33results about How to "Improve exception handling efficiency" patented technology

The invention relates to the technical field of electrical digital data processing, in particular to an anomaly detection and processing method and system based on deep learning. The method comprises the following steps: firstly, performing feature extraction and fusion on a log text sequence, a log timestamp sequence and a running state index sequence set to obtain an initial feature vector sequence; then, constructing a hierarchical graph neural network to perform feature updating, propagation and fusion on the initial feature vector sequence to obtain a multi-level fusion feature vector sequence, and calculating a standardized mode vector sequence in combination with a log text sequence and a running state index sequence set; obtaining a self-adaptive abnormal threshold value according to the standardized mode vector sequence and detecting candidate abnormal logs; and finally, verifying the candidate abnormal logs, determining verified abnormal logs, classifying the verified abnormal logs, generating a processing suggestion text according to a classification result, and carrying out early warning prompt. The false detection rate can be reduced, and the log anomaly detection accuracy is improved.

The invention relates to an electric energy quality detection device abnormity early warning method and system and an intelligent terminal, and relates to the technical field of electric energy detection, and the method comprises the steps: obtaining image detection information; when the image detection information is consistent with the reference image information, acquiring an electric energy detection parameter; when the electric energy detection parameter is inconsistent with the reference electric energy parameter, calculating a difference between the electric energy detection parameter and the reference electric energy parameter and taking the difference as an electric energy deviation value; when the electric energy deviation value does not fall into the reference deviation range, determining a wire loosening position according to the electric energy detection parameter and the phase parameter; according to the wire loosening position, traction is carried out through a traction method, and a wire rebound value and a wire specification are obtained; determining a reference rebound value according to the specification of the electric wire; if the wire rebound value is not less than the reference rebound value, continuing to obtain the electric energy detection parameter; and when the wire rebound value is smaller than the reference rebound value, outputting alarm information. The method and the device have the effect of improving the exception handling efficiency of the electric energy quality detection device.

The invention belongs to the field of chip power consumption detection, relates to a data analysis technology, is used for solving the problem that optimization decision analysis cannot be carried out according to test data when the power consumption of a chip is abnormal in the prior art, and particularly relates to a chip power consumption evaluation method and system based on artificial intelligence and a storage medium. Comprising a static test module, a static analysis module, a dynamic test module, a dynamic analysis module and a database, the static test module is used for performing static test on the chip power consumption; the static analysis module is used for analyzing a static test result of the chip and marking a static test object i as a static normal object or a static abnormal object; according to the method, the basic power consumption performance of the chip in the static operation environment can be tested, then the static test result is subjected to data processing to obtain the static coefficient, then the static test object is subjected to differential marking according to the static coefficient, and data support is provided for the performance evaluation and analysis process.

The invention relates to the technical field of intelligent traffic, in particular to a vehicle guiding method and system for an unattended highway. The invention discloses a vehicle guiding method for an unattended highway. The method comprises the following steps: S1, acquiring road image information and vehicle information on the highway in real time; s2, performing road section division according to the road image information and a historical congestion area, and analyzing each branch road section by using a mobile calculation formula to obtain an abnormal road section; s3, analyzing the behaviors of vehicles in each abnormal road section to obtain the lane changing times of the expressway entrance; and S4, performing planning guidance on the buffer road section according to the road image information of the expressway entrance. According to the method, the vehicle behavior in the abnormal road section is analyzed, the switching frequency of the expressway entrance is obtained, the buffer road section is further planned and guided, and the vehicle flow in the buffer area can be efficiently guided to avoid congestion through the dynamic analysis of the vehicle behavior and the intelligent planning of the divided road sections.

The invention provides an exception handling method and device, equipment, a storage medium and a program product, and relates to the technical field of computers. The method comprises the steps of executing an assembly line task, and obtaining an execution result of the assembly line task; if the sub-execution result of any sub-task indicates that the sub-task is executed abnormally, a log of the sub-task is obtained, and whether a target log field matched with a first log field of the log exists in a knowledge base corresponding to the sub-task or not is determined; if the target log field exists, determining a first exception handling strategy corresponding to the target log field in the knowledge base; and executing the first exception handling strategy. According to the method, the exception handling efficiency is effectively improved.

The invention discloses an order information customs declaration method and system oriented to cross-border trade, and relates to the technical field of customs declaration. During operation of the system, order information is collected in an e-commerce platform logistics company and a payment system, related data of the quality, efficiency and compliance of an order are acquired, and cleaning, verification and formatting are performed; after calculation, obtaining a cross-border declaration index Kjsb, checking whether a commodity code and a tax number meet customs declaration requirements or not, automatically complementing missing information, automatically matching the tax number with the commodity code, converting the preprocessed and verified order data into a data format meeting the declaration requirements according to customs declaration specifications, and sending the data to a server; and submitting the generated customs declaration data to a customs system through an API or an EDI interface, obtaining feedback, comparing a cross-border declaration index Kjsb with a preset threshold M, obtaining an abnormality assessment scheme, processing an abnormal condition, and notifying related personnel to intervene or automatically declarate again.

The invention relates to the technical field of workshop workflow management. The workshop workflow management method comprises the steps that production information of a production workshop is obtained, a production material preparation list is confirmed, material distribution information is determined according to the production material preparation list, the material distribution information is matched with operation report information of an operator, verification error prompt information is obtained, and the verification error prompt information is sent to the computer equipment. The method comprises the steps of obtaining operation record information in the production process according to material information, confirming an abnormal reason causing verification matching inconsistency based on the operation record information and in combination with the material information, obtaining updated operation report information, matching the updated operation report information with material distribution information again, generating corrected operation report information, and sending the corrected operation report information to a server. And checking the corrected operation report information, and storing the corrected operation report information in a production flow database. The device has the effect of improving the working efficiency of a production workshop.

The application provides a multi-agent cooperative task state management and abnormal backtracking method and system, relates to the technical field of artificial intelligence, and comprises the following steps: obtaining interaction data, state data and execution logs, constructing an initial task knowledge graph based on an ontology model, establishing a bidirectional causal chain by using a Bayesian network to form a dynamic knowledge graph, analyzing state migration rules when an abnormality is detected, identifying a propagation node, constructing an abnormal propagation subgraph, generating an influence chain, determining a recovery priority and triggering state rollback. The method can quickly trace the abnormal source, effectively control abnormal diffusion, and improve system robustness and task recovery efficiency.

The invention provides a data recovery method, device and equipment based on a Bayesian rule and a medium, and relates to the technical field of data recovery, and the method comprises the steps: obtaining historical data of a target business in a historical time period; running the target business to obtain target running data; calculating an abnormal probability of the target operation data based on a Bayesian formula and historical data to obtain an abnormal probability value; when the exception probability value is greater than a first set threshold value, storing the target operation data into a target table, and determining a corresponding target exception type; and determining a corresponding exception repair method based on the target exception type, and performing repair to obtain target data. According to the invention, based on the Bayesian rule, the position of the exception can be quickly determined after the system fails to operate normally, and the processing method required by the exception can be automatically matched, so that the exception can be automatically processed and repaired, the problem of business exception can be solved in a relatively short time, and the exception processing efficiency is improved.

The invention provides an electroscope operation intelligent control system based on an intelligent sensing technology, and belongs to the field of electroscope control. Comprising a processor which is in communication connection with a maintenance analysis module, a fault monitoring module and a prediction analysis module. The maintenance analysis module is used for carrying out maintenance analysis on the electroscope; the fault monitoring module is used for monitoring the operation fault of the electroscope in real time, and marking the execution process corresponding to the electricity verification operation of the electroscope as a normal process or a fault process. And the prediction analysis module is used for performing prediction analysis on the fault probability of the normal process at the end moment of the normal process, and marking the corresponding normal process as a risk process when the normal process has a fault risk. According to the invention, the fault probability of the normal process can be predicted and analyzed at the end time of the normal process through the prediction and analysis module, so that the electroscope is actively maintained when the fault probability is abnormal, the fault frequency of the electroscope is reduced, and the actual operation risk of the electroscope is effectively suppressed.

The invention discloses a data acquisition method based on big data, and relates to the technical field of big data, and the data acquisition method comprises the following steps: obtaining to-be-acquired data; obtaining target collection data; marking target acquisition abnormal data; evaluating the influence degree of the target acquisition abnormal data; and judging whether the target acquisition abnormal data meets a preset data acquisition standard or not. Based on the anomaly recognition algorithm, anomaly detection and marking can be quickly performed on the target data, accurate positioning of potential problem data is realized, the data anomaly processing efficiency can be improved, the abnormal data is compared with the existing database through the evaluation algorithm, the influence degree of the abnormal data can be accurately evaluated, and the accuracy of data anomaly processing is improved. The method shortens the manual judgment time, judges whether the data meets the collection standard or not according to the evaluation result, can effectively eliminate irrelevant or low-value data, guarantees the high correlation and application value of the collected data, and reduces the processing cost of invalid data.

The invention relates to the technical field of automobile data processing, and discloses an electric seat abnormity detection method and device, and the method comprises the steps: monitoring the starting state of an electric seat in real time; when the starting state of the electric seat is a heating state, calculating a temperature rising speed within first preset time; if the temperature rising speed is smaller than the preset temperature rising speed threshold value, it is judged that heating of the electric seat is abnormal; when the starting state of the electric seat is a refrigeration state, calculating a cooling speed and a wind speed noise correlation coefficient within a second preset time; if the cooling speed is smaller than a preset cooling speed threshold value or the wind speed noise correlation coefficient is out of a preset correlation standard range, it is judged that refrigeration of the electric seat is abnormal. The real-time performance of data monitoring is improved, the accuracy of monitored data is improved, and the riding comfort of passengers is improved.

The invention relates to a power distribution network exception handling method and device considering island operation, computer equipment, a readable storage medium and a program product. Island division is carried out in the power distribution network through information of each controllable distributed power supply and information of each section switch in the power distribution network to form an island region, and when it is determined that the island region is in an abnormal state according to difference values between output information of each load and a main distributed power supply in the island region and a preset power target value, the power distribution network is in an abnormal state. And carrying out separation processing on the load according to the weight corresponding to the load so as to realize exception recovery. Compared with the traditional abnormal processing without considering the island operation, the scheme carries out the island region division through combining the controllable distributed power supply information and the section switch information, detects the abnormality of the island region based on the load and output information in the island region, and carries out the abnormality recovery of the island region in the power distribution network through a load separation mode. And the exception handling efficiency in the power distribution network is improved.

The invention discloses a safety processing method, device and equipment for abnormal flash quit of an application program app. The security processing method is applied to a terminal, a user-defined app security processing module is preset in a terminal system program of an app, and the app security processing module is used for enabling the app to enter a security operation mode and performing security processing on the occurred exceptions when various exceptions occur in the operation process of the app; the safety processing method for the abnormal flash-back of the app comprises the steps that when an exception related to the flash-back of the app occurs in the running process of the app, the app safety processing module captures relevant information of the exception, the app is switched into a safety running mode, in the safety running mode, the app safety processing module starts a dynamic container page to replace an exception interface of the app, and the app safety processing module sends the exception to the app. And sending a security policy request message to the server, wherein the security policy request message carries the abnormal call stack information. According to the embodiment of the invention, when the app is abnormal, the app can enter the security mode.

The embodiment of the present invention discloses an exception processing method, device, terminal and storage medium, the method comprising: receiving a first exception message sent by at least one business system, wherein the first exception message is an exception message that meets a preset verification standard; recording the first exception message to an exception message table, and setting the processing status of the recorded first exception message to unrepaired, and updating the processing status according to the processing process of the first exception message; querying the exception message table in sequence, and using the first first exception message with an unrepaired processing status as the second exception message to be processed; broadcasting the second exception message so that the business system corresponding to the second exception message performs exception processing according to the monitored second exception message. The technical solution of this embodiment improves the efficiency of exception processing by sending exceptions occurring in different business systems to the exception processing system for processing, and can uniformly manage and repair different exceptions.

The invention belongs to the technical field of e-commerce informatization, and particularly relates to an informatization-based cross-border e-commerce business intelligent tracking management method and system, and the method comprises the steps: responding to business abnormality detection, and scheduling participants with independent data sovereignty; building a cross-participant federal causal atlas through a federal causal discovery module in a collaborative manner; dynamically configuring the business digital twinning model based on the causal relationship in the atlas, and generating a causal constraint enhanced digital twinning instance; encoding map structure features in a deep reinforcement learning agent, and taking an enhanced digital twinborn instance as an environment; and the operation agent outputs an intervention action sequence. According to the method, the problems of cross-border e-commerce data island, decision lag and lack of intelligent support are solved, privacy protection collaborative analysis of cross-domain data and active prediction and interpretable intelligent decision based on a causal mechanism are realized, and the overall toughness and decision efficiency of a supply chain are improved.

The present invention provides an abnormal information processing method, system, artificial intelligence platform, equipment and medium, which relates to the field of artificial intelligence technology. The method includes: according to the target reasoning service name information corresponding to the target reasoning service where the abnormal situation occurs, the state and event information of each service resource is obtained from the state and event database of the current cycle, wherein the state and event database is constructed based on the ownership relationship between the service resources, and the state and event database is used to store the state and events of the main service resources and sub-service resources in the ownership relationship; the preset resource composition table is constructed based on the definition configuration information of the service resources periodically obtained by the container cluster management tool; the state and event information is input into a large language model to obtain the reasoning abnormality analysis result. The present invention improves the efficiency of reasoning service exception processing, and can also more accurately locate the reasoning service abnormality problem.

The invention relates to the technical field of vehicles, and discloses a vehicle system data display method and system and electronic device.The method is applied to the electronic device and comprises the steps that in response to operation of selecting a target vehicle type, a first identifier corresponding to the target vehicle type is determined according to a first corresponding relation; a target three-dimensional model corresponding to the first identifier in a second corresponding relation is obtained, the second corresponding relation is the corresponding relation between the identifier of the vehicle type and the three-dimensional model of the vehicle, and each three-dimensional model comprises a plurality of system sub-models in one-to-one correspondence with a plurality of systems of the vehicle; displaying a first interface including the target three-dimensional model; in response to the first data reading operation, reading partial data of each system in a plurality of systems in the target vehicle according to a preset rule to obtain first data, the plurality of systems being in one-to-one correspondence with a plurality of system sub-models in the target three-dimensional model; and displaying the first data in the first interface. According to the invention, the vehicle system data can be visually displayed.

The invention relates to the technical field of vehicles, and discloses a state information display method and device for vehicle components and electronic equipment, and the method comprises the steps that state information of a plurality of vehicle components related to execution of a regeneration function of a vehicle diesel particulate filter (DPF) is obtained; an interactive interface is displayed, and the interactive interface comprises a plurality of schematic diagrams corresponding to the vehicle parts; state information of all the vehicle parts is displayed in areas related to all the schematic diagrams in the interactive interface; in response to the operation of clicking the start button, judging whether the state information meets a preset condition for executing a vehicle DPF regeneration function or not; and displaying at least one of the state information which is provided with the first identifier and does not meet the preset condition, the target state information meeting the preset condition and the operation method for achieving the state corresponding to the target state information in the interaction interface. According to the method and the device, the state information of the related vehicle parts can be visually displayed when the vehicle executes the DPF regeneration function.

The invention provides a construction method of a battery voltage model, the battery voltage model, a device and equipment. The method comprises the following steps: acquiring a candidate battery voltage model of a to-be-modeled battery object; acquiring a reference terminal voltage of the battery object; obtaining a historical battery parameter set of the battery object, and obtaining a measurement terminal voltage of the battery object based on the historical battery parameter set through the candidate battery voltage model; and adjusting and optimizing the candidate battery voltage model according to the reference terminal voltage and the measurement terminal voltage to obtain a target battery voltage model of the battery object. According to the method, the accuracy and the efficiency of the terminal voltage obtained by measuring and calculating the battery object through the target battery voltage model are improved, the measuring and calculating precision of the target battery voltage model is improved, accurate data support is provided for downstream tasks of the battery object, the precision and the efficiency of abnormal monitoring of the battery object are improved, and the reliability of abnormal monitoring of the battery object is improved. And the running safety and stability of the battery object are optimized.

The present application relates to a data acquisition method, device, computer equipment and storage medium. The method comprises: after determining that an abnormality occurs in a medical device, sending a list acquisition request to the medical device; the list acquisition request is used to instruct the medical device to send a data acquisition pile list; the data acquisition pile list contains pile information of multiple data acquisition piles pre-set in the main program; receiving the data acquisition pile list sent by the medical device, and sending a data acquisition request to the medical device according to the data acquisition pile list; the data acquisition request is used to instruct the medical device to send target data; receiving the target data sent by the medical device. The use of this method enables developers to obtain the first-site data in a timely manner, thereby seizing the best opportunity for abnormal diagnosis.

The invention relates to a transformer substation abnormal equipment disposal method and system, and belongs to the technical field of transformer substation operation management. According to the abnormal information, in combination with a power grid operation mode and a fault handling principle, an abnormal equipment isolation strategy is researched and judged; judging whether a parallel line exists on the bus or not, if yes, carrying out protection tripping criterion and protection information equipment signal abnormity analysis on the parallel line, and determining switch isolation of the parallel line according to a line power failure range minimization principle; and adding the determined switch isolation into the list, and identifying protection tripping equipment, protection information equipment and signal abnormal equipment. According to the invention, through correlation analysis and protection and information protection equipment signal fault criteria, reasonable processing prompts are provided for operators, the processing efficiency of the operators of the power system is improved, and the correctness of abnormal processing is improved.

The invention discloses a job task exception processing method and device, equipment and a medium. The method comprises the steps of obtaining a running log corresponding to a job task; identifying a running log corresponding to the job task to obtain job information corresponding to the job task; determining a target abnormal source corresponding to the job task according to the job information corresponding to the job task; and obtaining an exception handling script corresponding to the target exception source, and executing the exception handling script to perform exception handling on the job task. According to the embodiment of the invention, the operation task exception processing accuracy can be improved.

The application discloses a kind of water quality multi-parameter online monitoring system and method of water supply and drainage pipe network, the system includes water supply and drainage pipe network system, drainage sampling module, detection center module, data transmission module, cloud center module, monitoring center module and system management and maintenance module;Drainage sampling module is set at the key node of water supply and drainage pipe network, and the water sample to be detected is automatically drained to detection center module by control valve;Detection center module carries out the multi-parameter detection of pH value, turbidity, heavy metal ion concentration, organic pollutant content, dissolved oxygen and conductivity to water sample;Data transmission module carries out encrypted transmission to cloud center module with the detection data of time stamp and positioning information;Cloud center module carries out data cleaning, denoising, normalization, data mining, deep learning trend prediction and abnormal early warning;Monitoring center module displays water quality state in real time based on dynamic node diagram, generates alarm, pollution route and pollution traceability result when abnormal.

The embodiment of the invention discloses a parking fee reduction and remission abnormity processing method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining order information of a to-be-detected order; according to the order information and a pre-trained exception recognition engine, determining an exception reason of the to-be-detected order; and according to a preset processing mode corresponding to the abnormity reason, executing an abnormity processing flow, an abnormity identification engine performing multi-dimensional analysis on order information of a historical order to determine, and the historical order being a charging order which occurs before the to-be-detected order and is abnormal in parking fee reduction and remission. Therefore, according to the method, automatic identification of the abnormal reason of the to-be-detected order and timely processing of the order reduction and remission abnormity are realized based on the abnormity identification engine, and the abnormal reason identification and abnormity processing efficiency during parking fee reduction and remission abnormity can be improved.

The present application belongs to the field of temperature and pressure reducing equipment, and relates to data analysis technology, and is used for solving the problem that the temperature and pressure reducing actuator in the prior art cannot monitor parameters such as deformation and cracks of the pipeline thereof during operation, and in particular relates to a bimodal control box for a temperature and pressure reducing actuator and a control system thereof, which comprises a processor, the processor being communicatively connected with a deformation monitoring module, a crack monitoring module, an operation evaluation module and a storage module; the deformation monitoring module is used for monitoring the deformation of the pipeline of the temperature and pressure reducing actuator; a monitoring period is generated, the monitoring period is divided into a plurality of monitoring time periods, the pipeline of the temperature and pressure reducing actuator is marked as a monitoring object, and the deformation coefficient of the monitoring object in the monitoring time period is obtained; the present application can monitor the deformation of the pipeline of the temperature and pressure reducing actuator, so that the deformation coefficient is obtained through numerical calculation according to the deviation values of all monitoring points, and the deformation state of the monitoring object in the monitoring time period is fed back through the deformation coefficient.

The present invention provides an exception handling method and device. When an exception is detected, the root cause of the exception is analyzed to obtain the root cause of the exception, so as to merge the sliding window exceptions based on the root cause of the exception, and determine whether a new fault has occurred based on the root cause of the exception and the time when the exception occurred, so as to avoid merging exceptions of different faults together and not merging exceptions of the same fault, thereby improving the efficiency of exception handling.

The present invention relates to the field of electronic digital data processing technology, specifically to an anomaly detection and processing method and system based on deep learning. The present invention first extracts and fuses features of a log text sequence, a log timestamp sequence, and a set of operating status indicator sequences to obtain an initial feature vector sequence; then a hierarchical graph neural network is constructed to update, propagate, and fuse the initial feature vector sequence to obtain a multi-level fusion feature vector sequence, and a standardized pattern vector sequence is calculated in combination with the log text sequence and the set of operating status indicator sequences, and an adaptive anomaly threshold is obtained according to the standardized pattern vector sequence and candidate anomaly logs are detected; finally, the candidate anomaly logs are verified, the verified anomaly logs are determined, the verified anomaly logs are classified, and a processing suggestion text is generated according to the classification results and an early warning prompt is given. The present invention can reduce the false detection rate and improve the accuracy of log anomaly detection.

The present invention belongs to the field of road engineering and relates to data analysis technology. The present invention is used to solve the problem that the road engineering quality detection device in the prior art cannot detect the road soil before construction. Specifically, the present invention is a road engineering quality detection device, comprising a detection locomotive, wherein a driving motor and a storage bin are respectively arranged at the front and rear sides of the detection locomotive, and a processor is arranged at the top of the detection locomotive; the processor is communicatively connected with a soil detection module, a construction detection module and an optimization analysis module; the soil detection module is used to detect and analyze the road soil before the road engineering construction; the construction detection module is used to detect and analyze the construction quality after the road engineering construction; the present invention can detect and analyze the road soil before the road engineering construction, and timely feedback and processing can be performed when the road soil quality is unqualified, so as to avoid the phenomenon that the construction quality cannot be guaranteed due to direct construction, and improve the control degree of the road engineering construction quality.

The present invention discloses a BIM-based intelligent analysis and monitoring system for building electrical equipment. The BIM-based intelligent analysis and monitoring system for building electrical equipment includes a fire pump basic information acquisition module, a fire pump structure monitoring and analysis module, a fire pump cable monitoring and analysis module, a fire pump operation monitoring and analysis module, a fire pump comprehensive state analysis module, and an early warning feedback and marking module; the present invention locates the layout information of the fire pump from the BIM model corresponding to the specified building, and monitors and analyzes the state information, cable state information, and operation information corresponding to each component structure of each fire pump, thereby performing fire early warning analysis and early warning information marking, effectively solving certain limitations of the current technology, greatly improving the comprehensiveness of fire pump operation safety monitoring, and effectively reducing the maintenance cost and difficulty of the fire pump.