258 results about "Data authenticity" patented technology

A system and method for optimizing automated textual prompts in artificial intelligence (AI) conversational systems is disclosed. The system comprises a network interface, processors, and memory-storing instructions for performing operations to optimize prompts. These operations include receiving and preprocessing input data, tokenizing the data, verifying data authenticity, performing temporal analysis, calculating prompt complexity scores, and selectively expanding or refining prompts based on complexity thresholds. The system further incorporates context-aware optimization, multi-faceted prompt refinement, variation generation, and evaluation using machine learning models. Additional features include a technological hub with advanced processing capabilities, sensor-augmented input apparatus, device-specific prompt optimization, AI model selection, multimodal context integration, and an AI-driven creativity booster. The system provides interactive prompt visualization, certification, and uniqueness verification modules. This comprehensive approach ensures the generation of optimized, contextually relevant, and creative prompts for various AI applications while maintaining data integrity and user engagement.

The invention discloses a high-precision instrument assembly fault backtracking method and system, belongs to the field of precision manufacturing, and aims to solve the problems that in a traditional backtracking method, assembly data are scattered and unreliable, fault root positioning is fuzzy, and new scene adaptation depends on a large amount of data. The method comprises the following steps: collecting assembly structured data, video images and environment data in a multi-source manner, filtering out low-quality images, and distributing unique identifiers for products; fusing the multi-modal data to generate a depth feature matrix; constructing an anomaly detection model to output a risk score and a label; hashing the data and then storing the data into a product exclusive private block chain; when a fault occurs, extracting data on the chain through a unique identifier, reconstructing an assembly process by using a graph neural network, and comparing a standard positioning root; and based on the fault report incremental training model, parameters are optimized in combination with meta-reinforcement learning. According to the method, the data authenticity is guaranteed, the fault backtracking precision and efficiency are improved, a new scene is quickly adapted, the production rework rate is reduced, and the stable assembly quality is maintained.

The invention discloses an OBD tamper-proof two-way verification method, which comprises the steps of obtaining OBD equipment data and vehicle sensor data, registering to a cloud end based on the OBD equipment data, obtaining registration data, obtaining key data and non-key data according to the vehicle sensor data, symmetrically encrypting the key data by using a session key in the registration data, and verifying the key data and the non-key data. Performing approximate homomorphic encryption on the non-key data, obtaining a double-entropy source dynamic salt value according to the time entropy of the ciphertext and the OBD equipment operation entropy, reversely reconstructing the dynamic salt value by the cloud according to the timestamp and the registration data, verifying the ciphertext structure by using the dynamic salt value and generating a random challenge containing a salt value check code, and sending the random challenge to the cloud; the equipment verification result and the challenge value are signed and then transmitted back, the OBD equipment carries out reverse verification on the salt value according to the signature and the equipment verification result, and the cloud generates an authority token based on the reverse verification result. According to the method, the data authenticity and integrity of the diagnostic equipment are guaranteed through packet encryption, dynamic salt value generation and bidirectional identity verification of transmission data.

The invention provides an information security risk management method and management system. The system is composed of four core modules: a multi-modal data acquisition layer: probes deployed at a terminal and a server support heterogeneous data acquisition of network traffic, operation logs and an API (Application Program Interface) call chain; according to the block chain log storage layer, a private chain is constructed based on an improved BFT consensus algorithm, and each block comprises a timestamp hash value and a preorder block fingerprint; the dynamic risk assessment engine adopts an LSTM-Transform hybrid neural network, and input dimensions comprise a user behavior baseline, a vulnerability library version and threat intelligence feed; and the intelligent response decision module is used for integrating a reinforcement learning algorithm, automatically generating a disposal strategy and triggering the SDN controller to execute network isolation. According to the method, a local chain-alliance chain-audit chain three-level structure is applied to data island treatment and data integration in organization, the problem of internal data fragmentation is solved through the local chain structure, unified storage and rapid source tracing of heterogeneous logs are achieved, and data authenticity and time sequence integrity are ensured.

The invention discloses a data security sharing method and device for verifiable bilateral access control based on an attribute threshold, computer equipment and a storable medium, and aims to realize data privacy protection and source authenticity guarantee in an open cloud-edge collaborative environment. According to the method, an attribute threshold matching mechanism is introduced, a bilateral access control strategy is designed, decryption can be performed only when intersection of attribute sets of a data sender and a data receiver and access attribute sets specified by each other meets a preset threshold, and access authorization under fuzzy matching is supported. In order to ensure the authenticity and non-repudiation of the data, the attribute signature of the sender is embedded into the ciphertext structure, and the credibility of the data source is ensured. According to the method, a constant-size ciphertext structure is adopted, and an offline / online double-stage encryption technology is combined, so that the encryption delay and the calculation burden are reduced. At a decryption end, data verification and partial decryption tasks are outsourced to edge nodes, and a result verification mechanism is introduced, so that malicious nodes are effectively prevented from returning wrong results.

The invention relates to the technical field of bridge monitoring data storage, in particular to a bridge digital twin data storage and tracing platform based on a block chain. According to the technical scheme, the system comprises a multi-mode tamper-proof acquisition module, a credible data preprocessing module, a component-level block chain evidence storage module, a data-driven twin modeling module and a judicial traceability verification module, wherein the credible data preprocessing module adopts an improved ICP algorithm to realize submillimeter registration of laser point cloud and a BIM model; and the component-level block chain evidence storage module realizes millisecond-level credible evidence storage based on a fragment chain architecture divided by bridge components. According to the method, through hardware fingerprint binding, multi-modal data fusion, fragment chain evidence storage, dynamic twin modeling and judicial-level evidence chain construction, whole-process credible management of bridge monitoring data from collection to tracing is realized, and data authenticity, fusion precision, evidence storage efficiency and model dynamics are remarkably improved; and a real-time and intelligent solution with judicial efficacy is provided for bridge health monitoring.

The invention provides a snail bioactive peptide quality tracing system and method based on big data, and belongs to the technical field of snail bioactive peptides. Comprising a data acquisition module used for acquiring various data in the production process of the snail bioactive peptide, a big data processing module used for integrating and cleaning the data, and an intelligent quality analysis module used for analyzing the quality of the snail bioactive peptide. The intelligent quality analysis module is used for carrying out quality prediction and abnormal early warning on the snail bioactive peptide, and the block chain tracing module is used for carrying out quality tracing on the snail bioactive peptide. According to the method, data islands of cultivation, production and detection links can be broken, cross-link association analysis is realized, manual sampling inspection is upgraded to AI-driven real-time early warning and process optimization, data authenticity is guaranteed through a block chain technology, international compliance requirements are met, and consumer trust and brand price are improved by adopting three-dimensional visualization and scientific data.

The present application relates to the technical field of computers, and provides a data sharing method, a device, and a system. Shared data and a data protocol set by a data owner are bound for encryption protection, ciphertext structured data and retrieval metadata are integrally digitally signed, an encapsulation signature and a signature public key certificate are bound on the ciphertext structured data and the retrieval metadata, so that the data authenticity can be verified during circulation and sharing of encapsulated data, and confidentiality and integrity protection of the shared data and the data protocol during circulation and sharing are realized. In addition, the encapsulated data is generated by a trusted device trusted by the data owner, a shared agent is trusted by a plurality of participants participating in data sharing, and data is transmitted between the trusted device and the shared agent through a secure transmission channel, so that the security, confidentiality and integrity protection of the shared data and the data protocol during exporting are realized.

The invention relates to the technical field of data processing, in particular to a screw air compressor state monitoring method, which comprises the following steps: acquiring monitoring data of M dimensions of each monitoring object in an air compressor to obtain multi-dimensional real-time monitoring data and multi-dimensional historical monitoring data; screening suspected real multi-dimensional monitoring data from the multi-dimensional historical monitoring data, and obtaining the data real degree of each dimension in the suspected real multi-dimensional monitoring data; n reference dimensions of any monitoring object at the current moment are obtained, and the data authenticity of each reference dimension in each piece of suspected real multi-dimensional monitoring data and the difference between each piece of suspected real multi-dimensional monitoring data and the multi-dimensional real-time monitoring data are calculated according to the data authenticity of each reference dimension in each piece of suspected real multi-dimensional monitoring data. Obtaining the state voting weight of each piece of suspected real multi-dimensional monitoring data for any monitoring object; and according to the state voting weight of each piece of suspected real multi-dimensional monitoring data, a state evaluation result of any monitoring object at the current moment is obtained by utilizing a KNN algorithm, and the running state of the current air compressor is accurately obtained.

The invention discloses a privacy protection truth value discovery method based on reputation in a block chain. The method provided by the invention aims at the problems of perception data quality reduction, participant privacy information leakage and the like caused by lack of a trusted participant screening mechanism and insufficient privacy protection in the crowd intelligence network. Therefore, participants with high creditworthiness are screened on the premise of privacy protection to complete a data perception task, so that high-quality data are obtained. According to the method, firstly, a double-chain collaborative architecture is constructed based on a block chain, and privacy protection is performed on creditworthiness of participants through exclusive or homomorphic encryption; in the subsequent task allocation stage, the high-reputation participants are preferentially selected to execute the data acquisition task in combination with multi-round encryption communication and a dynamic screening mechanism; and finally, on the basis of the data of the high-credibility participants, calculating the task scores of the participants with unknown reputation, dynamically updating the reputation of the participants, and ensuring the authenticity and accuracy of the data.

A method for enhancing detection of fraudulent authentication data includes receiving, by an electronic device, data during an authentication transaction, computing a feature vector from the received data, and normalizing the feature vector. The method also includes encoding the normalized feature vector into qubits, expanding, using at least one quantum algorithm, the normalized feature vector into a high-dimensional space, and detecting in the high-dimensional space anomalies indicative of fraud based on the qubits. Furthermore, the method includes calculating, based on the detected anomalies, a confidence score reflecting a likelihood that the received data is genuine and comparing the confidence score against a threshold value. In response to determining the confidence score fails to satisfy the threshold value, the method determines that the received data requires secondary authentication.

The invention discloses a multi-dimensional credit asset traceability system and method. The system comprises a data acquisition and encryption module (10) which is used for performing encryption and digital signature on credit asset data based on a trusted execution environment (TEE) and a hardware security module (HSM) at a data generation end; the parallel Hash processing module (20) is used for performing parallel Hash operation on the encrypted data and generating leaf node Hash values based on GPU acceleration and a multi-thread technology; the nested hash and zero-knowledge proof module (30) constructs leaf node hash values into a compression type Merkle tree and generates a zero-knowledge proof (ZKP), so that a third party can verify the authenticity and consistency of data without accessing full data; the on-chain evidence storage module (40) is used for recording root Hash (RootHash) and zero knowledge proof of the compression type Merkle tree to the block chain; the dynamic authorization and secure multi-party computing module (50) performs access clipping on the credit asset data based on the access intention and realizes verification of the minimum data dimension through secure multi-party computing (SMPC). Through combination of technical means such as trusted hardware acquisition, parallel hash calculation and privacy protection verification, rapid verification is realized on the premise of not exposing original data, and verification performance, data security and privacy protection capability are remarkably improved.

The method provides for securely harvesting and distributing trusted metadata from devices to artificial intelligence (AI) systems. It enables use of cryptographic hashes and signatures on data for supply chain provenance. It is an agentless method to enhance application security by design, and data protection with data authenticity and confidentiality in device to upstream services communications and data sharing. It helps securely harvest, filter, and forward device metadata to webhooks for AI / ML driven data analytics.

The invention is suitable for the technical field of data processing, and particularly relates to a financial data processing and classification method, system and equipment based on verifiable computing, and the method comprises the steps: obtaining on-chain transaction data verified by a block chain consensus node storage and risk-related index data verified by an under-chain oracle machine; the problems that the authenticity of traditional financial data is difficult to verify and the data is split are solved; calling a risk assessment model through an intelligent contract deployed on a block chain, and placing an assessment process in a decentralized block chain network; performing verifiable calculation through an intelligent contract to determine a total performance score and a risk index, and realizing whole-course tracing and verification of a calculation process by utilizing the characteristics of verifiable calculation of a block chain; the total performance score and the risk index are stored in the block chain as non-tampering records, and the risk rating groups are automatically divided according to the fixed rating rule on the chain through the smart contract, so that the authenticity, objectivity, verifiability and fairness of financial data processing are guaranteed.

The invention discloses a vacuum degree condition evaluation method and system of a vacuum arc extinguish chamber, and belongs to the technical field of vacuum circuit breakers. The evaluation method combines RF and an XGBoost algorithm to realize heterogeneous integration, adds a GAN discriminator to perform physical constraint enhancement, and constructs a universal relation model of X-ray intensity and vacuum degree; the feature importance of RF can guide the feature selection or weighting of XGBoost and enhance feature interaction, the confrontation mechanism of GAN can coordinate data fitting and physical constraints, the physical rationality and data authenticity of model prediction are enhanced by means of confrontation training, and a discriminator is used for dual verification of a generated result. The model is ensured to conform to experimental data distribution and theoretical physical law constraints at the same time, and the result credibility is remarkably improved. According to the evaluation method, accurate fitting of the stable attenuation trend in the high vacuum stage and robustness prediction of fluctuation data in the critical vacuum stage can be achieved, and a new thought is provided for vacuum degree condition evaluation.

The invention relates to the technical field of visual inspection, and discloses an industrial product online visual inspection method based on cloud collaboration, which ensures data authenticity through edge side close-range acquisition of images, meets the real-time detection rhythm of a production line through a lightweight model, and quickly screens out most clearly qualified or unqualified samples. And for subsequent cloud collaboratively reducing invalid data transmission, the edge side single-frame image detection time is less than or equal to 30ms, the high-speed production line beat requirement is met, 80%-90% of clear samples are preliminarily screened out, burden is reduced for the cloud, and bandwidth congestion caused by total data transmission is avoided.

The invention provides a vehicle track generation method and system, and relates to the technical field of data processing. Obtaining vehicle trajectory data, and screening out vehicle following fragments with a traffic oscillation phenomenon; carrying out risk grade division on the vehicle following fragment, calculating a risk index value based on a peak speed variation coefficient, an aggregation approaching risk and an aggregation collision risk, and dividing the risk index value into discrete risk tags; a conditional diffusion Transform model guided by physical information is constructed and trained; and using the trained conditional diffusion Transformer model to generate high-fidelity and physically consistent vehicle trajectory data according to a specified risk tag. When the vehicle track is generated, the data authenticity and the physical rule consistency are ensured.

The invention relates to the technical field of information security, and discloses a security control method and system for a cloud inventory management platform. The method comprises the following steps: acquiring a cloud interaction data set; performing abnormal operation identification by adopting the behavior reference template to obtain an abnormal operation set; the behavior characteristics in the set are matched with historical risk modes, and threat possibility scores are generated; related inventory data records are extracted from the data of which the threat possibility scores exceed a preset score threshold, and a to-be-checked data set is formed; performing field content and time sequence comparison on the data set, and analyzing a tampering sign to obtain a tampering possibility level; and inputting the level into a risk processing rule base, and generating and executing a safety control strategy. According to the method, a whole-process security mechanism from user behavior monitoring to data authenticity verification to risk closed-loop disposal is constructed, so that potential data tampering risks can be effectively identified and coped with, and the security and data reliability of a cloud inventory management platform are improved.

The invention is suitable for the field of data consistency verification, and provides a versioning consistency verification system and method for language generation, and the system comprises a knowledge unit generation module which is used for segmenting a text into knowledge slices and defining semantic vectors; the knowledge unit voucher generation module is used for giving a hash voucher; the versioned voucher storage database is used for storing the block contents, the corresponding semantic vectors, the block / vector identification codes and the hash vouchers; the access control module is used for authorizing and recording data access and modification operation of the knowledge base; the vector pre-reading storage module is used for storing corresponding block identification codes of the vector data; the question unit generation module is used for generating question slices, question semantic vectors and question blocks / vector identification codes; and the retriever is used for performing block / vector identification code retrieval. Therefore, the problem of potential safety hazards of an existing RAG system in the aspect of ensuring data authenticity and consistency can be solved.

The invention discloses an intelligent auxiliary method and system for graphic annotation depth analysis, and particularly relates to the field of image processing and image distinguishing, and the method comprises the steps: collecting an image sequence containing a target object, and synchronously recording environment data which comprises an illumination condition, a shooting visual angle parameter and a shielding position; combining the image sequence with environmental data to form an input substrate; according to the scheme, multi-source information such as image area complexity, texture features and edge curvature is subjected to comprehensive modeling in a sheltered environment, a structure response mapping and boundary consistency scoring mechanism is constructed, and credible completion candidate areas are dynamically generated and screened, so that verifiable completion and graphic marking consistency guarantee of sheltered targets is realized, and the accuracy of the sheltered targets is improved. Therefore, the problem that the authenticity of the graphic annotation data is difficult to maintain due to target missing of the existing graphic annotation mechanism is solved.

The invention belongs to the related technical field of fault diagnosis, and discloses a fault diagnosis sample generation method, application and equipment based on a physical constraint adaptive migration network, and the method comprises the steps: (1) converting original vibration signals of reference equipment and monitoring equipment into a time sequence spectrogram through continuous wavelet transform; (2) processing the time sequence spectrogram by adopting a spectrum feature encoder of the physical constraint adaptive migration network to extract multi-scale spectrum features; wherein a loss function of the physical constraint adaptive migration network comprises content loss, style loss and frequency band energy loss; (3) processing the multi-scale spectrum features by adopting the adaptive style normalization module so as to fuse the fault content features of the reference equipment and the machine style features of the monitoring equipment; and (4) processing the fused features by adopting a feature reconstruction decoder so as to reconstruct and generate a synthetic sample. According to the invention, the problem of insufficient data authenticity and physical consistency is solved.

The invention discloses a policy information crowdsourcing-oriented multi-level verification system and method, and relates to the technical field of data verification. A data source verification module; a content identification module; a distributed consensus verification module; and an abnormal behavior detection module. According to the policy information crowdsourcing-oriented multi-level verification system and method, the problem of data authenticity verification in government policy information crowdsourcing is solved, a four-level cascade protection framework is adopted as the core, and through four-level verification of data source verification, content identification, distributed consensus verification and abnormal behavior detection, the data authenticity verification efficiency is improved. The authenticity and compliance of the government policy information crowdsourcing data are ensured, the efficient, low-delay and anti-cheating system performance is achieved through algorithm optimization, and finally the core targets of 95% verification accuracy, 90% cheating detection rate and the like are achieved.

The invention provides a power plant carbon emission data full life cycle management method and device, and relates to the technical field of data processing, and the method comprises the steps: collecting production, fuel and coal quality test system multi-source data in real time through a standard interface, and forming a dynamic data flow; predicting the carbon content of the fire coal element based on a BP neural network, and calculating the carbon emission by combining an emission factor method; carrying out back calculation on the discharge amount by using a mass balance method, analyzing and verifying the relevance between data and an accounting result through multiple regression, and triggering correction when an error exceeds a threshold value; a double-stage attention deep learning network is adopted to predict the carbon price in a rolling mode, a carbon asset dynamic analysis result is generated in combination with a quota estimation model, real-time emission data, quota surplus and shortage and the carbon price trend are displayed through a visual interface, and graded early warning is conducted on abnormal data. According to the method, full-life-cycle automatic management is realized, the data acquisition real-time performance and accounting precision are improved, the data authenticity and traceability are guaranteed, and the carbon asset management decision support capability is optimized.

The invention discloses a block chain-based low-altitude airspace usage recording method. The method comprises the steps of performing registration and authentication of an aircraft and an operator; carrying out flight plan declaration and auditing; recording flight data in real time; carrying out change processing on the flight plan; the emergency situation is handled; and sharing and querying airspace use information. Through distributed storage and a consensus mechanism, flight plans, real-time flight data and supervision operation cannot be tampered with an overground chain, and the authenticity, integrity and traceability of data are achieved.

The invention discloses an unattended wagon balance monitoring method and system, and relates to the field of wagon balance monitoring, and the method specifically comprises the following steps: S1, constructing a data processing architecture; s2, triggering and receiving multi-source data; s3, carrying out data verification and credibility evaluation; s4, executing a graded early warning decision; and S5, realizing data archiving and model iteration. The unattended wagon balance monitoring method and the unattended wagon balance monitoring system provided by the invention can accurately identify behaviors influencing data authenticity, such as incomplete loading of a vehicle and cargo cheating, and guarantee the accuracy of commercial measurement data from the source. Meanwhile, for a localized dynamic correction mechanism of slightly abnormal data, a complete traceability mark is reserved while data validity is ensured, a traceable verification evidence chain is formed, a reliable basis is provided for solving commercial transaction disputes, legal rights and interests of two transaction parties are practically maintained, meanwhile, the manual intervention requirement can be greatly reduced, and the transaction efficiency is improved. Management personnel are liberated from tedious remote verification work.

The invention discloses an employee performance evaluation method based on behavior data driving, and relates to the technical field of human resource management, and the method comprises the following steps: constructing a multi-channel network fluctuation prediction matrix, generating an expected behavior arrival time scale based on the transmission state of a task switching node, and taking the expected behavior arrival time scale as a baseline for behavior sequence gap recognition; and under the guidance of an expected behavior arrival time scale, performing sliding window scanning on the received behavior sequence, generating a gap identifier when detecting that a time slot exceeds a threshold value, and injecting a confidence coefficient weight descending label. According to the method, a behavior gap is accurately identified through network fluctuation prediction and sliding window scanning, behavior reconstruction is realized in combination with self-supervised generation and residual reasoning, and dynamic evaluation correction is completed by using confidence projection and an intelligent regulation engine, so that the data authenticity is guaranteed from the source, and the objectivity and robustness of performance evaluation and the resource allocation precision are improved.

The invention relates to the technical field of water environment quality supervision, and discloses a regional water environment quality intelligent supervision platform, which comprises an initialization module, a parameter configuration module, a data acquisition and preprocessing module, a reverse electronic fence module, an electronic fence module, a traceability and decision module and an adaptive optimization module. According to the invention, large-range monitoring is converted into key area precise monitoring of the multi-level electronic fence; safe operation of monitoring equipment is guaranteed through a reverse electronic fence technology, and data authenticity is ensured; a complete event chain is constructed by using a multi-fence correlation analysis technology, and accurate traceability is realized by fusing multi-source data; a time sequence mode is identified through a dynamic time warping algorithm, and water quality abnormity causal reasoning is realized based on a Bayesian network; and a risk assessment driven resource adaptive allocation mechanism is adopted to realize on-demand dynamic allocation of computing resources. The technical problems that a traditional water quality monitoring system is weak in traceability, frequent in false alarm, lagged in response and isolated in data are solved.

The present invention discloses a full-process traceability system for aquatic products based on blockchain technology and its implementation method, which belongs to the field of quality control technology. The system includes a data acquisition layer composed of an environmental monitoring unit, a feeding monitoring unit and an RFID identification unit for collecting aquaculture environment data; an edge computing layer including an edge server and an industrial control computer, which pre-process the collected data; a blockchain layer constructed using the Hyperledger Fabric platform to achieve data tamper-proof storage and automatically verify data authenticity through smart contracts; an application layer including aquaculture management subsystem, a processing management subsystem, a logistics management subsystem and a sales management subsystem, and a multi-level authorization mechanism to manage data access rights. The present invention adopts the above-mentioned system and its implementation method to solve problems such as data falsification, high tracing costs and information opacity, and realize full-chain credible traceability of aquatic product quality and safety.

The invention relates to the technical field of medical instrument manufacturing and management, in particular to a medical instrument production management system and method based on a cloud platform. The method comprises the following steps: collecting multi-source data, and ensuring the authenticity and accuracy of the data by adopting Hash binding and discrete logarithm problems; generating a production line state embedding vector by using time sequence convolution and multi-head self-attention fusion spatial-temporal characteristics; constructing a flexible task strategy graph based on a twinning state, and dynamically adjusting task path resource consumption, compliance risk and stability deviation weight in combination with multi-objective optimization; visual tracing is realized through flow map modeling and multi-scale compliance labeling, an illegal causal chain is positioned, and the weight of an adjustment strategy is fed back; and finally, generating a self-adaptive control instruction by the graph attention network. According to the method, multi-modal fusion, strategy graph dynamic optimization and causal inversion technologies are integrated, a perception-optimization-tracing-regulation closed loop is formed, resource scheduling, compliance and production stability are collaboratively optimized, and actual application requirements are met.

The invention provides a sequence decision strategy acquisition method and system for high-neuroplasm crowds. The method comprises the following steps: establishing a full-screen drawing canvas, and loading a task set comprising a decision map type, an emotion titer ratio, a reward parameter, a random seed and a sequence control parameter; collecting user gender selection and binding a corresponding emotion picture library; generating a decision map based on a Markov decision principle under the control of the parameter set, defining nodes and connecting edges, randomly scheduling and laying emotion pictures according to a titer ratio, and assigning rewards to form an emotion and reward coupling environment; based on the environment, recording a mouse position and a mouse path in an execution exploration stage, recording key pressing time, an action direction and an award in an execution stage, and establishing step sequence association according to a timestamp to form sequence decision data; a Latin square balance control task sequence is adopted, and the process is propelled under the trigger of a user; and finally, filing the data in a binary system and a text file for subsequent analysis. According to the invention, the authenticity and repeatability of data are improved.