10results about How to "Improve traceability efficiency" patented technology

The invention belongs to a sewage monitoring technology, and particularly provides a multi-source risk aggregation tracing method and system for sewage poison monitoring, and the tracing method comprises the steps: A1, collecting the current monitoring data of sewage at each point (i, j), the data comprising a poison content detection value D (i, j), time and a place; a2, obtaining a traceability matrix M (i, j) according to the detection value matrix Dk (i, j) of each point location in N rounds; a3, a point location with a value greater than 0 in the matrix M (i, j) is a traceable point location; and A4, visually displaying the point location of the traceability and the mark of the M value corresponding to the point location. The method has the advantages of accurate traceability and the like.

This invention relates to the field of power quality monitoring technology and provides a power quality disturbance tracing method based on a workflow platform using an intelligent diagnostic model. The method includes: collecting three-phase voltage and current waveform data from each monitoring node; when a power quality indicator exceeds a preset threshold, the workflow platform performs feature extraction to obtain a disturbance feature vector; performs disturbance pattern discrimination and selects the corresponding tracing sub-algorithm branch based on the discrimination result to obtain branch tracing results; performs hierarchical event mapping to aggregate abnormal indicators of each monitoring node into disturbance events according to spatiotemporal clustering and constructs a directed acyclic graph-like disturbance propagation chain based on the branch tracing results; inputs the disturbance feature vector and disturbance propagation chain into a trained target hybrid model; outputs the corrected disturbance propagation chain and root cause node; and finally outputs the tracing results. This invention effectively improves the tracing accuracy and efficiency in complex disturbance scenarios.

The application discloses a cross-industry supply chain supervision method based on multi-chain cooperation, a business sub-chain is used for recording internal information of enterprise transaction subjects and business data of various supply processes; a transaction main chain is mainly responsible for transaction implementation and interaction between different businesses, and records circulation states and transaction data of commodities at various links of the supply chain; and a supervision chain is used for supervising and auditing transaction data between different businesses of the supply chain. The application combines an identity-based encryption mechanism based on fine-grained privacy protection, protects the privacy of multi-party cooperative transaction data in the supply chain process, and solves the contradiction between the privacy of supply chain transaction data and the implementation of supervision. The application designs two verification methods of transaction types based on an aggregated signature protocol, so as to realize aggregated verification of on-chain transactions and rapid tracking of abnormal transactions, and effectively improves the performance of system supervision and traceability.

This invention discloses an event aggregation and attribution method based on attack chain homology analysis, comprising the following steps: Step 1, multi-source heterogeneous data fusion and standardization processing; Step 2, dynamic subject-object relationship modeling; Step 3, attack event homology analysis; Step 4, homology attack event aggregation; Step 5, attack path deduction and visualization; Step 6, dynamic threat adaptive optimization. This invention improves the completeness of attack path reconstruction and solves the defect of static modeling in failing to capture attack chain evolution by using a dynamic subject-object relationship model and homology analysis algorithm; it presents the attack process through visualization technology, shortening the analysis time and improving the efficiency of source tracing; it improves the robustness of homology determination and the accuracy of entity relationship identification by using a multi-feature weighted fusion hierarchical clustering mechanism combined with code string features and vulnerability exploitation methods for auxiliary verification; and it enhances the adaptability of dynamic threats and shortens the response window for new threats through a dynamic weight update mechanism.

The present application relates to the technical field of game data processing, in particular to a background game log information tracing method and system and a medium. The method comprises: obtaining the problem category of the feedback opinions of game users and corresponding log data; obtaining the data skew jam degree of each log source set based on the difference in data occupancy of different log source shards of each log source set; determining the jump delay burden factor of each log information path according to the interaction of the service area in each log information path in each log source set and the data skew jam degree; obtaining the shard delay adjustment parameter of each log source shard according to the jump delay burden factor of the log information path of different game users involved in the log source shard of each log source set, re-dividing the memory of the log source shard, and then obtaining the source path of each problem category. The present application improves the tracing efficiency of game log information.

The embodiment of the invention provides an abnormal data processing method and device, equipment, a storage medium and a program product, and relates to the field of financial science and technology. The method comprises the steps of obtaining a to-be-processed data table; extracting a blood relationship corresponding to the to-be-processed data table according to a to-be-executed script corresponding to the to-be-processed data table; according to the blood relationship, constructing a blood relationship topology network through a graph neural network in combination with a preset verification rule; and when it is detected that the abnormal data exists in the to-be-processed data table, determining a source node corresponding to the abnormal data by using the blood relationship topology network, and repairing the abnormal data by using the preset knowledge base. According to the method provided by the invention, through combination of data blood relationship modeling and graph neural network dynamic analysis, the efficiency and accuracy of financial data governance are remarkably improved; the range of occurrence of the abnormal data can be quickly delineated, the time of tracing the abnormal data is greatly shortened, and the efficiency of tracing the abnormal data is greatly improved.

This invention relates to the field of blockchain technology and provides a method, apparatus, electronic device, and storage medium for tracing electricity consumption data. The implementation scheme is as follows: A preset electricity traceability model is invoked to standardize and model the electricity consumption data of each user, obtaining various triplet data sets, which are then stored on the blockchain; a composite index structure is constructed based on the first field of each triplet data set, wherein the composite index structure includes a traceability positioning table and a multi-bucket index structure; responding to a query request from a target user, cross-block positioning is performed based on the second field in the query request and the traceability positioning table to obtain a cross-block traceability link; according to the second field and the multi-bucket index structure, intra-block data extraction is performed on each block in the cross-block traceability link to obtain the target traceability result; the target traceability result is then sent to the target user. This invention can improve traceability efficiency.

The application discloses a kind of based on instruction domain-vibration mapping numerical control processing quality determination method, comprising: obtaining the multi-source data in standard numerical control processing process;Multiple-channel vibration signals in the multi-source data in multiple spatial position units and execution numerical control program line are combined as instruction domain-vibration mapping unit;The instruction domain-vibration mapping unit in the spatial position unit is carried out multi-feature extraction, and the feature is combined to form standard feature vector;Multi-source data in real-time numerical control processing process are acquired, and multi-feature extraction is carried out to obtain real-time feature vector;The real-time feature vector is compared with the standard feature vector, and the numerical control processing abnormal degree of real-time numerical control processing process is determined.The application establishes the corresponding relationship of vibration signal and program line number by constructing instruction domain-vibration mapping unit, and realizes accurate determination and accurate tracing of numerical control processing abnormal degree by combining multi-dimensional feature vector comparison.

This invention discloses a method for assessing enterprise ESG environmental indicators based on IoT data, belonging to the field of intelligent industrial environmental management technology. It addresses the technical problems of ambiguous correlations between production factors and environmental indicators, lack of scientific basis for anomaly judgment, and low efficiency in source tracing and handling in existing technologies. The method establishes a ledger for the coding of equipment, processes, and ESG environmental indicators throughout the entire production process; collects IoT data to filter core correlation groups; builds a three-level correlation mapping table and establishes a dynamic update and traceability mechanism; establishes a threshold ledger for different operating conditions; constructs an abnormal feature library of enterprise ESG environmental indicators; and uses threshold comparison and cosine similarity methods to determine true anomalies; generates a source tracing list based on the three-level correlation mapping table; filters abnormal elements hierarchically; and integrates information to form an anomaly root cause location report. This invention achieves standardized and quantitative binding of production factors and ESG indicators, improves the rationality and accuracy of anomaly judgment, and effectively enhances the efficiency and scientific basis of enterprise ESG environmental indicator assessment and anomaly handling.

This invention discloses a collaborative intelligent water quality monitoring method using a dual-platform approach combining a drone and a biomimetic fish. It includes the following steps: The drone subsystem transmits the location and extent information of the initially screened contaminated area, along with spectral data, back to an intelligent remote monitoring platform in real time; a biomimetic fish precision detection task is generated, and the coordinates of the initially screened contaminated area and a preset monitoring path are sent to the biomimetic fish subsystem; the biomimetic fish subsystem collects measured data from the initially screened contaminated area and transmits the collected data back to the intelligent remote monitoring platform; based on the spectral data from the drone and the measured data from the biomimetic fish, data fusion is performed using spatiotemporal registration to obtain the fused contamination concentration inside and outside the contaminated area; an early warning level is automatically triggered according to the water quality anomaly level, and the coordinates of the contaminated area are pushed to the operation and maintenance terminal, completing the early warning response closed loop. This invention not only enables rapid response to pollution events and improves source tracing efficiency but also shortens the monitoring cycle.