53 results about "Sequence pattern" patented technology

The invention discloses a commodity recommendation method and system based on target sequence pattern mining, and belongs to the technical field of data mining. According to the method, a user shopping behavior sequence is coded to obtain a coded shopping sequence, and sequence candidate modes with the length of 2m + 1 are mined from the coded shopping sequence; deleting the modes which do not contain the target query sequence, taking the remaining modes as target sequence modes, and calculating the support degree of the target sequence modes; in the process of mining a sequence candidate mode with the length of 2m + 1 for a coding shopping sequence, when a target query sequence is a negative target query sequence, matching between a current prefix sequence and a precursor positive partner of a negative item and between the current prefix sequence and a subsequent positive partner of the negative item is realized under the guidance of the negative target query sequence. The method is short in time consumption for mining the target sequence pattern.

The invention belongs to the technical field of knowledge maps, and discloses a next interest point recommendation method based on a cross-regional city space knowledge map. According to the method, on the basis of the geographic space knowledge graph and the user preference knowledge graph, a cross-regional relationship modeling mechanism is introduced, the spatial proximity and the reachability between the regions are comprehensively considered, and more comprehensive description of the cross-regional behavior of the user is realized. According to the method, a recommendation framework combining a geographic module and a sequence module is designed, spatial dependence between interest points can be captured, a high-order sequence mode in user sign-in behaviors can be mined, and recommendation accuracy and diversity are improved. Geographic representation and sequence representation are fused through a consistency learning framework, the robustness and generalization ability of the model can be enhanced, and the model can still keep stable performance in sparse data and cold start scenes. According to the method, the defects of insufficient region boundary perception, recommendation result centralization, poor cross-region prediction adaptability and the like of an existing method are overcome.

The invention belongs to the technical field of Internet of Things. The invention provides a dynamic graph neural network link prediction method fusing multi-modal motifs. According to the embodiment of the invention, multi-source heterogeneous information such as structures, semantics, behaviors and the like is adaptively fused through a node-level modal attention mechanism, the problem that single-modal information is insufficient or unreliable is solved, and the discrimination ability of node representation is enhanced. And first-order adjacency, high-order adjacency and multiple motif modes are systematically fused, so that the model can simultaneously describe a local microcosmic mode and a global macroscopic topology, and the understanding of a complex network structure is more comprehensive. Spatio-temporal joint modeling is coordinated with time sequence attention through graph convolution, short-term mutation and long-term trend of network evolution are captured at the same time, and a dynamic link rule is accurately described. Through modal attention redistribution, time sequence consistency constraint and entropy regularization, modal missing and noise interference are effectively resisted, and prediction fluctuation is reduced.

The invention provides a vehicle fault early warning method, electronic equipment and a storage medium, and relates to the technical field of vehicle fault diagnosis and prediction. The method comprises the following steps: acquiring multi-source time sequence historical data in a vehicle running process within a first preset time period; discretizing the multi-source time sequence historical data based on the first preset time period to obtain a historical event sequence corresponding to each first preset time period; processing the historical event sequence by adopting a preset sequence pattern mining algorithm to obtain an event sequence pattern; determining a corresponding prediction rule based on the event sequence mode; performing monitoring matching on the multi-source time sequence real-time data and the prediction rule in the vehicle running process; and when the prediction rule is successfully matched with the multi-source time sequence real-time data, triggering target fault early warning of the vehicle. Through multi-source time sequence data mining and real-time matching, the limitation of isolated recording DTC is broken, early warning is triggered when matching succeeds, the passive situation of recording after a fault is thoroughly reversed, and beforehand early warning is achieved.

The embodiment of the invention discloses an AI failure mode dynamic diagnosis method and system based on a sequence mode and function data analysis, which realize the crossing of AI interaction failure from static type identification to dynamic process diagnosis and improve the depth and timeliness of failure analysis. Accurate and explainable data support is provided for optimization of an artificial intelligence system. Through a sequence pattern mining and function data analysis technology, a failure pattern generated in an interaction process of a user and an artificial intelligence model (such as a large language model, a generative AI or an AI intelligent agent) is identified, analyzed and dynamically tracked, and the influence of the failure pattern is analyzed.

The invention discloses a water conservancy construction risk assessment method based on big data analysis, relates to the technical field of water conservancy construction risk assessment, and is used for solving the problem of inaccurate risk identification early warning. According to the method, a construction task unit is taken as an object, time alignment and space mapping are carried out on monitoring, environment, progress, equipment, safety and other multi-source data, risk state vectors organized according to tasks and time windows are constructed, and a risk scene library is formed through clustering and sequence pattern mining; based on the risk scene type, the current risk state and the disposal strategy combination, a disposal strategy effect prediction model is trained, risk falling, construction period influence and resource occupation are jointly predicted, a state vector is circularly generated in construction operation, the risk scene is identified, and different disposal strategy effects are evaluated. And meanwhile, a risk scene library and a prediction model are updated by utilizing disposal result feedback, so that fine identification and dynamic management and control of complex coupling risks are realized, and the reliability of water conservancy construction risk early warning and scheduling decision making is improved.

The application discloses a learning strategy evaluation and guidance method based on experimental operation sequence pattern mining. The method collects original operation logs and reconstructs them into normalized operation event sequences; extracts the silent interval features between adjacent operations, analyzes the cognitive state labels corresponding to the silent interval in combination with the operation context, embeds them into the sequence to generate semantic enhanced operation sequences; then identifies the repeated operation fragments in the sequence, calculates the exploration intention features to distinguish the scientific exploration intention from the random trial intention, extracts the strategy feature vector of the student in combination with the typical strategy mode library; further locates the key decision branch point, extracts the decision micro-behavior features to calculate the decision hesitation index, and evaluates the strategy efficiency; and plans the gradual transition path from the current strategy to the target strategy in combination with the real-time cognitive load level, and generates personalized guidance suggestions. The application can realize deep quantitative analysis and accurate guidance on the cognitive state, operation intention and decision psychology of students.

The application relates to the technical field of computer networks and discloses a TCP service behavior portrait method and system based on network flow metadata, which comprises the following steps: decoding captured TCP network flow and extracting distributed correlation identifiers to form application layer interaction records; converting the interaction records into unified standardized operation logs; organizing the logs into ordered behavior chains based on time windows; further analyzing the logs and the behavior chains, quantifying atomic behaviors by aggregation, and discovering frequent service process modes by sequence pattern mining; attributing the analysis results to specific service entities to form entity portraits containing service function portraits and service process atlases; and finally constructing a global distributed service atlas representing the calling relationship between services by using the distributed correlation identifiers. The dependence strength between services is accurately quantified, and data decision-making basis is provided for system operation and maintenance and architecture optimization.

The application relates to a method and system for mining and analyzing the correlation of clinical comorbidity of discharged patients. The method comprises the following steps: constructing an individual diagnosis and treatment narrative graph for each patient according to the discharge medical record text, generating a time sequence transaction sequence corresponding to each patient, and constructing a sequence database; based on the database, the original time sequence frequent pattern set is constructed by analyzing through an improved generalized sequence pattern algorithm; each pattern in the set is classified to obtain multiple classification clusters, and the original time sequence frequent pattern in each classification cluster is processed through multi-sequence alignment to construct a generalized clinical path graph, and the information in the generalized clinical path graph is extracted to generate a natural language abstract. The method improves the time sequence logic, knowledge abstraction degree and clinical interpretability of the clinical comorbidity correlation mining by constructing a diagnosis and treatment narrative graph, mining a time sequence frequent pattern, and constructing a generalized clinical path graph through clustering and multi-sequence alignment.

The invention discloses a link log sampling method and device, equipment and a medium. The method comprises the steps of obtaining a target log set, wherein a target log comprises at least one burying point and original business data; processing each target log according to each burying point in the target log to obtain a target sequence set, the target sequence set comprising at least one target sequence and a sequence type matched with the target sequence; processing each target sequence in the target sequence set based on a pre-trained sequence pattern mining algorithm to obtain at least one service sequence type; and obtaining a sampling log set, and performing screening operation on each sampling log in the sampling log set according to each service sequence type to obtain a result log set. Through the technical scheme of the invention, sampling of the link logs can be realized, the efficiency of log acquisition work is improved, and the comprehensiveness, accuracy and reliability of an acquisition result are improved at the same time.

The invention discloses a firewall communication log anomaly detection method based on association rule and sequence pattern fusion, which comprises the following steps: 1, acquiring and cleaning formatted firewall historical logs, and extracting a source IP, a destination IP, a port, a protocol and an action field; 2, mining a static association rule by adopting an Apriori algorithm; 3, mining a sequential communication mode by adopting an improved PrefixSpan algorithm, and fusing the sequential communication mode with a static rule to construct a comprehensive behavior model; and 4, matching a real-time communication log with the model, and if the real-time communication log meets the front part but does not meet the back part or deviates from the sequence mode, judging that the real-time communication log is abnormal and triggering alarm and visual display. According to the method, the normal communication behavior model can be constructed by using the existing firewall log on the premise that the existing production network architecture is not changed, so that the abnormal communication can be quickly detected and alarmed, and the safety and reliability of the production network can be improved.

This invention proposes a method for visualizing criminal behavior sequences in drug-related cases. First, pre-trained word vectors are generated using the case details of the drug-related case. A tree structure of behavioral words is constructed based on the similarity of word vectors at sequence nodes. Then, nodes in the criminal behavior sequence are merged according to the tree structure of the behavioral words. Sequence patterns are mined, and all sequences are clustered based on these patterns. The sequences are represented as sequence patterns and sequence complements. Finally, the focal points in the sequence patterns are extracted for focused visualization. This invention provides assistance for case analysis in the judicial field, offers reference for judicial personnel's decision-making, and promotes the development of visualization analysis in the judicial field.

The invention belongs to the technical field of financial risk control, and particularly relates to a credit user intelligent identification method and system fusing multi-dimensional dynamic behavior characteristics, and the method comprises the steps: firstly integrating multi-source heterogeneous data, and generating a loan application main body characteristic view through preprocessing and correlation analysis; through a dynamic time sequence model, dynamic risk features are extracted based on a time decay function, time window statistics and sequence pattern mining, and the dynamic risk features and the static features are spliced into a high-dimensional vector; outputting a risk prediction value and a quantitative score by using double modules of an improved GBDT model and an expert rule score card; and finally, determining a final risk level through consistency check and a weight voting mechanism. According to the method, the problem that single-dimension or static data analysis has relatively large limitation in the existing credit risk control technology can be solved.

The invention discloses an infrared alarm system false alarm suppression system based on a big data mining algorithm, and the system achieves the effective suppression of a false alarm signal through the structural processing and correlation analysis of an alarm event generated by infrared detection. Infrared trigger signals are collected, a time-ordered alarm event sequence is formed, thermal disturbance identification, signal fluctuation suppression and credibility labeling are carried out on events, and a weighted event sequence is obtained; on the basis, performing sequence pattern mining on the weighted event sequence, extracting an event combination relationship with statistical significance, further guiding a rule generation process, and constructing a stable event structure rule; and performing validity evaluation on the generated rules, performing screening to form a reliable rule set, and outputting an alarm triggering, rechecking or suppression control signal in combination with a matching result of a current alarm event, a mode and a rule so as to realize intelligent judgment and control of infrared alarm false alarm.

An authentication device comprising: a storage unit for storing shared information including password and shared rule that are preset for each user; a random number sequence generation unit for generating a random number sequence pattern; a calculation unit for generating a key code based on the shared rules, using provisional numerals selected from the random number sequence pattern and secret numerals constituting passwords; a transceiver for receiving identification information of a user to be authenticated from an input terminal, transmitting the random number sequence pattern in association with the identification information to the input terminal, and receiving, from the input terminal, a key code consisting of the numerals selected based on the shared rules from the random number sequence pattern by the user to be authenticated; and an authentication unit for authenticating the user to be authenticated, using the key code received and the key code generated.

The invention discloses a method and a system for constructing a multi-channel interactive behavior sequence pattern knowledge graph. The method comprises the following steps: 1) preprocessing a multi-channel interaction log to obtain a generalization representation of each log record in the multi-channel interaction log; 2) mining a multi-channel interaction behavior sequence pattern with gap constraint from the multi-channel interaction log; 3) aiming at a multi-channel interaction behavior sequence mode, calculating a standardization support degree and a user forward uniqueness; and 4) taking the multi-channel interaction behavior category as an entity, constructing a quintuple set based on the multi-channel interaction behavior sequence pattern, and constructing a multi-channel interaction behavior sequence pattern knowledge graph for all users and a multi-channel interaction behavior sequence pattern knowledge graph for a target user. According to the method, structured knowledge representation covering general rules of all users and personalized preferences of specific users can be formed, and support is provided for a user experience optimization strategy in a multi-channel interaction scene.

1. The name of the design product: the setting pattern color sequence pattern user interface of electronic equipment. 2. The use of the design product: an electronic device. 3. The design points of the design product: in the pattern user interface. 4. The picture or photo that best indicates the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the pattern user interface: mainly used for setting pattern color sequence in textile machine production. 7. The man-machine interaction mode of the graphical user interface: in the design 1 main view, select "Embroidery Machine Management" in the upper right corner, click "Pattern List" in "Product Management" on the left side, enter design 1 change state diagram 1, click "Color Sequence Setting" of any pattern file card in the right side list in design 1 change state diagram 1, enter design 1 change state diagram 2 color sequence editing interface, the left side of the interface is a pattern preview, the numbered blocks in the upper right half of the right side are needle bars, and the lower half of the right side is a color palette and a color sequence editing button. After selecting any needle bar in the upper right corner of the interface, click "Plant Embroidery" below to enter design 1 change state diagram 3; in the design 2 main view, select "Embroidery Machine Management" in the upper right corner, click "Pattern List" in "Product Management" on the left side, enter design 2 change state diagram 1, click "Color Sequence Setting" of any pattern file card in the right side list in design 2 change state diagram 1, enter design 2 change state diagram 2 color sequence editing interface, the left side of the interface is a pattern preview, the numbered blocks in the upper right half of the right side are needle bars, and the lower half of the right side is a color palette and a color sequence editing button. After selecting any needle bar in the upper right corner of the interface, click "Combined Embroidery" below to enter design 2 change state diagram 3; in the design 3 main view, select "Embroidery Machine Management" in the upper right corner, click "Pattern List" in "Product Management" on the left side, enter design 3 change state diagram 1, click "Color Sequence Setting" of any pattern file card in the right side list in design 3 change state diagram 1, enter design 3 change state diagram 2 color sequence editing interface, the left side of the interface is a pattern preview, the numbered blocks in the upper right half of the right side are needle bars, and the lower half of the right side is a color palette and a color sequence editing button. After selecting any needle bar in the upper right corner of the interface, click "Color Sequence Zoom" below to enter design 3 change state diagram 3; in the design 4 main view, select "Embroidery Machine Management" in the upper right corner, click "Pattern List" in "Product Management" on the left side, enter design 4 change state diagram 1, click "Color Sequence Setting" of any pattern file card in the right side list in design 4 change state diagram 1, enter design 4 change state diagram 2 color sequence editing interface, the left side of the interface is a pattern preview, the numbered blocks in the upper right half of the right side are needle bars, and the lower half of the right side is a color palette and a color sequence editing button. After selecting any needle bar in the upper right corner of the interface, click "Get Machine Settings" below to enter design 4 change state diagram 3; in the design 5 main view, select "Embroidery Machine Management" in the upper right corner, click "Pattern List" in "Product Management" on the left side, enter design 5 change state diagram 1, click "Color Sequence Setting" of any pattern file card in the right side list in design 5 change state diagram 1, enter design 5 change state diagram 2 color sequence editing interface, the left side of the interface is a pattern preview, the numbered blocks in the upper right half of the right side are needle bars, and the lower half of the right side is a color palette and a color sequence editing button. After selecting any needle bar in the upper right corner of the interface, click "Edit Color Palette" below to enter design 5 change state diagram 3. 8. Other situations that need to be explained Other explanations: the gray coated area in each view is the content picture.

The invention discloses a learning strategy evaluation and guidance method based on experimental operation sequence pattern mining. The method comprises the steps of collecting an original operation log and reconstructing the original operation log into a standardized operation event sequence; extracting silence interval characteristics between adjacent operations, analyzing cognitive state tags corresponding to silence intervals in combination with an operation context, and embedding the cognitive state tags into a sequence to generate a semantic enhanced operation sequence; then identifying repeated operation fragments in the sequence, calculating exploration intention features to distinguish scientific exploration and random disordered trial intentions, and extracting strategy feature vectors of students in combination with a typical strategy pattern library; key decision branch points are further positioned, decision micro-behavior features are extracted to calculate decision hesitance indexes, and strategy efficiency is evaluated; and a progressive transition path from the current strategy to the target strategy is planned in combination with the real-time cognitive load level, and personalized guidance suggestions are generated. According to the invention, deep quantitative analysis and accurate guidance of the cognitive state, the operation intention and the decision psychology of the student can be realized.

The invention discloses a power grid security alarm correlation analysis method based on fuzzy sequence mining, and belongs to the technical field of power grid alarm. The method is realized through six steps of data access and preprocessing, alarm semantics and structuring, alarm attribute fuzzification, fuzzy sequence pattern mining, association analysis and root cause inference, and situation assessment and alarm output. According to the method, the alarm attributes are fuzzified, a time-ordered fuzzy alarm sequence is constructed, multi-dimensional information such as event level, frequency, duration, disturbance degree and electrical distance is effectively reserved, and the expression ability and robustness of complex events are enhanced. Weighted fuzzy support and confidence are introduced, alarm risk levels, causal roles and time effects are comprehensively considered, accurate recognition of key modes is realized, and false association is reduced. And an alarm relation network is constructed by integrating the electrical distance, the topological structure and the power flow direction, so that causal inference between alarms better conforms to the physical reality of the power grid, and the accuracy of root cause identification is improved.

The application discloses a commodity real-time recommendation method and system based on high-efficiency repeated negative sequence pattern mining, and relates to the technical field of data mining.The application designs a prefix tree structure for storing and maintaining high-efficiency repeated positive sequence patterns, and realizes efficient and dynamic management of data; and the application further proposes a new high-efficiency repeated negative sequence candidate pattern utility value calculation mode, specifically: for any high-efficiency repeated negative sequence candidate pattern, the utility values of the high-efficiency repeated positive sequence patterns matched with the high-efficiency repeated negative sequence candidate pattern are added to obtain the utility value of the high-efficiency repeated negative sequence candidate pattern.The application can effectively improve the accuracy of high-efficiency repeated negative sequence pattern mining, and the commodities recommended based on the more accurate high-efficiency repeated negative sequence patterns mined by the application layer can be more in line with the needs of users.

This invention provides a non-uniform linewidth coded scanning reconstruction method for high dynamic range objects. The method mainly includes the following steps: 1) Generating a non-uniform linewidth line scan sequence pattern and a white light pattern, and projecting them onto the surface of the high dynamic range object using an optical encoding device, while an image acquisition device simultaneously acquires the deformed image; 2) Calibrating the surface reflection feature values ​​using the reflection feature value calibration method proposed in this invention, and then equalizing the line scan image based on the reflection feature values; 3) Binarizing the equalized line scan image using a binarization method based on clustering to obtain a standard binary image; 4) Using sinusoidal weighting coefficients to... n Zhang's binary image is synthesized as m 5) Extract the wrapping phase from the sinusoidal image and identify the fringe order to obtain the absolute phase; 6) Import the absolute phase into the pre-calibrated system model to complete the final three-dimensional measurement. The non-uniform linewidth encoding scanning reconstruction method for high dynamic range objects provided by this invention only requires the non-uniform linewidth encoding strategy, surface reflection feature value calibration method, and sinusoidal synthesis model provided by this invention to eliminate image overexposure and achieve high-precision measurement of high dynamic range objects.

The present application belongs to the technical field of navigation light efficiency monitoring, and relates to a navigation light quality intelligent detection method. The present application synchronously collects light intensity data by deploying multiple heterogeneous sensors, and obtains a light intensity data sequence through data fusion. The sequence is preprocessed, dynamic adaptive threshold is used to identify the start and end time stamps of the light-on event, and the effectiveness is verified. Based on the time stamp sequence, characteristic parameters such as flicker period, duty cycle and light-off sequence are calculated in real time. Then, through period stability analysis, duty cycle consistency check and sequence pattern matching, a collaborative decision is made to comprehensively determine the working state of the navigation light. If at least two items are abnormal, an abnormal signal is output. The method solves the problems of low efficiency and poor accuracy of manual detection of navigation light quality, realizes real-time, intelligent and accurate monitoring of the working state of the navigation light, effectively improves the detection reliability and operation efficiency, and guarantees the safety and stability of navigation.

A sequence pattern generation device that can control skew between lanes of a plurality of lanes by controlling a transmission start timing of a sequence pattern of a plurality of outputs. The sequence pattern generation device includes a primary module 2 and a secondary module 3 each having two outputs of data signals, and a device control unit 16 that, in a case where a start of a sequence of the outputs of the data signals is synchronized, sets delay amounts, aligns phases of clocks of the primary module 2 and the secondary module 3, locks phases of clocks of a primary data generation unit 26 of the primary module 2 and a secondary data generation unit 36 of the secondary module 3, and starts the sequence with the delay amounts set in the primary data generation unit 26 and the secondary data generation unit 36.

Systems and methods for creating a magnetic resonance sequence for a magnetic resonance device. The magnetic resonance sequence is described by at least one sequence parameter and provided parameter values of the sequence parameters are used for the determination of a temporal sequence pattern over time by simulation of a subsection of the magnetic resonance sequence by a simulation unit. A permissible parameter value range for at least one selected one of the sequence parameters is determined by an evaluation unit on the basis of the temporal sequence pattern and provided limitations. A new parameter value of the at least one selected parameter within the permissible parameter value range is defined by a definition unit. At least one duration limitation relating to the entire duration of the magnetic resonance sequence, or a specified time constant is provided. The temporal sequence pattern is expanded beyond the subsection by the simulation unit by repetition of the subsection, until the entire duration or the specified time constant is reached or an abort criterion relating to the duration limitation is satisfied.

The invention discloses a risk behavior detection and alarm method and system based on log analysis, and belongs to the technical field of big data security analysis and big models.The method comprises the steps that log collection is conducted, specifically, a multi-source log is captured in real time through a distributed agent and standardized; feature engineering: extracting time, behavior, statistics and session features and constructing feature vectors; calculating an operation interval and a non-working period mark according to the time characteristic; according to the behavior characteristics, the difference between high-frequency operation and the geographic position is counted; analyzing session duration and an operation sequence mode according to the session characteristics; reasoning by adopting a logistic regression model: outputting a risk score based on the feature vector by adopting the logistic regression model; and graded alarming: triggering alarms of different grades according to the risk score. According to the invention, accurate identification and rapid disposal of system risk behaviors can be realized.

The present application relates to the technical field of data processing, and more particularly to a software configuration development management method and device based on artificial intelligence and a storage medium. The method comprises: extracting a configuration change sequence in a database and performing feature processing; mining a sequence pattern of the configuration change sequence to construct a configuration evolution pattern library; performing intelligent recommendation and conflict prediction of changes based on the sequence pattern; determining system configuration entropy according to the configuration evolution pattern library, and dividing the change risk level. The present application overcomes the defects of the existing configuration management technology, such as insufficient perception of the dynamic evolution relationship between configurations, lack of quantitative control of the overall confusion degree of the configuration change set, and provides a configuration management that can automatically learn the configuration evolution pattern and perform intelligent recommendation of changes and system entropy control based on the pattern.

This application relates to the field of power distribution technology, specifically to a remote maintenance method and system for high and low voltage complete sets of power distribution equipment. The system includes: a cloud platform and an edge terminal; the cloud platform is used to: perform fault mode identification on historical state data to obtain multiple fault events; perform sequence pattern mining based on the time and connection relationship between devices in the historical state data to obtain a fault mode tree; and send maintenance information to the edge terminal; the edge terminal is used to: obtain first state data of the device targeted by the received maintenance information after the historical state data, and obtain second state data of the device targeted by the maintenance information before the occurrence of the fault event; determine a maintenance strategy based on the matching result of the first state data and the second state data, and when the maintenance strategy expands to the maintenance subject, send maintenance information to the edge terminal where the device that has not received maintenance information is located.

The invention relates to the technical field of electric power communication, in particular to a data encryption transmission system and method for electric power communication, and aims to realize self-adaptive selection of encryption strategies by constructing a multi-dimensional network state sensing model and dynamically monitoring parameters such as link delay and packet loss probability and combining quantitative grading of data tasks. According to the method, one or more encryption links can be flexibly adopted according to real-time network fluctuation and task priority, an active data residue clearing mechanism is introduced, block chain evidence storage and an improved sequence pattern mining algorithm are combined, in addition, through a seamless switching mechanism of main and standby links, the continuity and stability of communication are ensured, and the communication efficiency is improved. And data loss or delay caused by link interruption is avoided. In this way, the technical problem that in the prior art, a static encryption algorithm or a protection mechanism of a single link is emphasized, and self-adaptive adjustment is difficult to carry out according to real-time fluctuation of a network state and the priority of a data task is solved.

The present disclosure relates to the technical field of data processing, and in particular, to a data set construction method, device, equipment, medium and product. The method comprises: obtaining maintenance text data and sensor time series data of an aero-engine; performing data analysis and information extraction processing on the maintenance text data to obtain first knowledge elements; performing sequence pattern mining processing on the sensor time series data to obtain second knowledge elements; constructing a knowledge graph of the aero-engine based on the first knowledge elements and the second knowledge elements; wherein the knowledge graph comprises an association relationship between fault events and a dynamic evolution law of the fault events; and performing subgraph sampling based on the knowledge graph to construct a question and answer data set of the aero-engine. The present disclosure effectively avoids the deviation caused by subjective judgment by establishing the static association between fault events and the dynamic evolution law of the fault events, thereby improving the accuracy of the question and answer data set.