670 results about "Troubleshooting" patented technology

The invention discloses an efficient processing method and system for online troubleshooting of the information department, and relates to the technical field of automatic operation and maintenance, and the system comprises a multi-modal data acquisition module, a dynamic knowledge graph construction module, a reinforcement learning driven self-healing module, an edge co-processing module and a cross-domain privacy calculation module. According to the method, the limitation of traditional single-dimensional monitoring is broken through, multi-source heterogeneous data such as server performance, container state and network flow are fused, a high-timeliness and high-consistency decision information base is constructed through unified time sequence alignment and semantic standardization, and a panoramic view is provided for dynamic analysis; based on an incremental learning mechanism and a causal inference engine, a service topology dependency relationship is updated in real time, a deep root cause is accurately positioned, and a cross-system and cross-level complex fault combination is solved.

A method for generating an auxiliary troubleshooting strategy for communication faults of a locomotive power battery system. The method comprises: acquiring a communication fault troubleshooting record of a locomotive power battery system; extracting fault causes at various levels for each type of communication faults, and establishing a fault tree; when there is a specific alarm communication fault, determining the time type and duration of the communication fault, and intercepting a fault subtree of the alarm communication fault; qualitatively analyzing the fault subtree to obtain a minimum cut set; quantitatively analyzing the fault subtree to obtain critical importance degrees of bottom events, and ranking same in a descending order; correcting the ranking of the importance degrees of the bottom events, so as to form a bottom event importance degree ranking table; and successively performing troubleshooting on the basis of the bottom event importance degree ranking table. Auxiliary troubleshooting strategies for communication faults are provided accurately, which improves the efficiency and accuracy of troubleshooting communication faults by maintenance staff, and helps to take corresponding maintenance measures, thus improving the utilization rate of new energy locomotives, and reducing downtime costs.

The invention discloses a storage system software defect analysis method and system based on a large model. The method comprises the following steps: firstly, collecting public and internal historical defect data and multi-source logs, training a lightweight log extraction model after cleaning, and coarsely screening original logs of newly added defects; and inputting a coarse screening result into a large language model, and further studying and judging to output a real key log. And fusing and vectorizing the key log and the Bug description, and writing the fused and vectorized key log and Bug description into Elasticsearch to construct a historical case knowledge base capable of semantic retrieval. When a new defect enters the system, quickly extracting a key log by utilizing the trained model, and executing dual-channel retrieval in the knowledge base to recall similar cases; and the big language model is combined with similar cases to automatically generate responsible person recommendation, root cause analysis and executable troubleshooting and avoiding suggestions. According to the method, the problem that historical cases are difficult to retrieve can be effectively solved, similar cases can be quickly and effectively found from massive historical data under the double-control of Bug description and key logs, and study and judgment assistance is provided.

Systems and methods are provided for automated incident investigation. Anomaly detection is used to identify anomalies in incident data (e.g., alerts, changes, metrics, logs, and / or system health), and the identified anomalies are converted into facts (or textual prompt inputs for a large language model (“LLM”)). A troubleshooting or diagnostic system is run on the anomalies to provide additional facts to identify a root cause of an incident. The facts from the diagnostics, the facts from the anomaly detections are entered into a consolidated explainer that generates a summary of what happened, what is a likely cause, and what to do next to resolve the issue. In examples, anomaly enrichment data including a time correlation result, a weighted list of abnormal transaction patterns, a list of abnormal trace patterns, a list of exception patterns, a difference pattern, and / or region data are input as further facts to enhance the incident investigation process.

The invention discloses a knowledge graph-AI fusion fault diagnosis method for operation and maintenance management of a power distribution network, and relates to the technical field of artificial intelligence. The problems of low initial diagnosis accuracy, insufficient diagnosis adaptation capability in different grounding forms, long fault positioning time and the like caused by external interference of zero-sequence current signals are solved, and the conditions that the operation and maintenance efficiency is reduced and the power supply loss is increased are finally avoided. The method comprises the following steps: acquiring multi-source data and constructing a dynamically updated power distribution network knowledge graph; multi-mode fault feature extraction is carried out; using an adaptive AI model to identify a fault type and performing primary judgment; and accurately positioning a fault section and analyzing a spreading path in combination with a knowledge graph. According to the method, the single-phase earth fault preliminary diagnosis accuracy is improved to reduce invalid troubleshooting of operation and maintenance, cross-scene diagnosis errors are reduced to adapt to diversified operation and maintenance scenes, average positioning time consumption is shortened, and the supporting capacity of fault diagnosis on operation and maintenance management of the power distribution network is remarkably improved.

Disclosed are apparatuses, systems, and techniques that train and use trained language models to assist users with complex systems installation, troubleshooting, and / or maintenance. A method can include determining, responsive to data received from a real robot having one or more real sensors and operating in a real environment, that the real robot needs assistance to navigate from a current state of the real robot within the real environment, causing simulated data to be obtained from one or more simulated sensors within a simulated environment at least partially modeling the real environment, the one or more simulated sensors including at least one simulated sensor different from the one or more real sensors, and using the simulated data to control operation of the real robot within the real environment in order to navigate the real robot from the current state.

The invention relates to the technical field of digital twinning and computer simulation, in particular to a power transmission and transformation project optical cable connection information operation and maintenance system based on a logic model, which comprises a model fusion module for constructing a digital twinning anchor point set by fusing a three-dimensional physical model and a secondary system logic model of a power transmission and transformation project; when the simulation analysis module receives a logic loop alarm, the physical path is reversely traced based on anchor point data, physical field simulation operation is executed, and a physical degradation risk point is output; the augmented reality module realizes spatial registration of a three-dimensional physical model and a live scene through a spatial computing technology, and visually presents logic loop and risk point information. According to the method, the space-time twin anchor points are constructed, the physical field simulation operation is driven, and the diagnosis result and the operation and maintenance instruction are superposed on the live scene through the augmented reality technology, so that the intelligent operation and maintenance closed loop of the optical cable connection information of the power transmission and transformation project is realized, and the troubleshooting efficiency and accuracy are improved.

The invention relates to the technical field of dam safety detection, and discloses a dam hidden danger troubleshooting method and system.The dam hidden danger troubleshooting method comprises the steps that a visual dam image collected in a multi-mode mode is obtained, a target detection model is used for processing the visual dam image, information of at least one bounding box with a hidden danger target is output, and then the bounding box is cut; the method comprises the steps of obtaining a local image where a hidden danger target is located, processing the local image by using a semantic segmentation model, outputting a pixel-level segmentation mask corresponding to the hidden danger target, and calculating quantitative geometric parameters of the hidden danger target based on the pixel-level segmentation mask, thereby providing objective data support for subsequent risk study and judgment of dam hidden dangers. According to the method, subjective judgment errors of manual detection are avoided, automatic dam hidden danger investigation is achieved, manpower and cost of manual inspection are greatly reduced, automatic collection, detection and quantitative analysis of dam images are achieved, dam hidden danger can be found and rectified in time, and the risk of dam safety accidents is reduced.

The invention discloses an electrical complete equipment state monitoring and early warning system based on multi-parameter intelligent sensing, belongs to the technical field of electrical equipment state monitoring, and can clearly point out a specific fault mode (such as contact resistance increasing overheating) and a possible position (such as an A-phase bus connection point). Through multi-parameter time sequence feature fusion and an intelligent diagnosis model, deep mining of a fault source is realized, the operation and maintenance efficiency is greatly improved, and the maintenance work is turned from blind troubleshooting to accurate disposal. Weak precursor signals of early and slow faults can be captured by extracting depth time sequence characteristics (such as temperature rise rate and dominant frequency offset rate) strongly related to a fault evolution mechanism. The AI model is utilized to learn complex modes of these precursor, and early warning can be given out before the equipment performance is obviously degraded, so that predictive maintenance of'nipping in advance 'is realized, and unplanned shutdown and major accidents are effectively avoided.

The embodiment of the invention provides a drive optimization method and system based on hardware state perception and a readable storage medium. The method comprises the steps that system data are collected, processed and stored, optimal adjustment instruction data are obtained according to standardized system data, verification processing is conducted through association mapping and execution adjustment, the program running state is monitored, and if the number of exceptions exceeds a preset threshold value, fault diagnosis is triggered, and a troubleshooting report is pushed. Performing interactive processing according to system standard parameters, updating bottom-layer codes and permission application logic and verification processes, updating mapping rules according to adjusted effect data, and optimizing a fault correlation analysis model; through hardware state perception and data acquisition, driving parameter dynamic matching and adjustment, fault intelligent diagnosis and report generation, cross-system version compatibility adaptation and data closed-loop optimization and rule iteration, balance between performance and resource consumption is realized, equipment operation stability and cruising ability are improved, and development and maintenance cost of a driving program is reduced.

The invention discloses a multi-agent dynamic diagnosis method and system for a vehicle-mounted software system, and relates to the technical field of software diagnosis. Collecting multi-source operation data of the vehicle-mounted software in real time; performing document analysis and skill processing on the internal technical document of the original equipment manufacturer to obtain an executable skill set; constructing a dynamic dependency graph based on the multi-source operation data and the executable skill set; obtaining a current fault prompt, calling a multi-skill collaborative agent, substituting multi-source operation data and the fault prompt into the dynamic dependency graph, arranging action layer operation in an executable skill set through the agent, executing log analysis, dependency chain verification, abnormal link identification and fault scene restoration, and outputting abnormal associated data; based on the dynamic dependency graph, the abnormal associated data, the rule layer information of the executable skill set and the historical fault cases, root cause reasoning is executed to obtain an executable troubleshooting scheme, and the fault diagnosis accuracy and efficiency of the vehicle-mounted software system are improved.

The invention relates to the technical field of the intelligent power grid industry, in particular to a power distribution and utilization ring network fault intelligent troubleshooting method and system. According to the method, a looped network situation primitive is generated through multi-source heterogeneous data fusion. The primitives are input into synchronous compression transformation to extract transient fault initial features, and the transient fault initial features are mapped into fault propagation path features through a graph convolutional network considering topological structure constraints; then, inputting the features into a space-time correlation prediction model, integrating defect type judgment and trend pre-judgment features, and generating a fault diagnosis evidence set; and starting an investigation mechanism based on the evidence set to determine an interval to be investigated, and performing closed-loop verification by constructing a fusion judgment matrix based on multi-modal physical sensing information to realize physical space locking of a fault defect point. According to the method, deep learning diagnosis and multi-mode positioning are integrated, and the detection precision and the positioning efficiency of looped network defects and the identification capability of transient hidden faults are effectively improved.

The invention relates to the technical field of electric energy data acquisition, in particular to an electric energy data acquisition system, device and method, and the system comprises a time calibration module which is used for transmitting a test frame carrying a transmission time mark to an electric energy meter according to a standard time reference signal, capturing an arrival time mark of a return frame of the electric energy meter, and transmitting the arrival time mark to a data acquisition module; and calculating a transmission loop delay value between the sending time mark and the arrival time mark. In the invention, sequencing and locking are carried out among multiple channels according to a communication quality blocking threshold, a high-reliability redundant link is constructed for data transmission, a data cutoff risk caused by single network fluctuation is avoided, hardware state parameters such as response frame return duration and central processing unit temperature are combined, a two-way interaction mechanism is established with a cloud server, and the reliability of data transmission is improved. And firmware updating is triggered according to the remote instruction, so that automatic operation and maintenance and fault early warning of the equipment in the full life cycle are realized, and the maintenance cost of on-site manual troubleshooting and the system downtime are reduced.

The invention relates to the technical field of computers, particularly provides a service building method based on a low-code-free platform, and aims at solving the technical problems that in an existing low-code platform, the coupling degree of a service process engine, a form engine and a rule engine is high, troubleshooting is difficult and the like. The method comprises the following steps: generating a unified metadata model through visual interface configuration, and defining a business process, a form, a business rule instance and an association relationship thereof in a structured mode; analyzing the model and converting the model into deployment configuration information which can be loaded by each engine according to a grammar mapping rule; a coordination rule for coordinating a multi-engine execution sequence and a data interface is automatically generated based on the incidence relation between the instances, and the coordination rule is integrated with deployment configuration information; and driving the corresponding engine to execute the service. According to the method, business and engine decoupling is realized through the unified metadata model, and pluggable replacement of the engine is supported; a collaboration rule is automatically generated through visualization, and the development threshold is reduced; and through unified time axis debugging, the operation and maintenance efficiency is improved.

The invention discloses a fault self-check recovery method and system based on a cloud deployment system, and the method comprises the steps: carrying out the multi-dimensional deep detection of the operation states of an operation system layer, a database layer and an application layer of a cloud platform through constructing an all-dimensional real-time monitoring system, and automatically triggering an adaptive recovery mechanism once an abnormality is found, and continuous and stable operation of the system is ensured. The matching tool integrates core modules of state acquisition, anomaly analysis, recovery execution, log recording and the like, collaboratively realizes real-time acquisition of operation data, accurate anomaly identification through an intelligent algorithm, rapid matching and implementation of a recovery scheme, and records logs in the whole process for troubleshooting and system optimization. According to the tool, server nodes, database states, service accessibility and webpage availability are checked in sequence, emergency processing is started immediately when abnormity is found, the stability and reliability of a cloud platform are remarkably improved, and the manual intervention cost is reduced.

The invention belongs to the technical field of rail traffic signals, particularly relates to a rail circuit interference test analysis method, system and equipment, and aims to solve the problems that a mixed interference source is difficult to accurately position, accidental faults cannot be captured and diagnosis is subjective. The method comprises the following steps: performing multi-channel synchronous acquisition and real-time storage on a voltage signal and a current signal of a track circuit; processing the original signal in real time, analyzing different frequency components through high-resolution spectrum analysis, and comparing with a preset threshold in combination with time domain analysis to trigger an alarm and generate a real-time analysis report; and performing intelligent analysis on historical data, performing verification and feature recognition on fault period data by establishing a probability model of a normal working condition, and generating a diagnosis analysis report. According to the method, the interference source can be accurately separated and positioned, the transient fault is automatically captured, objective and intelligent fault diagnosis is provided, and the efficiency and reliability of track circuit troubleshooting are remarkably improved.

The invention discloses a real-time fault prediction and self-healing system and method for server operation and maintenance. The system comprises a multi-source data acquisition module, a data preprocessing module, a fault prediction module, a fault positioning module, a self-healing execution module, a monitoring feedback module and a system control module. According to the method, potential faults are recognized in advance through multi-source data collaboration and a time sequence model, and passive response is avoided; positioning accuracy: based on a root cause contribution degree formula quantitative index influence, core root cause positioning efficiency is improved, and manual troubleshooting time is reduced; and dynamic self-healing: a rollback mechanism is supported in combination with a fault type and influence range matching strategy, and the risk of excessive repair is reduced.

One or more systems, devices, computer program products and / or computer-implemented methods of use provided herein relate to dynamic system state capture for troubleshooting of medical devices. Accordingly, a system can comprise a memory that can store computer executable components. The system can further comprise a processor that can execute at least one of the computer executable components that can collect, in response to a service request in connection with a system failure of a medical device and via an artificial intelligence model, troubleshooting data based on a time range or an error type of the system failure. In various aspects, at least one of the computer executable components can further generate a system state digest of the troubleshooting data. In various instances, the system can further dynamically generate an encryption of the system state digest.

The invention provides a linear tandem type on-line monitoring system and a troubleshooting method thereof, and the method comprises the steps: feeding back a self-checking state return instruction to superior equipment when ith sensor equipment receives a global self-checking instruction transmitted by superior equipment, and forwarding the global self-checking instruction to subordinate equipment at the same time, when a self-inspection state return instruction of the lower-level equipment is received, a self-inspection feedback success state instruction is returned to the lower-level equipment; and when the ith sensor equipment does not receive any one of the global self-checking instruction, the self-checking state return instruction and the self-checking feedback success state instruction in the global self-checking time period, the ith sensor equipment is preliminarily determined as suspected fault equipment. By issuing the global self-checking instruction in the global self-checking time period, the suspected fault equipment in the online monitoring system is preliminarily identified, so that a worker can conveniently carry out troubleshooting.

The invention relates to the field of chemical analysis automation equipment, and discloses an automatic potentiometric titration method which comprises the following steps: before a titration task is started, executing adaptive cleaning and collecting residual spectrum fingerprints of a system; calibrating a titration prediction model in real time by using the fingerprint; performing predictive titration to obtain complete titration data; after the titration is finished, carrying out multi-dimensional analysis on the whole process data, and quantitatively evaluating the pollution risk and the data quality; and finally, dynamically adjusting the cleaning strategy of the next task according to the evaluation result, and adaptively updating the prediction model according to the prediction deviation of the titration. By establishing a closed-loop intelligent working process of cleaning, diagnosis, feedback and learning, the state of the system can be sensed in real time, a cleaning strategy and a prediction model can be dynamically adjusted, the cleaning effect is ensured, self-optimization of the titration process is also realized, the long-term analysis accuracy and the intelligent level of the automatic titration system are improved, and the working efficiency is improved. And the dependence on manual intervention and troubleshooting is reduced.

The invention relates to the technical field of embedded systems, in particular to a secure access method and device for processor resources, equipment, a medium and a product, and the method comprises the steps: distributing each resource access request to a corresponding target virtual domain according to a matching result of a main equipment process identifier of each resource access request and a corresponding preset mask, injecting the distributed domain identifier into each request to obtain a domain marking request; intercepting and analyzing the domain marking requests to obtain a target address, an operation type and a domain identifier corresponding to each domain marking request; and verifying the operation type of the domain identifier corresponding to each domain marking request on the target address based on preset security policy information, and intercepting the abnormal access request. Therefore, the technical problems of large performance loss, insufficient security isolation granularity and lack of system-level protection in the existing intra-core resource isolation scheme are solved, the real-time performance and security of the system are remarkably improved, and the troubleshooting capability is enhanced.

The invention discloses a parcel clamping stagnation diagnosis and processing system and method based on a large-scale language model, and the system comprises an acquisition module, a multi-modal fusion layer, a prompt construction module, an LLM large-scale language model, a decision module, an execution module, a feedback collection module and a reinforcement learning module. The matching method comprises the steps of monitoring, sorting, parcel state diagnosis, automatic or human intervention execution of corresponding decisions according to diagnosis results, summarization and feedback, and enhancement of LLM large-scale language model learning. The parcel sorting condition is monitored in a data fusion mode, abnormal numerical values are responded and diagnosed, the model judges whether clamping stagnation occurs or not, measures such as confidence analysis, decision execution of distribution of affected target groups, manual troubleshooting and continuous monitoring are given, an operator is allowed to know diagnosis information and then conduct manual intervention, and the accuracy and the reliability of parcel sorting are improved. And the feedback is recorded in time for the model to update the strategy, the architecture is optimized, and the accuracy and response efficiency of subsequent diagnosis are improved.

The invention discloses a static detection method of an aero-engine pulsating pressure testing system and a related device, and belongs to the technical field of aero-engine pulsating pressure testing. The method comprises the following steps: carrying out integrity and electrical characteristic detection on a sensing unit; checking the operation state of the signal processing unit; a background signal is collected in a static environment, and whether the overall performance of the system meets a preset requirement is judged according to the background signal. The system comprises a sensing unit detection module, a signal processing unit detection module and a background signal detection module. By comprehensively detecting the sensor state, circuit connection and system background noise under the condition of no dynamic excitation, potential faults can be found in advance, parking troubleshooting caused by data abnormity in the test run process is avoided, and the stability and operation and maintenance efficiency of the test system are remarkably improved.

The invention relates to the technical field of driving and anchoring all-in-one machines for roadway driving of an underground coal mine driving working face, and aims to solve the problems that a complex and burdensome electric control system is formed by a plurality of electric elements and connecting lines, the intelligent automation degree between different control loops is low, and the normal work progress and economic benefits are influenced. According to the digging and anchoring all-in-one machine electric control system and method, a CAN bus serves as a communication mode, a main controller program is provided with an error detection mechanism, the working state of an element can be judged and a fault point can be determined rapidly according to read data of the element, the intelligence and automation of control over the digging and anchoring all-in-one machine are effectively improved, and the working efficiency of the digging and anchoring all-in-one machine is improved. A large amount of element arrangement space can be saved, the total wiring amount can be reduced, and the troubleshooting efficiency of a user can be effectively improved.

The invention provides an FC network switch simplified test method, which relates to the technical field of aviation systems, and comprises the following steps: S1, constructing a test environment, inserting a first FC switch and a second FC switch into a test tool, and interconnecting a plurality of ports of the first FC switch and the second FC switch; s2, controlling the first FC switch and the second FC switch to start and operate in a ground working mode through an external discrete magnitude, and setting domain IDs of the two switches to be 1; and S3, controlling the CPUs of the first FC switch and the second FC switch to inject an FC excitation source through software, so that the first FC switch and the second FC switch mutually send and receive FC test messages through interconnected ports to execute an automatic test. According to the method, the problem of test environment conflict frequently existing in actual scientific research test and experimental test is solved, the progress of production delivery, scientific research and troubleshooting is accelerated, and a reference basis is provided for development and test of similar products.

The application discloses an aviation graphite sealing assembly O-shaped sealing ring failure simulation device and method, and particularly relates to a method for simulating working environment including oil medium, working pressure, working temperature, working time and part deformation, clamping the O-shaped sealing ring by using the same O-shaped sealing ring clamp as the part structure, putting into a container filled with the same oil medium as the working time, heating according to certain conditions until the O-shaped sealing ring fails, and deducing the possible failure reason of the O-shaped sealing ring, which is suitable for aviation equipment graphite sealing assembly maintenance and fault elimination. The technical method and device overcome the shortcomings that the pressure of the oil medium is not considered and only the single axial deformation of the O-shaped sealing ring is considered in the commonly used GB / T 1690-2010 test method, and can more accurately simulate the O-shaped sealing ring failure critical temperature.

The application provides a monitoring method, device and equipment for a ufs test case and a storage medium, which are applied to a ufs verification platform, the ufs verification platform is connected to a to-be-tested device installed with ufs, the method comprises the following steps: the ufs verification platform generates at least one target instruction based on the ufs test case, sends all the target instructions to the to-be-tested device in sequence, and the to-be-tested device performs target testing based on the target instruction; the ufs verification platform writes all the target instructions and related information into a record file, wherein the related information comprises a logical unit number, a logical block address and a block number of the target instruction; the ufs verification platform generates an end signal and sends the end signal to the to-be-tested device and the record file respectively, the to-be-tested device terminates the target testing, the ufs test case is terminated, and the record file terminates writing. According to the technical scheme of the embodiment of the application, all the target instructions and related information can be recorded, so that the troubleshooting speed and efficiency of the ufs test result are improved.

The invention discloses a delay electric control system based on a multi-domain controller of a new energy commercial vehicle and a rapid diagnosis method of the delay electric control system in the field of new energy commercial vehicles, and aims to solve the technical problems that an existing relay is poor in control reliability and difficult in troubleshooting. The method comprises the steps that an ignition lock and a charging gun are used for providing starting or charging signals; a first high-side driving circuit is arranged in the vehicle body domain controller, and the vehicle body domain controller is used for outputting delay power; a second high-side driving circuit is arranged in the chassis domain controller, and the chassis domain controller is used for outputting delay power; the wake-up power supply end of the vehicle control unit is connected with the power supply output end of the vehicle body domain controller or the chassis domain controller; the power supply ends of the gateway and the vehicle-mounted remote information terminal are respectively connected with the output end of the first high-side driving circuit; and the power supply ends of the motor controller and the battery management system are respectively connected with the output end of the second high-side driving circuit. According to the invention, the reliability and the intelligent level of delay electric control can be improved, and rapid fault diagnosis is realized.

The application relates to a substrate management circuit, a method, a computer device and a mainboard control system. When the current power supply mode of the substrate management circuit is the mainboard power supply and the mainboard power supply is abnormal, the power supply management module connected with the mainboard power supply and the standby power supply is switched to the standby power supply. The control module connected with the mainboard, the mainboard power supply and the power supply management module generates a fault correction instruction according to the running state of the mainboard and / or the mainboard power supply when the power supply management module is switched to the standby power supply due to the abnormal mainboard power supply, so that the substrate management circuit is in a preset working state. The normal power supply of the substrate management circuit is realized when the mainboard power supply is abnormal, thereby guaranteeing the monitoring and troubleshooting of the running state of the mainboard and the mainboard power supply and improving the running stability and reliability of the mainboard and the mainboard power supply.

The invention relates to a parameter unified coding driven design task dynamic generation and execution method and system, and belongs to the technical field of engineering machinery product design digitization. A parameter list of a parameter editing interface is edited on an active template page, so that the design requirement of a current task can be met; dynamic configuration of the parameter editing interface is convenient and flexible; and then assignment calculation can be performed on the edited parameter list in combination with the latest business data on the activity task page, and the task information and the business data of each parameter in the parameter list are determined based on the pre-allocated coding information, so that the traceability and the accuracy are high; maintenance and troubleshooting are convenient, and data collaborative updating between upstream and downstream tasks can be automatically achieved.