145results about How to "Realize accurate identification" patented technology

The invention provides a semiconductor chip defect automatic detection method and system based on hyperspectral imaging, and relates to the technical field of chip detection, and the method comprises the steps: collecting a chip hyperspectral image, carrying out the image enhancement and illumination non-uniformity correction through an improved neuromorphic adaptive processing network and a dual-path diffusion model, and carrying out the detection of the hyperspectral image. Extracting multi-level features by using a dynamic mixed feature enhancement network based on neural architecture search, fusing the features through a cross-layer attention mechanism, generating an initial defect detection result by using a multi-branch expert decision-making module and an uncertainty dynamic integration strategy, constructing a space-time knowledge inference graph, and determining a defect detection result; and performing reliability evaluation and optimization on the initial defect detection result in combination with the form similarity, the spatial position relationship and the process parameter influence model to obtain a final defect detection result.

The invention relates to the technical field of geological disaster monitoring, in particular to a landslide geological disaster monitoring and early warning system based on multi-means fusion, which comprises a data acquisition module, a data fusion module, a landslide prediction module, a landslide disaster intelligent partition and response module and an early warning transmitting module, the data acquisition module acquires landslide related data in real time; the data fusion module is used for fusing landslide related data from different sources; the landslide prediction module is used for predicting the occurrence probability of the landslide; the landslide disaster intelligent partition and response module combines landslide related data and geologic structures monitored in real time, dynamically adjusts post-disaster response measures, and generates early warning information; and the early warning signal sending module automatically sends an early warning signal based on the generated early warning information. According to the invention, potential risks can be found in time in the early stage of landslide occurrence, and the accuracy and timeliness of disaster early warning are greatly improved.

The invention discloses a weld joint key point recognition method, device and equipment, a medium and a program product, and the method comprises the steps: generating a data set based on training image data and annotation data, constructing an original recognition model, improving the original recognition model, and training and optimizing the improved original recognition model based on the data set. Obtaining a target recognition model; acquiring a depth image and an RGB image of the welding part through image acquisition equipment on the welding robot; the RGB image is input into the target recognition model for weld joint key point recognition, weld joint key point coordinates of the welding part are determined based on the recognition result and the depth image, accurate recognition of the weld joint key points is achieved, a welding robot is guided to accurately reach the weld joint key point coordinate positions for welding, key point recognition autonomy and adaptability are improved, and the welding quality is improved. And welding tasks with different requirements are effectively adapted, the welding efficiency is greatly improved while the welding quality is ensured, and the time cost is reduced.

The invention relates to the technical field of intelligent control of transformer substations, in particular to a standardized intelligent control system of a transformer substation, which realizes real-time response to the running state of equipment by acquiring the current, voltage, power factor, temperature and humidity, operation state, insulation state and environment temperature and humidity of a power transformation unit in real time. A gradient boosting decision tree is combined with a variational auto-encoder and a self-attention mechanism, state vector features are extracted, complex abnormal states are accurately recognized, potential faults are detected in an early stage, and the monitoring efficiency and the early warning precision are greatly improved. The future power demand is accurately predicted by combining the long-short-term memory network model with historical and real-time load data, and the limitation of traditional static load management is overcome. Based on the predicted power demand, the reinforcement learning model is adopted to dynamically adjust the operation number of the power transformation units, optimal configuration of resources is achieved while the demand is met, and the overall operation efficiency of the transformer substation is remarkably improved.

The invention discloses a three-dimensional scene re-coloring optimization method based on Gaussian splashing and deep learning, and the method comprises the following steps: S1, constructing a multi-view data set of a given scene, and generating an initial point cloud; s2, based on the initial point cloud, extracting a representative color set from all input pixels, generating a palette and taking the palette as initial setting; s3, rendering a multi-view result picture by using the palette and a Gaussian splash algorithm, calculating a loss function, and generating an optimized Gaussian radiation field through back propagation optimization parameters; and S4, re-rendering the scene and generating a re-coloring result based on the optimized Gaussian radiation field and the palette color modified by the user. According to the method, the Gaussian splash algorithm and the deep learning technology are combined, efficient re-coloring optimization of the three-dimensional scene is achieved, and the method has the advantages of being visual in editing, accurate in illumination expression and real in rendering.

The invention discloses a mine surface feature accurate identification method based on a multi-source remote sensing data fusion technology, and relates to the technical field of data identification, and the method comprises the following implementation steps: configuring the IP address information of a remote control area server of a mine surface feature; entering a data acquisition end, and collecting time sequence multi-source remote sensing data from different sensors and platforms in real time; entering a data fusion end, and eliminating the influence of sensor errors on data quality in real time; and 4, entering a comprehensive identification end, extracting different ground feature features in real time, and verifying and optimizing a result in real time after comprehensive identification. According to the mine ground feature accurate identification method based on the multi-source remote sensing data fusion technology, remote sensing data early warning signals are sent in time, the accuracy of mine ground feature information acquisition is guaranteed, disaster early warning signals are sent to mine ground features, the safety and reliability of mine ground feature identification are guaranteed, accurate identification of strip mine mountain features is achieved, and the accuracy of mine ground feature identification is improved. And powerful support is provided for mine management and decision making.

The invention discloses an electromagnetic bearing fault mode analysis method for fault diagnosis of a primary helium fan. The method comprises the following steps: establishing an electromagnetic bearing-rotor-rack high-fidelity coupling dynamic model; extracting fault feature frequency and vibration amplitude, preprocessing the monitoring signal, and extracting multi-source heterogeneous fault features; evaluating the sensitivity of each feature to a fault, designing an adaptive weighted D-S evidence theory fusion algorithm, and identifying a bearing fault mode by dynamically adjusting the weight of an information source; building a convolutional neural network model to automatically extract deep fault features corresponding to the working conditions, and performing diagnosis model adaptation among different working conditions by adopting a transfer learning strategy; the GRU network is used for training and learning time sequence monitoring data, the development trend of bearing faults is predicted, and a heuristic algorithm is used for optimizing a fan operation and maintenance strategy. According to the method, the limitation of the existing method can be overcome, accurate diagnosis and trend prediction of the bearing fault are realized, and key technical support is provided for intelligent operation and maintenance of the rotating machinery.

The invention relates to the technical field of medical behavior data monitoring, in particular to a medical insurance common outpatient service supervision system and method based on a fraud abnormal portrait technology, and the system comprises a medical transaction data collection module, a medical behavior analysis module, a risk assessment feedback module and a medical transaction database. According to the medical insurance ordinary outpatient service supervision system and method based on the fraud anomaly portrait technology, a more comprehensive risk assessment system is provided, and by means of an intelligent means, the medical object behavior anomaly risk, the medical institution behavior anomaly risk and the number of abnormal behavior overlapping cycles are comprehensively analyzed, so that the risk assessment efficiency is improved. According to the invention, the method achieves the precise recognition, early warning and prevention of fraud abnormal behaviors in a common medical insurance outpatient service, facilitates the improvement of the use efficiency of medical insurance funds, guarantees the fairness and sustainability of a medical insurance system, protects the benefits of a patient, maintains the fairness and efficiency of a medical system, and also facilitates the improvement of the management level and service quality of a medical institution.

The invention provides a data cleaning method and device based on time sequence similarity, a medium and equipment. The method comprises the steps that firstly, a to-be-cleaned time sequence data set and multiple historical time sequence curves are acquired; discretizing the to-be-cleaned time sequence data based on a symbol clustering method to obtain a character sequence data set; performing similarity measurement on the character sequence data set and a historical character sequence data set corresponding to the historical time sequence curve by using an Euclidean distance algorithm, and determining a target time sequence curve; and finally, comparing the target time sequence curve with to-be-cleaned time sequence data in the to-be-cleaned time sequence data set, and determining abnormal data points. And correcting the time sequence data corresponding to the abnormal data points based on the target time sequence curve to obtain a target time sequence data set. According to the invention, through dimension reduction, similarity measurement optimization and accurate identification of abnormal data, missing data is reasonably filled up, and the accuracy and efficiency of power load data cleaning are improved.

The invention relates to a firmware identification method of IoT (Internet of Things) equipment, in particular to an online IoT equipment firmware identification method based on representation learning, which comprises the following steps: firstly simulating firmware of target IoT equipment to obtain the running state of the target IoT equipment, and then collecting an embedded webpage as analysis input through a GET request of HTTP (Hyper Text Transport Protocol); a fusion feature is generated through a deep feature fusion extraction module, and a hard mining cosine triple center loss mechanism is introduced to optimize the fusion feature; in the model training stage, a full-connection neural network is used as a classifier, and features are mapped to corresponding labels; after training is completed, feature representation learned by the framework is analyzed through a clustering algorithm, and similar samples are recognized and clustered; and according to a clustering result, selecting a representative page to query the real Internet of Things equipment, and determining a firmware version through a voting mechanism. The method provided by the invention is obviously superior to the most advanced scheme in the aspect of firmware identification, and the identification accuracy has extremely high superiority.

The invention discloses a passenger train axle box cover bolt fault detection method based on an attention-enhanced double-path network, and belongs to the technical field of passenger train fault detection. The method aims at solving the problem that axle box cover bolts are not easy to accurately recognize in a dense and complex background environment. Comprising the steps of constructing a dual-path neural network model, and obtaining axle box cover macroscopic geometric features by adopting a global feature path; using a local detail feature path to obtain an enhanced image microscopic feature; performing attention fusion by adopting a feature fusion module to obtain a fused feature image, and obtaining an axle box cover type, axle box cover direction and bolt state preliminary identification result; and in combination with the predefined mask matrix, transforming the predefined mask matrix to a to-be-identified image axle box cover space by adopting a spatial attitude matching algorithm to carry out spatial matching alignment, and determining a final state of the bolt through intersection operation of the predefined mask matrix and a bolt state preliminary identification result label output by the trained dual-path neural network model. The method is used for bolt fault detection.

The invention relates to a rotating target identification method, belongs to the technical field of ray image target detection, and solves the problems of high resource consumption and low accuracy during rotating target prediction in the prior art. The identification method comprises the following steps: preprocessing a to-be-identified image to obtain a to-be-detected image; inputting the to-be-detected image into a pre-trained rotating image detection model to obtain a prediction category and prediction rotating bounding box information of each target in the to-be-detected image; and calculating four corner point coordinates of each target according to the predicted rotation bounding box information of each target, and connecting the four corner point coordinates of each target as a predicted rotation bounding box of each target. The method achieves the quick prediction of the rotating target, and improves the recognition precision of the rotating image.

The invention relates to the technical field of image recognition, and particularly provides a road target monitoring method, system and device and a storage medium, and the method comprises the steps: obtaining a road monitoring video stream, converting the road monitoring video stream into an image frame sequence, and carrying out the preprocessing of image frames; identifying a road target in each image frame by using a target detection algorithm, and generating a track of the road target in the image frame sequence by using a multi-target tracking algorithm; performing anomaly analysis on the trajectory based on a preset rule, and identifying an abnormal trajectory; and obtaining a confidence score of an abnormal road target to which the abnormal track belongs, screening out a target detection frame with the highest score, and extracting feature information of the road target from the target detection frame. According to the invention, the speed and precision of road target identification in a complex environment are improved, an abnormal analysis and subsequent processing method of the moving track is provided, the quality of road target monitoring is improved, and a data basis is provided for road management.

The invention relates to a robot standing posture recovery method and device, equipment, a storage medium and a product, and the method comprises the following steps: after the falling state of a robot is detected, collecting joint angles in real time to obtain falling posture data, and calculating contact points with the ground to determine a support part set; in combination with a preset zero moment point reference trajectory, performing inverse dynamics solution based on the set to generate a joint motion sequence, and configuring the joint motion sequence as joint position instructions to form a getting-up planning trajectory; the current output torque of each joint is compensated in real time according to the track, a driving torque instruction is obtained, each joint motor is driven to execute actions, so that the robot is recovered from the current posture to the standing posture, and the problem that the preset rigid action is often not matched with the current actual supporting state when the robot falls to the ground, and consequently the robot cannot stand up when executing the getting-up action is solved. And center-of-gravity unbalance is easy to occur.

The invention discloses a small sample action recognition method, electronic equipment and a medium. The method comprises the following steps: acquiring a support set video, a query set video and a category label template; performing feature processing on the support set video, the query set video and the category label template through a preset multi-modal training model to obtain a first visual feature vector, a category-level semantic feature and a second visual feature vector; inputting the three data into a proxy semantic diffusion module for noise diffusion processing; performing feature representation on the first instance-level proxy semantics, the class-level semantic features and the first visual feature vector output by the proxy semantic diffusion module to construct a class prototype, and performing feature fusion on the second instance-level proxy semantics to obtain query feature representation; and performing action recognition on the query feature representation and the category prototype to determine an action type of a second action video frame in the query set video. According to the embodiment of the invention, semantic details of an instance level can be captured, and the action recognition precision is improved.

The invention relates to the technical field of image communication, in particular to an intelligent camera with active defense and a defense method thereof, and the intelligent camera comprises a data acquisition module, an extraction module, a judgment module, a determination module, a screening module and a defense module. According to the invention, through fusion of a multi-parameter analysis technology and combination of the target position, the attitude angle and the height of a real-time image and dynamic changes of sound intensity, accurate identification of an intrusion target is realized, and the judgment module adopts a multi-level logic of combination of boundary judgment and attitude angle change, so that the accuracy of abnormal target judgment is effectively improved; the screening module reduces erroneous judgment through comprehensive screening of sound intensity and position; the defense module calculates a launching track based on the real-time position and height of the target, fixed-point active defense is achieved through a water bomb, passivity of a traditional voice alarm mode is avoided, and the problem that the response speed is not timely due to single fingerprint recognition and vibration monitoring data is effectively solved.

The invention relates to photovoltaic fault detection, in particular to a photovoltaic fault intelligent diagnosis method and system based on IV curve data, and the method comprises the steps: obtaining an IV curve of a photovoltaic array at any temperature and irradiance in advance, and obtaining an IV curve model of the photovoltaic array; whether the working state of the photovoltaic array is abnormal or not is judged according to the IV curve model, and working data of the photovoltaic array are collected when the photovoltaic array is in the abnormal working state; inputting the working data of the photovoltaic array into the fault type identification model to obtain the fault type of the photovoltaic array; obtaining an effective voltage time-domain signal of the photovoltaic array according to the voltage time-domain signal of the photovoltaic array, and obtaining an effective current time-domain signal of the photovoltaic array according to the voltage data and the current data of the photovoltaic array; according to the technical scheme provided by the invention, the defect that the fault type and the fault position of the photovoltaic array are difficult to accurately identify by using an IV curve in the prior art can be effectively overcome.

The invention provides an image sensitive element examination system and method, computer equipment and a medium, and belongs to the technical field of image sensitive element examination. The system comprises an image preprocessing module, a preliminary judgment module, a triple extraction module, a multi-head expansion module and a screening and denoising module. The image preprocessing module carries out preprocessing operation on the image to obtain a to-be-detected image. The preliminary judgment module is used for preliminarily judging the relevancy between the to-be-detected image and the sensitive word by using a multi-modal large language model; the triple extraction module is used for extracting triple information in an image to be detected, and the multi-head expansion module is used for performing multi-head expansion on each single word in a triple and searching related sensitive words in combination with a sensitive word list. And the screening and denoising module is used for screening and denoising the sensitive words and judging whether the sensitive words really appear in the to-be-detected image or not, so that the security of the to-be-detected image is judged. According to the invention, the accuracy of image sensitive element examination is improved.

The invention discloses a picture resource identification method and a related device, and relates to the technical field of picture identification, and the method comprises the following steps: responding to a picture resource identification trigger instruction, and identifying a picture resource from a resource file name under a directory structure of an iOS project; screening out all first picture resource file names meeting a preset picture resource file extension name requirement to obtain a first array, determining second picture resource file names having a reference relationship with the code files from the code files under the directory structure of the iOS project to obtain a second array, and comparing the first array with the second array to obtain a first picture resource file name and a second picture resource file name; and searching the target picture resource file names which exist in the first array and do not exist in the second array, and determining the picture resources corresponding to all the target picture resource file names as the identified useless picture resources. According to the method and the device, the useless picture resources in the iOS project are automatically identified, the consumed time is short, the efficiency is high, and the useless picture resources can be accurately identified.

The invention relates to the technical field of industrial data processing, in particular to an intelligent safety accident early warning method and system based on risk assessment, and the method comprises the steps: obtaining a mining operation event, extracting response situations before and after the operation event, checking the response situations with a reference response template, and judging a disturbance response according to the deviation degree; a ventilation network model is constructed, and when it is detected that a monitoring point is abnormal, management traceability is carried out along a traversal upstream path in combination with an air volume distribution proportion; predicting the propagation trajectory of the abnormal gas along the wind flow path and the time of reaching a downstream node based on the ventilation network model, and obtaining spatial correlation analysis; analyzing the disturbance response and the spatial correlation, calculating a coupling risk increment, and outputting a comprehensive risk value; judging the critical degree grade of the comprehensive risk value time series data; and determining a scheduling level according to the combination of the comprehensive risk value level and the critical degree level, when the critical index indicates to evolve to a dangerous state, improving the scheduling level in advance, and generating a differentiated resource scheduling suggestion in combination with the propagation trajectory.

The invention discloses a millimeter wave radar and monocular camera fused information acquisition device and method, and relates to the technical field of target detection. The device comprises a millimeter wave radar, a monocular camera and an adjusting bracket, wherein the adjusting bracket can adjust the positions and angles of the radar and the camera. And the data fusion module performs time synchronization and space alignment on the radar information and the image information, and obtains a target recognition result through a preset target recognition model. The acquisition method comprises the steps of timestamp matching, time compensation, joint calibration, coordinate transformation, image distortion correction and the like. Further, radar physical features are extracted by using a CFAR algorithm, image features are extracted by using a deep learning model, and effective features are obtained through PCA dimension reduction. And finally, an accurate target recognition result is obtained through feature splicing and optimization by using a support vector machine (SVM). According to the method, efficient fusion and target identification of millimeter wave radar and monocular camera information are realized, and the method has a wide application prospect.

The invention discloses an electric power standard structuring method based on an image recognition technology, and the method comprises the steps: obtaining all characters of an electric power standard file through the OCR character extraction of an electric power standard PDF file; then, through an OCR (Optical Character Recognition) self-healing mechanism, the accuracy of identifying all characters of the power standard file by the model is improved, all accurate characters of the power standard file are obtained, then power standard meta-information is extracted, and then power standard text content entries are carried out; by deeply researching the storage form and characteristics of the electric power standard file and combining the technologies of image recognition, natural language processing and the like, accurate recognition, content extraction and structured storage of the electric power standard file are realized, and powerful support is provided for informatization construction and efficient operation of the electric power industry.

The invention discloses a family relationship identification method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, and the main technical scheme comprises the following steps: firstly, obtaining an insurance policy record of a target user, and determining a relative relationship between related personnel and the target user in the insurance policy record; secondly, building a family relationship according to the relative relationship between the target user and the related personnel; wherein the family relationship comprises a third-generation relative relationship; and finally, when it is determined that the members in the family relationship belong to two or more family relationships at the same time, determining a target family relationship to which the members belong according to the occurrence frequency of the members in the family relationships. Compared with the prior art, the embodiment of the invention detects and analyzes the composition and change of the family members in real time through the insurance policy data, and achieves the precise recognition of the family members through the family affiliation analysis of the plurality of family members.

The invention discloses a method for constructing a digital twin system for manufacturing a spaceflight thermal control coating film through magnetron sputtering coating. The method comprises the following steps of: firstly, constructing a machine tool three-dimensional model by utilizing Unity 3D and a three-dimensional reconstruction technology; a digital twinborn model is adopted to carry out mapping simulation on equipment operation actions and operation data acquired in real time, and visualization of the manufacturing process is achieved; according to the production requirements of magnetron sputtering coating equipment, an instruction prediction model is established for a specified coating product or process through a digital twin system, and online quality prediction of a real-time production process is realized; by establishing a channel-space attention mechanism, a mapping relation between coating defect strong correlation characteristics and defect categories is established, and magnetron sputtering coating system state early warning based on fusion of multi-source acquisition information is realized.

The invention relates to the technical field of video processing, and discloses a livestock three-dimensional behavior detection method, which comprises the following steps: S1, establishing a livestock three-dimensional behavior detection system for shooting livestock behavior videos; s2, constructing a livestock three-dimensional behavior real-time detection model, namely an RT-DETRv3 model; and S3, according to a livestock shot video acquired by the behavior detection system, outputting different behaviors of the livestock by using the real-time detection model for classification and analysis, and generating a visual livestock behavior statistical report. According to the invention, through combination of the multi-view camera system and the deep learning algorithm, especially integration of the 3D convolutional neural network and the RT-DETRv3Transformer, three-dimensional behaviors of livestock can be captured in real time. Compared with the prior art which only depends on two-dimensional monitoring of a single visual angle, the method can achieve the precise recognition of the complex three-dimensional postures of standing, lying, walking and the like of the livestock, and greatly improves the detection accuracy and precision. In addition, attitude key point detection and a multi-target supervision mechanism are used.

The present invention discloses a pressure sensor appearance defect visual detection system, which relates to the field of defect visual detection technology, and includes a light source control module, an image acquisition module, an image preprocessing module, a feature extraction module, a defect recognition module, an evaluation and grading module, a result display module, and a human-computer interaction module integrated on an appearance defect AOI device, wherein electrical signals are connected between the modules. The pressure sensor appearance defect visual detection system can accurately identify tiny defects on the surface of the product through high-precision industrial cameras and advanced image processing technology. Compared with traditional manual detection methods, it has higher detection accuracy and can detect tiny scratches and dents that are difficult for the human eye to detect. At the same time, the automated detection process greatly shortens the detection time and improves production efficiency. The system can detect a large number of products in a short time.

The invention discloses a real-time intelligent disaster early warning method based on multi-modal data, and belongs to the technical field of disaster early warning, and the method comprises the steps: 1, constructing a multi-modal data platform; 2, obtaining a first training model by using a graph neural network technology, and obtaining a second training model in combination with a federated learning technology; 3, integrating the first training model and the second training model to obtain a disaster propagation model; 4, deploying a response function on edge equipment close to the data source; and 5, continuously collecting data of actual disaster occurrence, comparing the data with a model prediction result, and carrying out early warning response. According to the invention, by integrating disaster data obtained by a plurality of data sources, more data sources can be obtained, the comprehensiveness and accuracy of early warning can be improved, and the generalization ability of the model can be significantly improved by constructing the disaster recognition model by using the graph neural network and federated learning, so that the system can cope with various complex and changeable disaster scenes.

The invention provides an environmental protection equipment energy consumption optimization and energy-saving regulation and control method based on multi-source data fusion, and relates to the technical field of environmental protection equipment energy-saving control, and the method comprises the steps: collecting the operation data of environmental protection equipment, carrying out the multi-scale feature extraction, recognizing the energy consumption abnormity through combining the energy efficiency curve reconstruction, and carrying out the energy-saving control of the environmental protection equipment; energy consumption sensitive parameters are extracted, energy-saving space is calculated based on load fluctuation, and an optimized parameter adjusting scheme and an optimized time sequence control sequence are generated by integrating the environment load change rate and the equipment thermal inertia coefficient. The energy consumption abnormity can be accurately identified, the energy-saving space is scientifically quantified, the intelligent optimization and dynamic regulation and control of the energy consumption of the environmental protection equipment are realized, and the operation efficiency of the equipment is obviously improved.

The invention discloses a day-ahead response capability evaluation method, a scheduling optimization method and equipment for an electric vehicle virtual power plant, and relates to the technical field of smart power grids. The method is based on a data driving thought, adopts an LSTM-GA combination method, captures a time dependency relationship in massive historical operation data of a virtual power plant, realizes accurate identification of non-fixed characteristic parameters of an EV monomer, and comprises the following steps: firstly, taking an EV group (namely an electric vehicle virtual power plant) as an evaluation object, and evaluating the day-ahead response capability of the electric vehicle virtual power plant; the accuracy of day-ahead response capability evaluation is improved, and then the accuracy of optimization scheduling is improved.

The present invention discloses a method for identifying vehicles based on signaling data, including: S1 obtaining terminal signaling data, and performing preprocessing to obtain user trajectory data; S2 identifying and obtaining the corresponding vehicle type based on the user trajectory data; S3 extracting conventional feature values ​​based on the user trajectory data and the corresponding vehicle type, and obtaining the distribution of each feature value; S4 establishing conventional feature rule strategies based on conventional feature values ​​and distribution; S5 establishing advanced feature rule strategies based on user trajectory data and distribution; S6 establishing expert system rules based on conventional feature rule strategies and advanced feature rule strategies; S7 verifying and adjusting the expert system rules to obtain the optimal expert system rules; S8 identifying the terminal signaling data to be tested based on the optimal expert system rules to obtain the vehicle type. The vehicle type to which the signaling data belongs is efficiently and accurately identified through the optimal expert system rules.