter smoothing factor of the probabilistic neural network by virtue of an improved single-objective optimization genetic algorithm; searching possible abnormal reasons from different aspects according to the identification result.The method solves the problems that all abnormal conditions cannot be monitored and recognized when an existing enterprise carries out quality control, effective abnormal information is difficult to find from a control chart, and appropriate measures cannot be taken to correct the abnormal conditions in the production process.

The invention discloses a method for fast finding out switches of function menus needing to be operated. The method comprises dividing a seekbar into a plurality of areas, judging whether touching operations exist in any area, displaying pages corresponding to the area with the touching operations according to judging results so as to fast find out the switches of the function menus needing to be operated. Compared with the prior art, the method for fast finding out the switches of the function menus needing to be operated firstly divides the seekbar into the plurality of areas, then judges whether the touching operations exist in any area, and when the judging results show that the touching operations exist in any area, the pages corresponding to the area with the touching operations are displayed, wherein the pages display a plurality of switches of the function menus. Therefore, the purpose of fast finding out the switches of the function menus needing to be operated is achieved, and finding out the switches of the function menus needing to be operated does not need to be carried out by sliding left and right after a notification bar is fully pulled down. The invention further discloses a terminal for fast finding out switches of function menus needing to be operated.

The invention provides a music recommendation algorithm based on content and user history, belonging to multimedia analysis technology field. The recommendation algorithm comprises: using a piece of music which is appointed by the user as interested music as input of the recommendation algorithm, calculating recommendation probability u (i, j) of other music relative to the user input by utilizing a recommendation algorithm based on cooperation to analysis user history, wherein the user history is music appreciated by the user in the past; calculating similarity s (i, j) between each piece of music and the user input music by utilizing a spatial distance relation of characteristics according to three music characteristics; calculating importance g (i, j) of other pieces of music relative to the user input music by utilizing characteristic vector centrality in a graph based analysis method to analysis music network;determining weight relationship among the recommendation algorithm based on cooperation, similarity analysis algorithm and analysis algorithm based on characteristic vector centrality; and calculating final recommendation probability of each piece of music by fusing the three algorithms. The music recommendation algorithm provided in the invention saves time and energy of users and solves appreciation preference problem of users.

The invention discloses a probe-based rotating shaft rotation center line geometric error calibration method, and belongs to the technical field of measurement error compensation. The probe-based rotating shaft rotation center line geometric error calibration method comprises the following steps of S1, compensating the measurement accuracy of a linear shaft, S2, establishing a rotation center lineerror compensation model and S3, geometric error calibration of a rotation center line. The probe-based rotating shaft rotation center line geometric error calibration method is simple in measuring flow, is helpful for measuring machine development engineers and end users to quickly find out errors of the rotation center line, and the measuring accuracy is improved.

The invention discloses a management system for biological samples. The system comprises a database, a management server connected with the database and a plurality of management terminals, bar code printers, data collectors and sample tubes connected with the management server through a network. A sample tag of each miniature centrifuge tube storing a biological sample is generated by the bar code printer, and a two-dimensional bar code of each miniature centrifuge tube storing a biological sample is generated according to a storage position of each sample tube in a refrigerator. The sample tags and the two-dimensional bar codes on the sample tubes are read by the data collectors, and corresponding biological sample information of the sample tubes is collected. The management server is registered by the management terminal, and recorded data for each sample tube is established by the management server according to preset data structure and put-in-storage condition of the sample tube. Management of the biological samples is standardized, operation processes of put-in-storage and put-out-storage of the biological samples are simplified, and accordingly conditions for realizing establishment, use and maintenance of a biological sample library are provided.

The invention discloses a method for panoramic inspection on a substation. The method comprises the following steps: selecting image acquisition points according to specific conditions of the substation; acquiring image information by an inspector holding inspection equipment; performing fault judgement on a panoramic image by the inspection equipment according to preset fault criteria during the process of inspection performed by the inspector holding the inspection equipment; and finally, performing key survey and maintenance aiming at a fault point. The inspection equipment comprises an inspection device, a location device and a signal processor. The method can be used for acquiring panoramic information of the substation once via image splicing technology, finding the fault point in the substation quickly and accurately, reducing omission in inspection, and improving the work efficiency of the inspector to a great extent.

The invention discloses a method for locating and inquiring a biological sample. The method comprises the steps of creating a biological sample management system, wherein the system comprises a database, a management server which is connected with the database, and a plurality of management terminals, bar code printer, data sampling devices and sample tubes, which are connected with the management server through a network; finding out the recording data corresponding to the sample tubes to be delivered in the database, and finding a storage position of each sample tube; finding out the sample tubes in a storage box corresponding to the storage position to be delivered out of a warehouse; and deleting the recording data of the delivered sample tubes from the database, and updating the database. Due to the adoption of the method, the management of the biological samples can be regulated, the warehousing and delivery operation program of the biological samples can be simplified, and the realization condition is provided for creating, utilizing and maintaining a biological sample database.

The invention provides a web service data modeling and searching method based on SDD (service data description), which comprises the steps of I, establishing a web service data model based on the SDD, wherein the model is divided into a parse layer, a controller layer and an expression layer; and II, providing the web service data searching method described in the specification based on the model.

The invention discloses a five-shaft cradle type numerical control machine tool non-deformation cutting three-dimensional geometrical modeling method, for solving the technical problem of poor modeling accuracy by use of a conventional method. The technical scheme is that first of all, a movement amount of each movement shaft of a five-shaft machine tool in a processing process at any instantaneous time by use of a linear interpolation method; then, performing modeling by use of a two-dimensional contour in which a group of parallel planes are intersected with a cutter and a workpiece, and through combination with a kinematic chain of the machine tool, reducing a space three-dimensional problem to a two-dimensional plane problem; establishing a mathematical expression of an instantaneous cutting edge of the cutter on a selection layer of the workpiece in a work coordinate system; solving an envelope boundary curve and an internal envelope area of the instantaneous cutting edge on each layer of a part at any time; solving a non-deformation cutting shape on the selection layer at the moment by subtracting an envelope area at the moment by use of an envelope area at last moment; and obtaining a three-dimensional non-deformation cutting geometry of the period by stacking all non-deformation cutting shapes generated on all layers of the workpiece. The modeling precision is high.

The invention discloses a rolling bearing reliability prediction method based on mathematical morphology and IFOA-SVR, and relates to the technical field of rolling bearing reliability prediction. The method aims at guaranteeing the prediction precision and prolonging a prediction step at the same time. The method comprises the steps: firstly extracting an envelope signal of a vibration signal, calculating a mathematical morphology fractal dimension of the envelope signal, and enabling the mathematical morphology fractal dimension to serve as the performance degeneration state characteristics of the rolling bearing; secondly carrying out the optimizing of the parameters C, g and epsilon in SVR at the same time through IFOA and building a prediction model, and, meanwhile, building a Weibull proportion fault rate model through employing MLE (Maximum Likelihood Estimation) and combining with IFOA, thereby obtaining a reliability model; finally enabling the performance degeneration state characteristics to serve as the input of an IFOA-SVR prediction model, obtaining a characteristic prediction result through employing a long-time iteration prediction method, enabling the result to be embedded into the reliability mode, and predicting the reliability of the operation state of the rolling bearing. An experiment indicates that the method prolongs the prediction step while guaranteeing the prediction precision.

The invention relates to a waterproof electronic device with a test structure, a water leakage tester and a water leakage testing method. The waterproof electronic device with the test structure mainly comprises a test hole, a sealing structure and a fixing structure, wherein the test hole is formed on a shell of the waterproof electronic device, and is used for introducing a test fluid to perform a water leakage test; the sealing structure is arranged on the shell or the water leakage tester, and can be used for accommodating a sealing element to form seal between the waterproof electronic device and the water leakage tester in the water leakage test; and in the water leakage test, the fixing structure can be used for fixing the water leakage tester to the waterproof electronic device. The waterproof electronic device with the test structure, the water leakage tester and the water leakage testing method are applicable to the water leakage test at various stages such as a development stage, a production process, after-sale stage and the like, can effectively, rapidly and economically find water leakage positions to clarify reasons for water leakage and timely take remedial measures, and are practical.

The invention provides a 10kV line change relationship evaluation method based on a grey correlation analysis method. The method comprises steps that S1, original data of same period line loss evaluation of the transmission line is collected; S2, the collected original data is pre-processed; S3, the correlation degree is acquired through a grey correlation analysis method; S4, transformers with low correlation degrees are selected after sorting according to the correlation degrees; and S5, a Granger causality test method is utilized to test whether the transformers with the low correlation degrees are causes for line loss power generation, and the transformers with the abnormal line change relationship are determined based on the test result. The method is advantaged in that the human andfinancial resource consumption is reduced, the line change relationship of a power system is effectively and quickly sorted, the number of lines with disqualified line loss is quickly reduced, the causes of abnormal line loss are ascertained, the effect of line loss treatment is effectively improved, and the improvement bottleneck of the same period line loss evaluation index in the prior art is broken through.

The invention relates to a robot zero point calibration method. The method comprises the steps that firstly, a first shaft is controlled to move in one direction with the preset speed, and the read number of a dial indicator arranged on the first shaft is obtained; secondly, when the read number of the dial indicator becomes small from large and then becomes large from small, the first shaft is controlled to stop moving, wherein one-time stroke of the first shaft is from movement starting to movement stopping; thirdly, the first shaft is controlled to reversely rotate and move in the opposite direction, and the difference value between the current read number and the previous read number of the dial indicator is calculated; fourthly, when the absolute value of the difference value is smaller than or equal to the preset deviation threshold value, the fifth step is executed, and when the absolute value of the difference value is larger than the preset deviation threshold value and the current read number of the dial indicator is larger than the previous read number, the first shaft is controlled to stop moving, and the third step is executed; and fifthly, the first shaft is controlled to stop moving, the current value of an encoder of a drive motor of the first shaft is recorded, and the current position of the first shaft is recorded as the mechanical zero point of the first shaft. By means of the robot zero point calibration method, the corresponding relation between the robot motor encoder value and zero point calibration shaft position is achieved.

The invention relates to a communication fault detecting device and method in an air conditioning system which has an outdoor unit connected with a plurality of indoor units, aiming at making use of the wired/wireless controller to cut off the communication of the indoor units even when the communication lines connecting the outdoor unit and the indoor units are not separated, so that the indoor unit with communication fault can be more quickly and easily found. To achieve the aim, the communication fault detecting method provided by the invention in the air conditioning system which has an outdoor unit connected with a plurality of indoor units includes: whether a communication fault happens to the air conditioning system is judged; when the communication fault occurs, selectively separate the indoor units through the communication lines and then find out the indoor unit with communication fault.

The invention provides a method for knowledge points diagnosis based on influence factors and neural networks. By means of the method for knowledge points diagnosis based on influence factors and neural networks, the network parameters of the convergent neural network model are preserved through the refined influence factors and the designed multi-layer neural network model after training offline. During the knowledge points diagnosis process, the influence factors related to the knowledge points to be tested of the user to be tested can be input into the trained neural network model to directly know the grasping situation of the knowledge of the user by means of the calculation of the algorithm, therefore, the knowledge points diagnosis of the user can be performed quickly. Compared with the original way of doing exercise, the method for knowledge points diagnosis based on influence factors and neural networks has the following advantages: the duration of the diagnosis is greatly shortened, the efficiency is improved, and the user experience is effectively enhanced. The method for knowledge points diagnosis based on influence factors and neural networks has broad market prospect.