893 results about "System maintenance" patented technology

The invention discloses a high-precision current detection circuit calibration system, and belongs to the technical field of current detection circuit calibration. Comprising a shunt resistor, a Hall sensor array, a chopping amplifier, a low-noise operational amplifier, a temperature sensor, an ADC acquisition unit, an RC low-pass filter, a signal conditioning unit, a main control unit, an environmental parameter adaptive compensation module and a gain adaptive adjustment module. After an original signal is obtained from a multi-channel sensor, a dynamic error model is constructed in combination with temperature data, a long-term imbalance trend is extracted by using a moving average or median filtering algorithm, and real-time compensation is performed through a pre-calibrated temperature-drift function, so that the system maintenance period is remarkably prolonged.

The invention discloses an interactive simulation method for calculating aging parameters of a superconducting cable and a superconducting cable system.The system comprises the steps that a cable body electromagnetic transient model, a cooling system transient thermal model and a three-dimensional multi-field finite element model are built, and cable material characteristics, environment parameters and operation condition data are input; a model interaction interface is constructed through a simulation platform, data interaction and coupling among multiple models are realized, simulation parameters are dynamically adjusted, accurate matching of model step length and dynamic characteristics of the system is ensured, and aging parameters of the superconducting cable system under complex working conditions are calculated. According to the method, the operation state of the superconducting cable is evaluated by analyzing the mixed data, aging analysis of the superconducting cable system under multiple working conditions is carried out, and decision support is provided for system maintenance.

The invention provides an intelligent monitoring method and system based on task operation historical information, and relates to the technical field of intelligent monitoring, and the method comprises the steps: collecting target task operation data to construct a task operation portrait, carrying out the real-time monitoring, calculating a task health degree score, and when the health degree score is lower than a preset threshold value, executing the task operation. And an intelligent diagnosis mechanism is automatically triggered to identify the exception type and execute a corresponding processing strategy, and exception processing experience is stored in a historical information database to optimize the diagnosis accuracy. According to the invention, the intelligent level of task monitoring can be improved, rapid and accurate identification and processing of abnormities are realized, and the maintenance cost of the system is reduced.

The invention discloses an intelligent flow sensor test analysis method and system, and relates to the technical field of sensor testing, and the method comprises the steps: applying a stepped flow excitation signal to a tested sensor through a standard flow generation device, and synchronously collecting the dynamic response data of the sensor; extracting a feature parameter set from the response data; performing similarity matching calculation to obtain a matching degree evaluation value of each error type; quantifying the induction relation strength among different error types; generating a composite weight coefficient in combination with the matching degree evaluation value and the evoked relationship strength; and making a multi-stage compensation strategy based on the composite weight coefficient. The method has the advantages that by combining error correlation network modeling driven by historical data, induction mechanisms and interaction relations of different error types can be analyzed, the pertinence of a compensation strategy is greatly improved, a multi-stage error suppression mechanism is formed, stable control over comprehensive errors is achieved under complex working conditions, and meanwhile the system maintenance requirement is lowered.

The invention discloses a superconducting cable aging state evaluation system and method based on mixed data, and the system comprises a data analysis module, an aging evaluation module, a physicochemical association module and a report generation module which are in communication connection. Wherein the data analysis module comprises a simulation sub-module, a data acquisition sub-module and a data processing sub-module connected with the data acquisition sub-module, and in the aging state evaluation process of the aging evaluation module, superconducting cable system model parameters are output and calibrated through a physicochemical association model; and then an aging state evaluation fitting function is output through the data processing sub-module, and an aging state evaluation report is automatically generated by the report generation module. According to the method, the operation state of the superconducting cable is evaluated by analyzing the mixed data, aging analysis of the superconducting cable system under multiple working conditions is carried out, and decision support is provided for system maintenance.

A maintenance reservation system according to a first aspect includes: an equipment information acquisition unit configured to acquire equipment information including information about equipment; a maintenance contractor information acquisition unit configured to acquire maintenance contractor information including information about maintenance contractors who provide maintenance services on the basis of information transmitted from maintenance contractor terminals; a reservation information acquisition unit configured to acquire reservation information including desired conditions for the maintenance services on the basis of information transmitted from a user terminal; a processing unit configured to select maintenance contractor candidates on the basis of the equipment information and the maintenance contractor information, and narrow down the candidates on the basis of the desired conditions and the maintenance contractor information corresponding to the maintenance contractors included in the candidates; and a notification unit configured to notify the user terminal of the candidates narrowed down by the processing unit.

The present application provides a message-driven microservice dynamic orchestration method and related equipment. The method is applied to a microservice dynamic orchestration system, wherein the system includes multiple microservices, and the method includes: obtaining the impact factor of the microservice from the business message; generating the process orchestration data of the microservice according to the message subject preset by the microservice and the impact factor, and storing the process orchestration data in a cache center; allowing each of the microservices in all the microservices to read the process orchestration data from the cache center and determine the next microservice connected to it according to the process orchestration data. The method provided by the present application realizes the dynamic orchestration of microservices, and there is no need to change or restart the microservice when adjusting the microservice call link, thereby ensuring the work efficiency of the system, reducing the difficulty of system maintenance, and solving the problem that the microservice call link is difficult to adjust.

The application relates to the technical field of text label marking, and provides a text specification marking method and system based on a rule corpus, which comprises the following steps: analyzing a policy and regulation document, identifying and marking condition morphemes and conclusion morphemes in the policy and regulation document, constructing a logical relationship between the two by using a large language model, and forming a rule corpus composed of structured morpheme pairs; performing semantic embedding on the corpus to generate a semantic vector library; performing multi-label coding on a verification data set based on the rule corpus, and constructing a multi-label training data set; training a deep learning classification model by taking semantic vectors as features and multi-labels as targets, so that a text specification marking model is obtained; and automatically marking target text by using the model. The application significantly improves the accuracy, interpretability and business adaptability of text marking, improves the update quality of a system label data set, and reduces the system maintenance cost.

The invention belongs to the technical field of gas detection, and relates to a ship exhaust gas multi-component synchronous online monitoring system and method based on a laser Raman spectrum. The system comprises a sampling module, a preprocessing module, an analysis module, a data processing module, a blowback module and a flow measurement module. The method comprises the following steps: starting and self-checking the system; sample collection, pretreatment and flow velocity measurement; laser excitation and signal acquisition; carrying out spectrum analysis and concentration inversion; data output and system maintenance; and performing periodic automatic calibration. According to the invention, a complete monitoring process chain from sampling, pretreatment, flow measurement, analysis to back flushing is creatively integrated. The high-efficiency pretreatment and back flushing design greatly reduces the influence of high-dust and high-humidity environment on the analysis system, and ensures the long-term stability and reliability of the system.

A unitary hermetically sealed device is disclosed for providing self-contained electric propulsion drive power to a motor boat or sailboat, where the entire propulsion system is contained in a separable pressurized hermetic enclosure attached to the underside of the vessel and also acts as a keel of the vessel, and which simplifies production, provides improved safety from high voltages, and prevents drive system maintenance or repair when the vessel is located in the water. The rigid hermetically sealed device functions as a skid-plate to avoid damage to the softer typically fiberglass hull when running the vessel aground or when running up onto or off of the beach or onto or off a trailer.

The invention discloses an information acquisition system and method suitable for visually impaired people. The system comprises a hardware layer, a driving layer, an operating system layer, a middleware layer and an application layer which are sequentially in communication connection through interfaces. The application layer comprises a voice interaction module, a large model integration module and a braille conversion module; the hardware layer is used for collecting a voice input instruction and outputting a braille character sequence and a voice output result; the voice interaction module is used for converting a voice input instruction into a text input instruction and converting a text output result into a voice output result; the large model integration module is used for analyzing the text input instruction and generating a text output result; and the braille conversion module is used for converting the text output result into a braille character sequence, so that a hardware layer outputs the braille character sequence based on a timestamp alignment technology. Therefore, based on powerful semantic understanding and information retrieval of the large model integration module, accurate information acquisition is realized, and a five-layer decoupling architecture is adopted, so that the convenience of later system maintenance is improved.

The invention provides a DCS system maintenance system based on networking intelligent monitoring and a method thereof, and relates to the technical field of intelligent manufacturing and high-voltage circuit breaker health management. Firstly, an independent classification identifier and a unique increasing number are distributed for each type of high-voltage circuit breaker, and multi-dimensional operation data of temperature, pressure, vibration and current are collected at fixed time intervals and are stored in a high-voltage circuit breaker state data set according to the number and timestamp of the high-voltage circuit breaker. And then constructing a dynamic weight distribution formula, outputting a health state score and recording the health state score to a health state database. And extracting operation data in a fault state to construct an abnormal data set, combining the health state score with the abnormal data set, and performing deep training and prediction by using an LSTM model so as to obtain a future fault occurrence probability of the high-voltage circuit breaker. Once the predicted fault occurrence probability of the high-voltage circuit breaker is higher than a fault threshold value, the high-voltage circuit breaker is immediately calibrated as a potential fault high-voltage circuit breaker, a detailed and targeted maintenance plan is generated, and meanwhile, the operation parameters of the potential fault high-voltage circuit breaker are rapidly adjusted.

The invention relates to the technical field of hybrid submarine cable system maintenance, in particular to a reactive compensation method, system and device for a hybrid submarine cable system and a medium, and the method comprises the steps: obtaining the standard electrical parameters of an oil-filled submarine cable and an XLPE submarine cable; constructing an electromagnetic transient simulation model of the hybrid submarine cable system; calculating an ideal reactance compensation range; a plurality of groups of series reactance compensation schemes are set and applied in the electromagnetic transient simulation model, and three-phase transmission power distribution, total transmission power and voltage unbalance degree under each series reactance compensation scheme are simulated and calculated; comparing simulation results of each series reactance compensation scheme, and selecting the series reactance compensation scheme with the maximum total transmission power on the premise that the three-phase power unbalance degree is lower than a preset equilibrium constraint condition as an optimal compensation scheme; an optimal compensation scheme is applied in a hybrid submarine cable system. According to the invention, three-phase power balance and total transmission power maximization of the hybrid submarine cable system can be realized at the same time.

The utility model discloses a packing spray tower which comprises a tower body, a gas outlet pipe is arranged at the top of the tower body, a gas inlet pipe is arranged on the side wall of the lower part of the tower body, two screen plates are fixedly arranged in the tower body, a packing layer is arranged on each screen plate, a spray component is arranged on the upper side of each packing layer, a maintenance window is arranged on one side of each spray component, and a gas inlet pipe is arranged on the side wall of the lower part of the tower body. The maintenance window is arranged on the side wall of the tower body, a window cover plate is arranged on one side of the maintenance window, when the spraying assembly is maintained, a maintainer can take down the window cover plate from the maintenance window and then take out the spraying assembly from the tower body from the maintenance window, and the maintenance window is arranged on the side wall of the tower body. And then, a maintainer can directly carry out maintenance operation on the spraying assembly outside the tower body without stretching the body into the tower body through a maintenance window, so that the maintenance of the spraying system of the filler spraying tower is more convenient.

The invention relates to the technical field of text label marking, and provides a text standard marking method and system based on a rule corpus, and the method comprises the steps: analyzing a policy and regulation file, recognizing and marking a condition morpheme and a conclusion morpheme in the policy and regulation file, constructing a logic relation between the condition morpheme and the conclusion morpheme by using a large language model, and marking the rule corpus; forming a rule corpus consisting of structured morpheme pairs; performing semantic embedding on the corpus to generate a semantic vector library; performing multi-label coding on the verification data set based on the rule corpus, and constructing a multi-label training data set; training a deep learning classification model by taking the semantic vector as a feature and taking multiple tags as a target to obtain a text specification marking model; and automatically marking the target text by using the model. According to the method, the accuracy, the interpretability and the service adaptability of text marking are remarkably improved, the updating quality of the system label data set is improved, and the system maintenance cost is reduced.

The invention discloses a battery energy storage system predictive maintenance method and system based on a large language model, and relates to the field of battery energy storage system predictive maintenance, and the method comprises the following steps: collecting multi-modal sensor time sequence data of a battery energy storage system, and carrying out the interception through a sliding window to obtain a multi-modal sensor time sequence data fragment; inputting the time series data fragments of the multi-modal sensor into a large language model for feature extraction to obtain a probabilistic semantic tag set; the probabilistic semantic tag set analyzes a causal relationship between probabilistic semantic tags through a fault mode correlator, and calculates to obtain a dynamic risk score and a recommendation action; and adjusting operation parameters of the battery energy storage system according to the dynamic risk score, and executing the recommended action at the same time to realize predictive maintenance of the battery energy storage system. According to the invention, the problem of lack of real-time dynamic labeling during maintenance of the battery energy storage system in the prior art can be solved.

The invention discloses a power grid space image fine labeling method, system and device based on SAM semantic segmentation and a medium, and belongs to the technical field of power system maintenance, and the method comprises the steps: obtaining an initial device label through semantic segmentation model labeling; generating probability distribution of power grid equipment categories through the satellite image, and obtaining a final equipment label through the probability distribution of the power grid equipment categories; collecting defect information of the power grid equipment to generate a defect report, and marking the machine patrol image through the semantic segmentation model in combination with the defect report to obtain a defect mark; correcting the final equipment label and the defect label, and training the semantic segmentation model; according to the method, the semantic segmentation model is combined with the probability distribution of the power grid equipment category, the equipment characteristics in the satellite image under the complex background are extracted, and the image processing capability and the marking accuracy are improved; manual intervention is reduced through entropy value screening data calculation and self-training iteration logic, the labeling and maintenance cost is reduced, and the requirements of a power grid for the image recognition accuracy and the automation degree are met.

A device for the maintenance of an overhead line system of a track has a handling device and a displacement device. The displacement device serves to displace the handling device between a transport position and a working position. In the working position, the handling device reaches the overhead line system and is able to perform maintenance work by way of a tool. At least one sensor serves for controlling and / or monitoring the handling device. The device thus enables a simple, efficient and safe maintenance of the overhead line system.

The invention discloses urban sewage deep purification equipment capable of dynamically intercepting and feeding back and a purification treatment method.The urban sewage deep purification equipment comprises a reaction tank, a reaction device is fixedly installed in the reaction tank, connecting pipes are symmetrically and fixedly installed on one side of the reaction tank, and one end of each connecting pipe is connected with the reaction device in a matched mode; the water inlet unit is fixedly mounted at the top of one end of the reaction tank. Through the arrangement of the start-stop mechanism, water inflow can be rapidly cut off, sewage and suspended solids are prevented from continuously flowing into the reaction tank, stable operation of the subsequent purification process is effectively protected, time can be bought for workers to clean the suspended solids in the filter frame, the frequency and strength of manual inspection are reduced, meanwhile, the water inflow time is accurately controlled, and the efficiency is improved. Equipment damage caused by excessive blockage of the filter frame can be avoided, the service life of the filter assembly is prolonged, the maintenance cost of the system is reduced, and efficient, stable and low-consumption operation of the whole sewage purification system is guaranteed.

The invention belongs to the technical field of water affair management, and discloses an intelligent water affair dynamic monitoring system and monitoring method based on digital twinning. Comprising the steps of collecting multi-modal water affair data in a preset water affair system and performing data cleaning to obtain standard multi-modal water affair data; adaptive weighted fusion is carried out on the standard multi-modal water affair data, and a multi-modal water affair data matrix is constructed; constructing a digital twin water affair architecture and constructing a simulated water affair management scene; constructing an ecological evaluation model to perform water quality monitoring on the simulated water affair management scene, and outputting water quality evaluation data; constructing a water affair management partition topological structure to perform water resource cross-region scheduling on the simulated water affair management scene, and outputting a water affair partition management strategy; constructing a water affair system assembly operation scene to carry out system fault monitoring, and outputting a system maintenance log; collecting real-time water affair data and combining with a system maintenance log to perform parameter updating on the digital twin water affair architecture to obtain an optimized digital twin water affair architecture; and efficient water affair management is realized.

The invention discloses a transmission tower material missing detection method and device based on three-dimensional point cloud data, and belongs to the technical field of power system maintenance, and the method comprises the steps: obtaining the three-dimensional point cloud data of a transmission tower, and carrying out the denoising and thinning processing; classifying the three-dimensional point clouds; different tower material components are separated from the tower point cloud categories by adopting a clustering algorithm, the tower is disassembled into independent tower material components, and geometric parameters are marked; positioning intersection points of the tower material components, screening density peak points as candidate nodes, and verifying the number of the tower material components connected with the candidate nodes and geometric included angle conditions; the three-dimensional point cloud is converted into a topological graph with nodes and edges connected, and it is ensured that the topological graph meets the engineering standard through rule verification; detecting the topological graph based on a preset rule; and for the detected abnormal nodes / edges, reversely deducing theoretical positions and parameters of missing tower material components, generating a three-dimensional visual report and marking missing positions. According to the method, the limitation of traditional manual tower material identification is broken through, and the detection efficiency and reliability are improved.

The invention discloses an intrusion detection rule self-adaptive generation and optimization method based on a large language model, and belongs to the technical field of network security, and the method comprises the steps: collecting and preprocessing multi-source original data, and obtaining a high-quality data set; obtaining a key mode based on the high-quality data set and the fine-tuned large language model, and generating an initial rule template based on the key mode; on the basis of the real-time traffic data and the initial rule template, generating new candidate rules in combination with large language model reasoning, and sorting the candidate rules; performing optimization screening on the candidate rules by adopting a deep reinforcement learning algorithm and a genetic algorithm; and distributing and synchronously updating the optimized and screened candidate rules through a distributed architecture. According to the method, automatic generation and dynamic optimization of the intrusion detection rule are realized, the real-time detection capability of novel unknown threats is remarkably improved, and meanwhile, the false alarm rate and the system maintenance cost are greatly reduced.

The invention discloses a deep learning-based fault prediction method for a bulk cargo port area belt type conveying system, and belongs to the field of fault prediction for the bulk cargo port area belt type conveying system. The method comprises the following steps of: 1, cleaning fault shutdown data of a harbor area belt type conveying system, complementing and extracting key component data of the belt type conveying system to obtain original data; step 2, carrying out belt conveying system degradation curve fitting based on Weber distribution; and 3, constructing a deep learning model, excavating the relationship between the deterioration trend of the key components of the belt conveying system and the fault of the belt conveying system, and accurately predicting the fault-free working time of the belt conveying system. And 4, designing an experiment and analyzing a result. According to the invention, several different deep learning models are utilized to predict faults of the bulk cargo port area belt conveying system, and a Weber distribution method is adopted to make a data set. Through the method, a decision can be provided for making a maintenance plan of the belt type conveying system, and potential safety hazards caused by faults of the belt type conveying system are reduced.

The invention relates to the technical field of artificial intelligence, and discloses a heat meter energy consumption behavior abnormity identification method based on a graph neural network, and the method comprises the steps: carrying out the collection and preprocessing of multi-dimensional data; constructing a three-dimensional unified representation model comprising a graph topology encoder, a time domain sequence encoder and a frequency domain feature encoder; a time-frequency joint attention mechanism is realized, and tiny abnormal sensitivity and anti-noise stability are dynamically balanced; constructing a hierarchical self-encoding network, and calculating a reconstruction error on a plurality of abstract hierarchies; calculating an anomaly score based on the reconstruction error distribution characteristics in the unified representation space, and generating an anomaly interpretation report; and optimizing and deploying the model. According to the method, time domain, frequency domain and graph domain information are organically fused, and the detection accuracy rate reaches 95% or above; tiny abnormal signals can be captured, and the abnormal early warning time is advanced to 15-20 days; the false alarm rate is controlled below 3% while high detection sensitivity is maintained; the parameter quantity is reduced by 40%, the calculation complexity is reduced by 58%, and the system maintenance cost is greatly reduced.

The invention relates to the technical field of fault maintenance, and particularly provides an intelligent decision-making system for fault maintenance of multi-type loading and unloading equipment of an ore wharf, which comprises an acquisition and transmission module, a cleaning and extraction module, a fault diagnosis module, a fault pre-control module, a graded early warning module, a resource scheduling module and a system maintenance module. The full-dimensional monitoring of the equipment state can sensitively capture tiny abnormities in the operation of the equipment, find early fault symptoms of bearing wear and hydraulic oil degradation in advance, change the problem of high omission ratio of traditional manual inspection, strive for sufficient processing time for equipment maintenance, enable the equipment maintenance to be changed from dependence on artificial experience to data support, and improve the equipment maintenance efficiency. And the diagnosis efficiency and the prediction accuracy are greatly improved. The problem of conflicts in traditional resource scheduling is effectively solved, reasonable configuration of maintenance resources is achieved, the fault processing period is shortened, the overall efficiency of operation and maintenance of wharf equipment is improved, accurate control over the equipment state is kept, and the method adapts to dynamic changes of operation scenes of multiple types of loading and unloading equipment of an ore wharf.

The invention provides a photovoltaic power generation system fault intelligent diagnosis method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a photovoltaic power generation system target operation data set which comprises an equipment state data flow, an environment perception data flow and an energy transmission data flow, and carrying out the feature extraction to generate a target feature representation; calling a pre-trained fault intelligent diagnosis model to carry out abnormal mode decoding processing on the target feature representation, generating an intermediate diagnosis result containing an abnormal feature chain, and determining a fault type and a fault propagation network of the photovoltaic power generation system based on the intermediate diagnosis result; and finally, a diagnosis report containing a network topology identifier is generated according to the fault type and the fault propagation network and is used for triggering system maintenance response operation, so that intelligentization and precision of fault diagnosis of the photovoltaic power generation system are realized, and the reliability and the operation and maintenance efficiency of the system are improved.

The invention discloses a business processing method and device based on micro-service, electronic equipment and a storage medium, relates to the technical field of data processing, and compared with the related technology, according to the application, insurance business requests are accurately distributed to corresponding micro-service modules according to the types of the insurance business requests, so that different types of businesses are independently processed by special modules; decoupling and specialized division of system functions are realized, and mutual dependence among modules is reduced, so that the technical problems of high system coupling degree, high maintenance cost, low task processing efficiency and performance bottleneck during high concurrency in the existing insurance service system can be solved; the technical effects of improving the insurance service processing efficiency, reducing the system maintenance cost, enhancing the ability of the system to deal with high-concurrency services, and further improving the insurance service processing quality and the customer experience are achieved.

This invention relates to the field of heat exchange technology, and more particularly to a plug-in frame CDU system and its control method. The plug-in frame CDU system includes an outdoor heat dissipation module, a server heat exchange module, and a power auxiliary module. The outdoor heat dissipation module is used to dissipate heat and cool the heat exchange medium, while the server heat exchange module is used to allow the heat exchange medium to absorb heat from the server. The power auxiliary module includes a power component and a detection component. The input end of the power component is connected to the output end of the outdoor heat dissipation module, and the output end is connected to the input end of the server heat exchange module. The input end of the detection component is connected to the output end of the server heat exchange module, and the output end is connected to the input end of the outdoor heat dissipation module. The outdoor heat dissipation module, power component, server heat exchange module, and detection component are sequentially connected to form a loop, thereby forming a circulating heat exchange system to absorb heat from the server and release the heat to the outdoor environment. This plug-in frame CDU system can reduce energy consumption, reduce operating costs, and simplify system maintenance.

The invention discloses a toughness-driven underwater production system on-condition maintenance decision optimization method and system, and belongs to the field of underwater production system maintenance decision, and the method comprises the steps: predicting the remaining service life of a system, modeling the natural degradation performance of a component based on Weibull distribution, and quantifying the random impact degradation amount by combining a Poisson-normal impact model; performing superposition calculation on the total degradation performance of the system and predicting the remaining service life; predicting oil and gas yield, and fitting historical yield data by adopting an I-type generalized mathematical model; performing real-time toughness evaluation, fusing technical indexes and economic indexes, and calculating a comprehensive toughness value through a performance retention function and a yield stability function; and maintenance decision optimization: taking the real-time toughness value as an input variable, establishing a dual-objective decision model for maximizing the system toughness and minimizing the maintenance cost, and solving an optimal maintenance trigger threshold by adopting a multi-objective optimization algorithm, the maintenance decision is driven through the toughness index, and the reliability and economical efficiency of the underwater production system are effectively improved.

The embodiment of the invention provides a multi-agent-based attack simulation system and method and electronic equipment, and the system comprises an asset detection agent which is used for processing obtained server-side data so as to generate structured environment summary information; the retrieval agent communicates with the asset agent and is used for receiving the environment summary information sent by the asset detection agent, matching known vulnerability information based on the environment summary information and generating a vulnerability utilization execution plan; the execution agent communicates with the retrieval agent and is used for simulating an attack by utilizing an execution plan based on the received vulnerability in the target environment and determining a vulnerability processing result; and the report agent is used for generating a simulation attack report based on the vulnerability processing result and displaying the simulation attack report. According to the technical scheme, the intelligent agents based on various processing functions cooperatively process the penetration testing process, intelligent decision making of penetration testing is achieved, the task processing efficiency and accuracy are improved, manual screening is reduced, and the system maintenance and development cost is reduced.