1057 results about "Time stamping" patented technology

A method for packaging digital evidence for long term validation comprises forming a package of a digital document (10), an electronic signature (12) for the document (10), together with evidence (16) of the authority of the signature in the document and a time stamp (20) indicating when the document was digitally signed. All of the pieces form parts of the packaged evidence.

The invention discloses an engineering machinery fault prediction and intelligent maintenance method based on deep learning, and belongs to the technical field of intelligent operation and maintenance of engineering machinery. The method comprises the following steps: firstly, performing timestamp synchronization and feature enhancement on multi-source sensor data to generate a space-time alignment tensor; fusing the image and time sequence features through a multi-modal feature distillation network, and constructing a cross-modal unified feature vector; thirdly, calculating a fault probability and residual life distribution, and constructing a Markov decision model in combination with a resource state; and finally, dynamically optimizing the maintenance instruction by using deep reinforcement learning, and continuously updating the model through closed-loop feedback. According to the method, early-stage accurate prediction of the fault and dynamic optimization of the maintenance strategy are realized, the problems of inaccurate prediction, decision lag, resource waste and the like in a traditional method are effectively solved, and the availability rate and the maintenance economy of equipment are remarkably improved.

The invention provides a data traceability verification method in a trusted data space, and relates to the technical field of zero-knowledge certification, the method comprises the following steps: collecting a plurality of operation records of target data in the trusted data space, and arranging the operation records according to a timestamp sequence to generate an operation sequence; converting into a state transition constraint condition; inputting the operation sequence into a pre-trained key node identification model, and outputting position identifiers of one or more key state transition nodes in the operation sequence; constructing a zero-knowledge proof circuit according to the operation sequence, the state transition constraint condition and the key state transition node, and operating the zero-knowledge proof circuit to generate a composite zero-knowledge proof; and providing the composite zero-knowledge proof as a data traceability voucher of the target data to a verification party to complete traceability verification of the whole processing process of the target data. The technical problems of privacy disclosure risk and low verification efficiency of data traceability verification in the prior art are solved.

The invention relates to the technical field of communication data security, and discloses a mimicry encryption method and system for communication data transmission. The method comprises the following steps: synchronizing a high-precision timestamp through an NTP protocol and generating a standardized environment parameter; temperature, voltage and signal strength indexes are extracted based on the environmental parameters, entropy hybrid calculation and dynamic splicing coding are carried out, and a dynamic encryption key is generated; performing multi-layer mimicry permutation and diffusion confusion on plaintext data after the plaintext data is partitioned by using the key to generate a final ciphertext; performing protocol encapsulation and transmission optimization on the ciphertext; and the receiving end realizes decryption through de-encapsulation and inverse mimicry transformation, and dynamically adjusts the key in combination with environmental parameter feedback. According to the method, key dynamics is enhanced through environment randomness, multi-layer encryption and integrity verification are combined, the anti-attack ability and safety of data transmission are remarkably improved, and the method is suitable for communication scenes with high real-time performance requirements.

The invention belongs to the technical field of signal synchronization, and discloses a multi-camera anti-shake time sequence synchronization control system and method based on an FPGA. The method comprises the following steps: taking an FPGA as a core, generating an FSYNC signal as a camera exposure reference, and obtaining a delay value through a timestamp and differential measurement; acquiring I MU data and obtaining a global motion vector through a gradient descent algorithm; analyzing by combining the two to obtain phase deviation and feeding back the phase deviation to the FPGA, and adjusting the FSYNC signal by combining the global motion vector by the FPGA according to the phase deviation; according to the method, nanosecond timestamp recording is realized through a homologous clock reference and hardware-level signal binding mechanism, and the limitation of dependence on static time difference compensation in the prior art is broken through in combination with global motion vector analysis and dynamic phase offset adjustment.

The invention provides a circuit breaker action coil current characteristic diagnosis and analysis system and method. Belongs to the technical field of power system automation and substation state monitoring. The method comprises the following steps: carrying out synchronous wave recording on current of a plurality of circuit breaker actuating coils on a direct current feeder screen side to generate multi-channel synchronous wave recording data; meanwhile, SOE message information is collected, a circuit breaker with specific action is automatically matched and positioned by utilizing a timestamp and action identification information in an SOE message, and action circuit breaker positioning data is generated; the current waveform data of the circuit breaker actuating coil are collected in real time, and a high-precision algorithm is used for feature extraction and analysis, so that abnormal fluctuation in current change can be accurately captured, such as overlarge starting current and unstable holding current, the risk of misoperation or rejection of operation of the circuit breaker caused by abnormal current characteristics is reduced, and the service life of the circuit breaker is prolonged. Electric power system accidents caused by equipment faults are avoided, and the operation reliability of the circuit breaker is improved.

The invention relates to the technical field of language information processing, and provides a large language model-based structured information retrieval method, which comprises the following steps of: deploying an adapter, accessing operation and maintenance data, carrying out timestamp synchronization on an original entry and identifying a source, mapping equipment, parts and personnel based on an asset directory and recording mapping confidence, extracting structural elements from the records; injecting three types of metadata into candidate evidences, establishing corresponding nodes and edges in a graph database, analyzing query into a structured retrieval intention, expanding a candidate evidence set along three chains to form candidate sub-graphs, and calculating comprehensive scores for sorting; the candidate evidences should be verified and constrained, meanwhile, LLM is called for inference, feasibility inference confidence is output, actual measurement results, the inference confidence and source confidence are fused according to preset weights, feasibility scores of the candidate evidences are obtained, and for the candidate evidences passing feasibility verification, accurate anchor points are calibrated for each evidence along a source chain; and generating a structured answer.

The invention provides an intelligent factory manufacturing optimization method and system based on energy consumption prediction, and the method comprises the steps: carrying out the time sequence structural processing of historical energy consumption record data continuously collected in a factory manufacturing process, and generating an energy consumption sequence unit with a continuous time stamp through time window division and data correlation verification; constructing a manufacturing process-energy consumption association knowledge graph based on the energy consumption sequence unit, and establishing a structured knowledge representation containing node attributes and edge relationship weights by identifying a causal association relationship between process execution nodes and energy consumption fluctuation features; calling a pre-trained graph neural network model to carry out time sequence evolution prediction processing on the graph, and generating an energy consumption prediction sequence of a future preset manufacturing cycle; and according to the energy consumption prediction sequence, performing dynamic scheduling optimization on the execution sequence of the current manufacturing process and the resource allocation scheme, and generating an energy consumption optimization oriented manufacturing execution instruction. The energy consumption management level and the resource utilization efficiency in the manufacturing process of the intelligent factory are improved.

The invention discloses a rolling bearing lightweight fault diagnosis method and system based on multi-source signal fusion. Vibration, temperature and rotating speed signals are synchronously collected, and timestamps are calibrated; respectively carrying out denoising and normalization preprocessing; dividing and aligning windows; differential feature extraction: extracting time-frequency features of the vibration signals by using a one-dimensional residual CNN, and extracting abnormal measurement of the temperature / rotating speed signals by using an LSTM in combination with an isolated forest algorithm; carrying out self-adaptive weighted fusion on the features through an attention mechanism; the lightweight diagnosis model (through knowledge distillation, pruning and quantification) deduces and outputs the fault category, the health index and the confidence coefficient. The system correspondingly comprises an acquisition module, a preprocessing module, a feature extraction module, a fusion module and a diagnosis module. The method improves the early fault sensitivity, enhances the variable working condition robustness, supports the real-time deployment of edge equipment, and is suitable for the intelligent monitoring of industrial bearings.

The invention discloses an intelligent power grid load prediction and dynamic response coordinated scheduling method, and relates to the technical field of power system automation, and the method comprises the steps: accessing intelligent ammeters, distributed power controllers and other devices of Modbus, IEC61850 and DL / T645 protocols through a multi-protocol adaptive gateway, and achieving data standardization; time stamps are calibrated by means of Beidou time service and an IEEE1588PTP protocol, and it is ensured that multi-source data synchronization errors are controllable; deploying an edge computing node cluster, distributing high-priority tasks to low-load nodes through an edge coordinator in combination with a load fluctuation level and a greedy algorithm, and ensuring real-time processing efficiency; the edge nodes generate short-term load prediction, and the cloud platform outputs medium and long-term prediction based on a historical data training model; and finally, the coordinated scheduling decision module fuses the two types of prediction results and the real-time parameters of the power grid, and generates a dynamic instruction to control the output of the adjustable load and the distributed power supply.

The invention relates to the technical field of data management, and discloses a dynamic error management and tracing method for flow line production data, and the method comprises the following steps: collecting multi-source dynamic data in a flow line production process, including equipment sensor data, production parameter data and product quality data; performing real-time dynamic error detection processing based on the multi-source dynamic data, and generating dynamic error characteristic data through a sliding window algorithm and an abnormal value analysis technology; in the dynamic error management and tracing process of flow line production data, equipment sensor data, production parameter data and product quality data are acquired in real time through a multi-source dynamic data acquisition technology, and data integration is performed by using a timestamp alignment algorithm, so that the synchronism and integrity of error detection data are ensured; meanwhile, through real-time dynamic error detection processing, abnormal fluctuation in the production process can be recognized in time, and the real-time performance and reliability of error detection are guaranteed.

The invention belongs to the technical field of intelligent safety supervision, and particularly relates to a coal mine personnel safety behavior analysis method, which synchronously associates a management instruction, personnel behavior data, an equipment state and environment parameters according to timestamps through a management platform, obtains the current position of a personnel in real time, dynamically judges whether the personnel moves correctly or not, and improves the safety of the personnel. When a person approaches each maintenance position, automatically checking whether the maintenance position has omission or not, if the omission is found, generating a rechecking instruction in real time and pausing subsequent operation authorization, if the omission is not found, carrying out consistency checking on the operation sequence, the operation video content and the standard regulation, and if the checking does not reach the standard, carrying out rechecking on the operation video content and the standard regulation; if yes, second-level early warning is triggered immediately, a recheck mechanism is started, subsequent operation authorization is paused by dynamically monitoring operation compliance and combining authority control, personnel are prevented from entering a subsequent maintenance position with an illegal state, and the problem of multi-position continuous violation which can only be traced afterwards in an existing method is thoroughly solved.

The invention provides a touch screen coordinate data reporting method and device and a storage medium. According to the method, in the sliding operation scene, according to the timestamp of the data transmission signal sent by the target application, multiple pieces of coordinate data collected by the touch screen are screened, and the screened coordinate data are sent to the target application, so that the link that the target application carries out resampling on the multiple pieces of coordinate data is reduced, the performance of the electronic equipment is improved, and the user experience is improved. And the power consumption of the electronic equipment is reduced, so that the user experience is improved.

The invention discloses a real-time noise reduction method and system supporting Bluetooth audio interaction, and the method comprises the steps: collecting an environment audio signal and a Bluetooth interaction audio signal, and carrying out the timestamp alignment and spatial calibration of the environment audio signal and the Bluetooth interaction audio signal; an improved multivariate variational mode decomposition algorithm is adopted to decompose and extract noise features, the noise features are combined with user historical noise data, a dynamic noise model is constructed through a long-short-term memory network, parameters are updated in real time, Bluetooth interaction audio content features and a user behavior data recognition scene are analyzed, and a noise reduction strategy is determined. And according to the dynamic noise model and the scene result, reinforcing learning to optimize filter parameters, generating a directional anti-noise signal to carry out noise reduction on the Bluetooth interaction audio signal, collecting the noise reduction intensity manually adjusted by a user, calculating voice definition and total harmonic distortion, and carrying out noise reduction on the Bluetooth interaction audio signal. And inputting a feedback and evaluation result into the modeling and noise reduction steps, carrying out dynamic range adjustment, sound channel balance and power amplification on the audio after noise reduction, and outputting the audio through a loudspeaker.

The invention discloses an AI-based creative thinking auxiliary generation method and system, relates to the technical field of artificial intelligence, and solves the problem of inaccurate sound and picture matching in a traditional method by performing timestamp alignment and feature extraction on audio data and a visual image frame and calculating the correlation between the audio data and the visual image frame by using a cross-modal attention mechanism. According to the method, user eye movement track data is introduced, real attention points of a user are mapped into a visual sequence, and an optimized weight matrix is generated by constructing attention masks and fusing model attention weights, so that a generation result is more in line with perception key points of the user. And meanwhile, a feedback mechanism is established based on the synchronization error score, and when the sound and the picture are detected to be asynchronous, the visual frame timestamp can be dynamically adjusted, so that the self-adaptive correction of the content is realized. On the whole, the method has remarkable advantages in the aspects of improving modal alignment precision, enhancing user perception consistency and optimizing generation result naturalness.

The invention discloses a non-screen POS machine data acquisition method and system based on NFC and a WeChat applet. A POS machine simulates an NFC Type 4 label through a non-contact chip, performs SM2 signature and encryption on equipment data containing a timestamp and a random number by using a private key instantaneously reconstructed by a PUF, and performs Base64URL-CRC packaging, segmented NDEF writing and 30s TTL setting; the mobile terminal automatically pulls up the WeChat applet after triple verification of radio frequency field stability detection, URL white list and integrity fingerprint; the small program uploads parameters through an AES-128-GCM secondary encryption and a Reed-Solomon fragment tunnel; the background adopts a Redis idempotent lock, an HSM private key decryption, an SGX enclave one-time token display mechanism and other mechanisms to complete signature verification and decryption, and it is ensured that the device data are only briefly visible in the WeChat security sandbox. According to the invention, the problems of difficult operation and maintenance data acquisition, high cost and poor security of the non-screen POS machine are solved, and cross-platform, anti-replay and man-in-the-middle-resistant high-security data acquisition can be completed within 10 seconds.

The invention discloses a lightweight low-delay quantum key distribution method and system adaptive to an electric power scene, and belongs to the technical field of quantum communication and electric power communication crossing. The method comprises the steps cooperatively executed by a sending end and a receiving end: the sending end determines a basis vector based on a pre-associated quantum fingerprint sequence and a timestamp, sends a key through polarization-phase two-dimensional coding, generates a selection mark and executes privacy amplification; and a receiving end synchronizes a timestamp to determine a basis vector, compares the basis vector with the quantum fingerprint to complete authentication, decodes through an integrated module, performs collaborative error correction and executes privacy amplification. The system comprises functional modules of a sending end and a receiving end, is integrated on a miniaturized board card, and supports common-fiber transmission and protocol conversion. According to the method, the problems of high time delay, high deployment cost and poor adaptability of the traditional QKD are solved, the end-to-end time delay is reduced to 14 microseconds, the deployment cost is reduced by more than 60%, the requirements of high safety, low time delay and lightweight deployment of a power system are met, and the method is suitable for key services such as power dispatching and relay protection.

The invention relates to the technical field of intelligent control, and provides an efficient machine room equipment control decision method and system based on machine learning, and the method comprises the steps: obtaining a real-time operation data set of equipment operation information and environment monitoring information containing timestamp marks, carrying out the state feature extraction, and obtaining an equipment operation state feature and an environment correlation feature; equipment working modes, performance, environment temperature and humidity and airflow field conditions are reflected respectively; utilizing an equipment control strategy model to jointly analyze the features to generate control decision information which covers the target equipment identifier, the adjustment action type and the amplitude indication; according to the method, the equipment and environmental factors are integrated to optimize the control strategy, the operation stability and efficiency of the equipment can be improved, and the operation and maintenance cost can be reduced.

The invention relates to the technical field of industrial automation, and provides a master-slave distributed multi-machine asynchronous system sampling synchronization method and system, and the method comprises the steps: after a host device is powered on and started, a synchronous time synchronization message is sent to a slave device through a gigabit Ethernet optical port at the whole second moment; the slave devices are responsible for data sampling and receiving synchronous time synchronization messages from the host device, and the second starting time recorded by each slave device is synchronized with the second starting time of the host device after being subjected to delay compensation processing; the sampling CPU module of each slave device packs and frames the sampling data and the corresponding relative time mark to form a sampling data frame, and the sampling data frame is sent to the slave device of the previous node in real time through a gigabit Ethernet optical port; the CPU module of the host device receives the sampling data frames sent by the slave devices, and the sampling data of the slave devices are synchronously aligned through the relative time marks of the sampling data frames, so that the data sampling synchronization of the multi-machine asynchronous system is realized. The method is high in universality, and synchronization of sampling data of the slaves can be guaranteed.

The invention discloses an early warning method and device for multi-device time sequence data and electronic equipment. The method comprises the steps of determining a time sequence data stream; dividing the time sequence data stream according to the model identifier to obtain a plurality of sub-time sequence data streams, sliding on the sub-time sequence data streams by using the time windows according to a preset step length from the starting timestamps of the sub-time sequence data streams, and determining the time sequence data under the time window which slides each time as a target time sequence data stream; performing timestamp alignment and data filling on time sequence data in the target time sequence data stream to obtain an aligned target time sequence data stream, and predicting a time sequence data predicted value corresponding to the aligned target time sequence data stream in a future time period through a prediction model corresponding to the aligned target time sequence data stream; and determining early warning information corresponding to the target time sequence data stream according to the time sequence data predicted value. The technical problem that the accuracy of an early warning result is affected by data time sequence dislocation caused by inconsistent acquisition time of time sequence data between devices is solved.

The invention discloses a server hardware information processing method and device, and relates to the technical field of server hardware information processing, and the method comprises the steps: obtaining a dynamic change mode in a fault event mapping table, and carrying out the real-time monitoring of the numerical fluctuation and state conversion of hardware configuration parameters, if it is detected that the parameter value exceeds the normal range or the state is abnormally skipped, a deep analysis mechanism is triggered to carry out fine-grained information extraction on related hardware components, and specific fault hardware positions and configuration abnormity details are determined; constructing a multi-dimensional hardware health state evaluation matrix according to the configuration exception details and the historical fault modes, and judging the overall operation state and potential risk points of the current hardware system by comprehensively analyzing the hardware category, the model parameter, the timestamp information and the configuration change trend; and a complete hardware information analysis result supporting accurate fault positioning is obtained.

The invention relates to the technical field of office automation and business process intelligent supervision driven by big data, and discloses an office operation supervision method and system based on big data. The method comprises the following steps: firstly, extracting original logs containing an initiator, a receiver and timestamps from an approval system, checking one by one, de-duplicating and sorting according to time; counting flow times based on the ordered logs, and constructing an approval flow times matrix and a directed network; calculating the initiating and receiving quantity of each node, and screening actual participating nodes; calculating a flow imbalance index for each node, adaptively setting a threshold value according to a whole network average value and a standard deviation, and identifying abnormal nodes as bottleneck candidates; an adjacent matrix is constructed for the candidate nodes, connected clustering is carried out, and a structural bottleneck group is revealed; re-calculating the imbalance index according to an isochronous window in the whole monitoring period and counting the trend; and finally, combining the static unbalance degree and the dynamic change to generate a comprehensive score, and automatically outputting a structured monitoring report.

The invention discloses an interaction state backtracking and semantic reconstruction system based on a user operation log. The system comprises an operation log recording module which is used for capturing interactive operation of a user in an interface in real time and generating an event log with a timestamp; the state backtracking module is used for recovering the interactive semantic state of any time node according to the log; the semantic reconstruction module is used for allowing a user to derive a new interaction path on the basis of the backtracking state so as to realize branch evolution of a semantic structure; and the user control confirmation module is used for providing a confirmation mechanism in the state recovery and reconstruction process to ensure that final execution is triggered by a user. The technical problems that an existing system lacks historical operation tracking, and semantic rollback and reconstruction are difficult to carry out are solved.

The invention discloses a smooth processing method and system for flight path data of an aircraft, and the method comprises the steps: obtaining an original flight path data set, each flight path point in the original flight path data set comprising a timestamp, a flight number, latitude and longitude coordinates, height, speed and course angle attributes; grouping according to flight numbers and sorting according to timestamps to obtain a plurality of flight track sequences; the method comprises the following steps of: calculating the time interval of adjacent points for each flight track sequence, dividing the flight track sequence into a plurality of continuous track sections according to an interruption threshold, dynamically calculating an interpolation interval according to a timestamp difference value for each pair of adjacent starting point and ending point in each section, and ensuring that the time interval of any adjacent point in the continuous track sections does not exceed the interruption threshold. According to the method and the system, the problems of wrong interpolation, course angle sudden change, low interpolation interval fixing efficiency and unsmooth track caused by data interruption in the prior art are solved, and the continuity, the accuracy and the visual reality sense of the track data are improved.

The embodiment of the invention provides a real-time collection method and system for welding heat input, and the method comprises the steps: sensing the displacement movement of a welding gun or a welding wire through a diffuse reflection photoelectric switch array installed on the surface of a pipeline, and generating a corresponding pulse trigger signal; current data and voltage data of a welding loop are sensed through a Hall sensing system, and corresponding analog electric signals are generated; synchronously acquiring the pulse trigger signal and the analog electric signal through a multi-channel data acquisition device, and converting the analog electric signal into a digital signal with a timestamp; real-time welding speed data are calculated according to the time sequence of the pulse trigger signals and the preset space geometrical relation of the diffuse reflection optoelectronic switches; performing multi-stage filtering processing on the current data and the voltage data; a unified timestamp is determined, and time sequence matching is conducted on the filtered current data, the filtered voltage data and the filtered welding speed data based on the unified timestamp; and calculating to obtain a welding heat input value.

The invention discloses a UVM-based end-to-end random verification method for a complex bus interconnection bridge, and relates to the field of communication verification, and the method comprises the steps: building a verification environment based on a UVM, connecting a corresponding bus protocol master VIP containing a driver and a monitor to each slave interface of the bus interconnection bridge, connecting a corresponding bus protocol slave VIP containing a driver and a monitor to each master interface, and connecting the corresponding slave VIP containing a driver and a monitor to each slave interface of the bus interconnection bridge; a master VIP driver is used for sending read-write request excitation which carries the slave interface identity information and is carried by a sideband signal; a verification environment based on a UVM is established, excitation carrying slave identity information is sent through a VIP driver, data are packaged through a monitor and are distributed to a reference model and a scoreboard, meanwhile, the reference model is subjected to decoding routing according to a preset array, the scoreboard is matched with queue elements in sequence, and the functions of reserved queue grading processing, outsourcing characteristic directional coverage and timestamp calibration are further arranged. And multi-scene high-load simulation and error positioning are supported.

The invention discloses a distributed electric power measurement anomaly detection method and system based on a graph neural network. The method comprises the steps of collecting distributed data; processing the distributed data, and recording a timestamp; converting the nonlinear distributed data into linear data; constructing a graph model by taking each metering device as a node, taking the processed distributed data as a node initial feature and taking a physical connection or communication interaction relationship between the devices as an edge; constructing a graph neural network model; inputting the graph model into a graph neural network model, extracting node space features through a graph convolutional neural network layer, constraining edge weights through a graph attention network layer, and updating node features; and inputting the output of the graph neural network into K-Means clustering, dividing a center node and other nodes, calculating the distance between the center node and the other nodes by using Euclidean distance, setting a threshold value, and if the distance is greater than the threshold value, corresponding metering equipment is abnormal. The method can accurately distinguish the abnormal power fluctuation, shortens the response time, and reduces the omission ratio.

The invention relates to the technical field of numerical control machining, in particular to a numerical control tool wear online monitoring method and system based on multi-sensor fusion, and solves the problems that the monitoring precision is reduced and the false alarm rate is high due to working condition parameter drift. The technical bottlenecks that the discrimination adaptability of a fixed threshold value is insufficient and an offline training model cannot be continuously optimized are overcome. According to the method, the tool wear identification accuracy is effectively improved, the false alarm occurrence frequency is reduced, the residual life prediction reliability is improved, the intelligent monitoring level and the production stability in the numerical control machining process are improved, a reliable technical means is provided for precise management and preventive maintenance of the tool in the whole life cycle, and the method is suitable for popularization and application. Through a dual synchronization mechanism of unifying timestamps of a hardware clock and triggering angles of a main shaft encoder, accurate alignment of multi-sensor data with different sampling frequencies in a time axis and an angle domain is ensured.

The invention discloses a metering inspection platform and method, and the method comprises the following steps: carrying out the extraction of communication parameters and function information of metering equipment, building a unified data model, distributing a unique identifier, and achieving the automatic access of different types of equipment. The platform automatically generates an adaptive inspection flow path according to equipment functions and inspection requirements, can temporarily cache and collect data when a network is abnormal in a flow execution process, and adds a timestamp and verification information to the data. And after the network is recovered, the system compares and verifies the cached data, updates the process state after ensuring the data consistency, records and archives the effective data, and generates a traceable record entry. According to the invention, the access flexibility of the metering equipment is improved, and automatic construction of a process and reliable transmission and storage of data are supported.

The invention discloses an equipment logic interlocking control method supporting edge collaboration and an intelligent gateway, and belongs to the field of industrial control and industrial Internet of Things. Reading a configuration center rule and generating a verification entry list; reading a local key value cache, sending a remote state query request and analyzing a state response message; obtaining an interlocking judgment result based on aging screening, numerical value comparison and Boolean operation of the collection timestamps; and executing physical bus message issuing or failure response output according to an interlocking judgment result. According to the method, a data access mode of combining configuration center rule management and local cache is adopted, so that the state of the dependent object can be uniformly verified, and the interlocking judgment traceability is improved.