808 results about "Pattern matching" patented technology

The invention provides a supply chain multi-dimensional fluctuation prediction and inventory optimization method based on artificial intelligence, and relates to the technical field of supply chain management, and the method comprises the steps: obtaining multi-dimensional data, carrying out the preprocessing of the multi-dimensional data, generating a standardized time series data set and a knowledge graph, carrying out the multi-scale decomposition through wavelet transformation, and extracting fluctuation features; and constructing a state matrix by combining a graph attention network and a multi-head self-attention mechanism, predicting a risk conduction path based on historical fluctuation pattern matching and a graph convolutional network, and generating a risk prevention and control scheme. According to the invention, the fluctuation characteristics and the risk conduction path in the supply chain system can be accurately identified, and the early warning accuracy and the response timeliness are improved.

The invention discloses a network threat multi-modal detection method based on a large model, and belongs to the technical field of network security, and the method comprises the steps: collecting three types of heterogeneous data of NetFlow flow of a network layer, a system call chain sequence of a host layer and a protocol load of an application layer, and carrying out the desensitization processing and feature coding to generate a unified tensor format; the method comprises the following steps: through network security threat intelligence and MITRE ATTamp; performing supervision fine tuning on the large model by using a CK attack chain sample, and constructing a network threat identification special model; cross-device behavior characteristics are extracted through a model self-attention mechanism, and a dynamic behavior map is constructed; and finally, comprehensively evaluating an attack mode matching degree, a node vulnerability mean value, historical alarm association and an attack path risk by adopting a weighted fusion algorithm, and triggering a high-confidence alarm when a comprehensive score exceeds 0.8. According to the method, through multi-modal data fusion and dynamic graph analysis, the detection precision and response efficiency of the complex attack chain are improved.

The invention provides a photovoltaic active distribution network voltage out-of-limit problem tracing method comprising the following steps: receiving research data and carrying out multi-scale feature analysis to obtain a standardized multi-dimensional feature matrix and a time-space incidence matrix; constructing an initial causal graph based on basic research data and power grid physical constraints, and constructing a multi-layer causal network by calculating causal intensity; extracting a dynamic feature sequence, analyzing propagation features, and generating a propagation path set and a path weight matrix; performing tensor completion and state estimation on the research data to obtain a state estimation matrix and estimation uncertainty; and outputting an out-of-limit source position, an out-of-limit risk score and early warning information based on pattern matching analysis and risk assessment. According to the invention, through combination of multi-dimensional feature analysis and a multi-layer causal network, accurate positioning and risk early warning of a voltage out-of-limit source are realized, and traceability accuracy and analysis efficiency are improved.

The invention relates to the technical field of data exchange, in particular to a data exchange system of a nested tag structure, which comprises a data carrier adaptation module, a structure analysis module, a field mapping module, a logic driving module and a script generation module. According to the method, carrier identifiers are generated through comparison of MIME types, file header features are positioned through pattern matching, the recognition accuracy and adaptability are improved, a nesting relation is calculated through depth-first traversal of file paths, node sequences are generated through analysis in combination with a DOM tree, the label analysis efficiency is optimized, and a mapping table is generated through algorithm matching of field identifiers and database codes; the method comprises the following steps: dynamically constructing a field mapping relation, reducing manual configuration, constructing a decision matrix based on a mapping table, executing logical operation to generate a status bit and triggering a threshold write-in instruction, enhancing strategy dynamic adjustment capability, reducing semantic ambiguity through multi-algorithm collaboration and a dynamic decision mechanism, improving data exchange robustness, and supporting real-time state response. And the transmission timeliness of the industrial Internet of Things is ensured.

The invention discloses a document information input method and system based on big data processing, and belongs to the technical field of document information processing. According to the method, deep risk feature screening is performed on document data by combining big data mining and a knowledge graph, potential sensitive elements can be accurately identified, risk categories and influence ranges are analyzed based on association rules, automatic early warning and real-time risk notification are realized, a dynamic rule knowledge base is adopted, multi-dimensional compliance verification is supported, and the risk assessment efficiency is improved. Service constraint conditions can be updated in real time, the flexibility and adaptability of rules are ensured, the accuracy and timeliness of compliance check are improved, pattern matching and multi-level check are performed on compliance data objects through an intelligent check process and a recheck rule set trained by big data, the preciseness and reliability of data check are ensured, and the data check efficiency is improved. Through comprehensive quantitative calculation of the business complexity, the risk coefficient and the timeliness priority, the task scheduling weight parameter is generated, the receipt auditing path is optimized, and the auditing efficiency is improved.

The invention provides a monitoring information identification method based on the Internet of Things and a server, and the method comprises the steps: firstly obtaining multi-modal perception data which are collected by a plurality of Internet of Things monitoring nodes in a target monitoring area in real time and comprise images, sounds and environment parameters, and then carrying out the cross-modal feature extraction and fusion processing of the multi-modal perception data; the method comprises the steps of generating a comprehensive spatio-temporal characteristic spectrum, determining abnormal event types and spatio-temporal distribution information in a region through dynamic mode matching based on a preset abnormal mode library, generating an adaptive regulation and control instruction set according to the abnormal event types and the spatio-temporal distribution information, and determining control parameters and execution priorities of different Internet of Things execution devices. And finally, distributing the instruction set to an associated Internet of Things execution device cluster, triggering the cluster to execute control parameters according to priorities, eliminating the influence of abnormal events, and realizing comprehensive and intelligent monitoring and exception handling of a target monitoring area.

The invention relates to the technical field of AI protection, and discloses an AI protection engine construction method and system based on Web application. The method comprises the steps of obtaining a Web application HTTP request and performing semantic analysis to obtain request feature data; performing multi-dimensional threat feature extraction to obtain a Web request threat feature set; performing mode matching on the Web request threat feature set and a preset attack mode library to obtain a threat type judgment result and attack intention information; performing time sequence behavior analysis to obtain a Web behavior abnormal index; comprehensive decision making is carried out through a multi-agent collaborative decision making system, and a defense decision making scheme is obtained; according to the defense decision scheme, a corresponding defense component is selected through a defense execution engine for safety protection, and a Web request processing result is obtained. According to the method, corresponding protection measures can be taken aiming at requests of different risk levels, so that logic vulnerability attacks which are difficult to detect by a traditional system are effectively identified and defended.

The invention discloses a fault root cause positioning method and system driven by a dynamic knowledge graph, and relates to the technical field of fault root cause localization, and the method comprises the steps: collecting and obtaining a multi-source fault associated data set, carrying out the entity association extraction of the multi-source fault associated data set, and obtaining a fault entity set and an entity relationship set; performing graph node cascading and incremental learning updating, and constructing a fault updating knowledge graph; monitoring and acquiring target fault data, performing mode matching reasoning, and generating a fault mode candidate root cause set; and performing similarity matching on the fault mode candidate root cause set in combination with a historical fault case library, and determining a target fault root cause positioning result. The technical problem of low fault diagnosis efficiency caused by inaccurate fault root cause positioning and knowledge graph updating lagging in the prior art is solved, and the technical effects of realizing accurate positioning of the fault root cause and dynamic improvement of the knowledge graph and improving the fault diagnosis efficiency and accuracy are achieved.

The invention discloses a moving target trajectory prediction method and related equipment, and relates to the technical field of trajectory prediction.The method comprises the steps that multi-source dynamic sensing data of a moving target are obtained, and the multi-source dynamic sensing data comprise target motion state parameters, environment disturbance parameters and biological behavior characteristic parameters; generating an initial trajectory prediction result based on the target motion state parameters; dynamically correcting the initial trajectory prediction result based on the environmental disturbance parameters to obtain a disturbance compensation trajectory; and performing behavior pattern matching on the disturbance compensation trajectory based on the biological behavior characteristic parameters to generate a target trajectory prediction result. By fusing the multi-source dynamic sensing data and combining a dual optimization mechanism of environment disturbance dynamic correction and biological behavior pattern matching, the trajectory prediction precision in a complex scene is improved.

The invention provides a network attack detection method and system based on a distributed intelligent probe, and the method comprises the steps: receiving real-time flow data synchronously collected by the distributed intelligent probe at each node of a network, carrying out the inter-node interaction relation modeling processing of the real-time flow data, recognizing a flow communication mode between different network nodes, and carrying out the detection of the network attack. Generating a flow association map containing the node connection relationship and the communication frequency; carrying out abnormal communication path mining based on the flow association map, and extracting a node communication sequence with an abnormal mode by analyzing the deviation degree of a node connection relationship and the fluctuation characteristics of communication frequency; performing pattern matching processing on the node communication sequence and an attack behavior template in a preset attack feature library, calculating sequence matching similarity and generating an attack matching degree score set; and determining a network attack type and attack source node positioning information, and generating a network attack detection result. According to the invention, the practicability and effectiveness of network attack detection are improved.

The embodiment of the invention provides a fraud phone real-time recognition method and device based on AI semantic understanding, and the method and device achieve the precise understanding of the dialogue content through the innovative construction of a voice analysis mechanism, the grammatical feature extraction and the semantic role marking. And designing a scene discrimination model based on verbal skill recognition, and establishing a fraud verbal skill recognition strategy for intelligent classification in combination with semantic pattern matching and hierarchical clustering algorithms. A residual fusion assessment mechanism is introduced, and accurate assessment and timely prevention and control of call risks are realized through historical case feature fusion and risk scoring. According to the method, the defects of the traditional technology in the aspects of speech understanding, verbal skill recognition, risk assessment and the like are effectively overcome, and the accuracy and reliability of fraud phone recognition are remarkably improved.

The invention provides a processing method and system for reconstructing an LDI sub-pixel image through sparse compression and application, and the method comprises the steps: calculating a low-resolution to-be-exposed image corresponding to each phase structure in advance through a computer image compression algorithm; the low-resolution to-be-exposed images obtained through calculation are loaded to the digital micro-reflector according to the time sequence, and the display moment of each pattern is matched with the rotation position of the corresponding phase structure; coupling the pattern of the digital micro-mirror and the phase encoding mask in a frequency domain through a 4-f optical system, and reconstructing a high-resolution exposure pattern at a sub-pixel level by utilizing a diffraction effect; projecting a high-resolution light field of the reconstructed high-resolution exposure pattern to the surface of the photoresist, and forming a target circuit pattern by accumulating exposure dose; and the micro-nano structure with sub-pixel precision is obtained after development. The system comprises an image compression module, a pattern matching module and a pattern forming module. According to the invention, the manufacturing cost of the laser direct imaging equipment is reduced under the condition of the same precision.

The invention discloses a low-voltage transformer area-oriented power failure fault research and positioning method and system, and relates to the technical field of electric power, and the method comprises the steps: collecting power failure event data of a low-voltage transformer area in real time; performing multi-dimensional feature analysis on the power failure event data, and constructing a transformer area topological relation network according to space-time correlation features; generating a fault feature vector based on the space-time correlation feature and the transformer area topological relation network, performing mode matching according to the fault feature vector, and determining a fault matching result; and carrying out positioning verification on the low-voltage transformer area according to a fault matching result, and generating a fault positioning parameter. Therefore, the technical effects of quickly and efficiently realizing the power failure fault diagnosis and positioning in the low-voltage transformer area and improving the positioning accuracy and reliability are achieved.

The invention provides a full-layout defect rapid detection system based on pattern matching and classification, which relates to the technical field of semiconductor manufacturing and comprises a data acquisition and multi-source fusion module, a self-adaptive preprocessing module, a dynamic pattern matching module, a multi-source feature fusion module and a closed-loop optimization and output module, the dynamic mode matching module dynamically generates a defect template from real-time data through a clustering algorithm (such as DBSCAN) and a generative adversarial network (GAN), the speed and accuracy of defect detection are remarkably improved in combination with reinforcement learning filtering and region focusing technologies of the self-adaptive preprocessing module, a detection strategy can be adaptively adjusted according to the real-time data, and the defect detection accuracy is improved. According to the method, a high-risk area is preferentially matched, and GPU parallel computing acceleration processing is performed, so that the detection time is shortened to 50% or below of that of a traditional method, deformation or fuzzy defects can be effectively identified, the false alarm rate is reduced by about 30%, and high-quality input is provided for subsequent matching and classification.

The invention provides an ecological circulation barrel-in-barrel culture data acquisition method and system, and relates to the technical field of intelligent control, and the method comprises the steps: carrying out the feature extraction of a historical data flow through a multivariable coupling analysis model, carrying out the mode matching through a preset environment threshold interval and a culture environment state matrix, and obtaining a culture environment state matrix; generating a water quality health degree evaluation index and an ecological imbalance early warning signal; and analyzing the water quality health degree evaluation index and the ecological imbalance early warning signal based on a fuzzy control algorithm, dynamically calculating a final feeding amount curve and a water change intensity function, and generating a regulation and control instruction set containing the feeding frequency, the bait particle size and the water pump rotating speed. According to the invention, real-time evaluation and accurate intervention of the water quality health state can be realized.

The invention relates to the field of energy consumption analysis, and discloses an AI-based energy consumption data analysis and prediction system, which comprises the steps of collecting environmental parameters and running states of equipment, dynamically identifying the current system working condition by using a working condition identification algorithm combining incremental clustering and historical mode matching, and predicting the energy consumption data. Collected data is divided according to time, space and working condition dimensions, multi-scale features are extracted, normalization parameters can be dynamically adjusted along with changes of working conditions, a drift index is calculated through comparison of a drift threshold value and historical distribution, an optimal normalization updating strategy is selected according to the drift index, the normalization parameters are dynamically updated, and energy consumption trend prediction is conducted through a statistical model. A prediction result is combined with a working condition label to carry out weighted correction, error analysis and deviation detection are carried out in combination with a drift index, working condition prediction and historical error data, and an analysis result is fed back to a working condition sensing module, a feature adaptive module and a normalization control module. The method has the advantage of improving the stability and reliability in a dynamic environment.

The invention discloses a government affair data automatic classification and grading method, and relates to the technical field of data management and information processing. According to the method, data of different sources and formats are converted into structured formats by adopting a regular expression and a pattern matching technology, and the consistency and accuracy of the data are improved by means of a machine learning algorithm, data quality detection, anomaly correction and the like; by constructing a government domain ontology and a knowledge graph and combining a natural language processing technology, semantic classification is effectively carried out on government affair data, overlapping and uncertainty between categories are solved, robustness and accuracy of classification decision are further improved through an integrated learning algorithm, high efficiency and reliability of classification are ensured, and the method is suitable for large-scale popularization and application. By establishing a data grading dynamic adjustment and update mechanism and combining real-time data monitoring, sensitivity evaluation and automatic adjustment of access control rules, the flexibility and security of data management are ensured.

The invention relates to the technical field of equipment fault prediction, and discloses a fire fighting equipment fault early warning method and system based on multi-dimensional data linkage, and the method comprises the steps: collecting multi-dimensional operation parameters, and carrying out the data preprocessing, and obtaining standardized data; and then time synchronization and dependency relationship analysis are carried out, an equipment dependency graph is constructed, influence weights among equipment are quantified, and abnormal fluctuation characteristics are extracted to form a risk mode set. And based on the risk mode set, identifying a high-frequency high-weight risk cluster as a preliminary fault feature through grouping analysis, and determining a core fault feature by combining historical data correlation analysis. And then matching abnormal signals to generate spatial labels, integrating data to form an abnormal event candidate list, analyzing an event propagation path in combination with environmental data, and verifying the event propagation path to obtain a final abnormal event list. And finally, performing priority grading according to a historical mode matching result, and generating a visual early warning report by using a three-dimensional point cloud rendering technology. According to the method, the fault early warning accuracy of the fire-fighting equipment is improved.

The invention provides a cross-protocol identification data intelligent analysis middleware method and system. The method comprises the following steps: taking multiple industrial communication protocol identifiers in advance to form a metadata set, and constructing a protocol feature element model library containing a non-standard protocol feature template; a programmable logic device is introduced, physical layer signal waveform characteristics are matched, and corresponding communication protocol stacks are dynamically switched at a hardware level according to a matching result; receiving a communication data stream through an electric domain processing unit of the programmable logic device, and generating a protocol analysis logic sequence in combination with a state transition rule of the meta-model library and a mode matching rule of the protocol stack; performing combined operation on the structured feature template and the analysis logic sequence to generate an executable cross-protocol data analysis rule; and when unknown protocol characteristics are detected, triggering dynamic reconstruction of the template, so that the analysis rule is adapted to a newly added protocol. According to the technical scheme provided by the invention, the protocol compatibility development cost in an industrial multi-protocol scene is remarkably reduced.

The invention relates to the technical field of intelligent power grid and household energy management, and provides a household ultra-short-term load prediction method, which comprises the following steps of: performing preprocessing and characteristic engineering processing on user power consumption data acquired by an intelligent electric meter; user type clusters are obtained through clustering analysis, and a standard user power consumption mode corresponding to each cluster is determined; constructing a deep learning prediction model based on the mode, and generating a model parameter matrix sequence and a timestamp; generating an ultra-short-term load prediction value by using the parameter matrix and historical data, and forming a prediction load curve according to a timestamp; and finally, the prediction curve and the management suggestion are sent to the household energy management terminal. According to the method, accurate prediction is realized through data cleaning, user grouping, pattern matching and deep learning modeling, and a visual result and an energy-saving suggestion are output. According to the invention, the household energy management efficiency and the power utilization economy can be improved.

The invention discloses an automatic monitoring method for infrastructure construction operation violation based on multi-modal data fusion, and belongs to the technical field of graphic data processing and pattern recognition, and the method comprises the steps: obtaining multi-modal source data of a target scene, carrying out the data recognition and standardization processing of the multi-modal source data, and generating preprocessing feature data; acquiring an on-site environment condition, and generating an environment state parameter; performing dynamic weight distribution on the preprocessed feature data based on the environment state parameters to generate weighted feature data; performing multi-source data conflict judgment and resolution on the weighted feature data to generate conflict resolution event data; and based on a preset behavior rule pattern library, performing behavior pattern matching on the conflict resolution event data to generate a violation judgment result. According to the method, a multi-modal spatial-temporal feature fusion and dynamic weight distribution mechanism is adopted, and an environment adaptive perception and multi-source data conflict resolution technology is combined, so that automatic monitoring and pattern recognition of illegal behaviors in a construction environment can be realized.

The invention relates to the technical field of cold storage spraying, and discloses a cold storage spraying polyurethane real-time monitoring system based on temperature and humidity feedback, and the system comprises an environment collection module, a state analysis module, a parameter regulation and control module, a dynamic fusion module, a mode matching module and a feedback execution module. The environment acquisition module acquires temperature and humidity data and sets a monitoring interval; the state analysis module divides coating detection nodes and generates feature vectors; the parameter regulation and control module is used for separating influence factors, establishing control rules and acquiring process parameters; the dynamic fusion module identifies the equipment state, performs dynamic compensation and calculates the parameter difference degree; the mode matching module deduces an optimal curing threshold value and generates a deviation sequence; and the feedback execution module analyzes the deviation sequence and generates an adjustment instruction. The system realizes real-time monitoring and accurate regulation and control of the process of spraying polyurethane on the refrigeration house, improves the quality stability of the coating and the construction efficiency, is suitable for refrigeration house construction and maintenance scenes, and has the advantages of high environmental adaptability, accurate parameter regulation and control and the like.

The invention relates to the technical field of intelligent fall detection, in particular to an old people fall detection and early warning system based on multi-modal data fusion, which comprises a dynamic weight fusion unit, a precursor trigger unit, a multi-level decision unit and a verification feedback unit, the dynamic weight fusion unit collects three-axis acceleration, surface electromyographic signals and millimeter wave radar data, multi-source data association analysis is achieved through a double-variable attenuation model and dynamic threshold calibration, and the precursor trigger unit activates electromyographic high-frequency sampling and joint three-dimensional space included angle monitoring when the exercise intensity is abnormal. And the multi-level decision-making unit constructs a tumble probability algorithm based on a random forest model, and performs three-level verification in combination with acceleration standard deviation, ground contact point density and historical mode matching, thereby realizing accurate discrimination of tumble events, providing a high-reliability intelligent solution for safety protection of old people, reducing the risk of tumble injury, and improving the safety of the old people. And the first-aid response efficiency is improved.

The invention relates to the technical field of shot action recognition, and discloses a shot throwing action recognition method based on a single attitude sensor. The method comprises the following steps: acquiring a single attitude sensor original data stream in a shot throwing process, and extracting a three-axis acceleration sequence and a three-axis angular velocity sequence from the single attitude sensor original data stream; performing motion event segmentation processing on the two sequences, and generating a key action time period mark set comprising a preparation stage, a sliding step stage, a power generation stage and a release stage; based on the mark set, calling an attitude feature solution algorithm to perform spatial trajectory reconstruction on the original data stream to obtain a key attitude frame set containing a joint angle change curve and a centroid displacement trajectory; and inputting the key attitude frame set into an action recognition model for attitude mode matching, and outputting a shot throwing action classification result which comprises an action deviation parameter generated based on a standard action template library and a key frame correction identifier. According to the method, key attitude information is reconstructed, and support is provided for shot throwing action analysis and improvement.

The invention provides a potential information identification method and system in combination with a heterogeneous network environment, and the method comprises the steps: firstly obtaining a heterogeneous network environment data set of a target network environment, which comprises original information resources of various data formats, and carrying out the feature extraction of the heterogeneous network environment data set to obtain an associated feature set; performing hierarchical pattern matching processing on the associated feature set based on a pre-trained information pattern analysis model to generate a potential information identification result set containing significant value information resource attribute identifiers and distribution paths, and generating a dynamic resource integration strategy according to the potential information identification result set; and the resource management system is used for adjusting a resource scheduling rule and a storage path mapping relation, finally feeding back a dynamic resource integration strategy to the data management system, triggering a resource directory updating operation, and improving heterogeneous network resource utilization and management efficiency.

Monitoring is performed for the activation of a set of one or more previously attached Kprobes. A determination is made that a strategy pattern match has occurred. The strategy pattern comprises a set of one or more behaviors including the activation of the at least one Kprobe included in the set of Kprobes. A remedial action is taken in response to the determination. Examples of such remedial actions include generating an alert and terminating a network connection.

The invention provides an AC mode matching tree construction method and device and an AC mode matching tree matching method and device.In one example, the AC mode matching tree construction method comprises the steps that according to a mode set, the mode that all modes in the mode set are inserted into a dictionary tree is adopted, and an AC mode matching tree is constructed; according to a breadth-first traversal mechanism, state identifiers are distributed to all nodes of the AC mode matching tree in sequence; and according to a breadth-first traversal mechanism, for a plurality of different sub-nodes of the same node, according to characters corresponding to edges, connected with the different sub-nodes, of the node, and according to an ASCII code sequence, rearranging state identifiers for the plurality of different sub-nodes to obtain a final AC mode matching tree. By applying the embodiment of the invention, the memory access performance of the AC mode matching tree can be optimized.

The invention relates to the field of environmental monitoring, and discloses a comprehensive soil detection method, which comprises the following steps: S1, deploying a multi-parameter sensor network, and dynamically configuring node density and a communication protocol according to soil heterogeneity and environmental interference factors; s2, abnormal data cleaning of three-level joint verification is executed, wherein single-parameter statistical verification, multi-parameter materialization association verification and historical pollution mode matching are included; s3, realizing adaptive sampling control by adopting a three-layer decision model, wherein the adaptive sampling control comprises rule driving adjustment, reinforcement learning optimization and sudden pollution emergency response; and S4, based on dynamic weight distribution and nonlinear effect modeling, calculating a soil health index (SHI). According to the invention, through a heterogeneity-driven node density adaptive technology and in combination with LoRa-ZigBee dual-mode communication dynamic switching, compared with an existing single protocol networking scheme, the problem of data fault caused by signal attenuation under a complex terrain is solved.

The invention discloses a feature fusion processing method for anesthesia depth multi-modal data, and belongs to the technical field of graphic data processing and pattern recognition, and the method comprises the steps: obtaining a multi-modal physiological signal and an electromyographic signal of a patient; performing time axis calibration on the physiological signal to generate an alignment signal; extracting a multi-modal feature vector and calculating an anesthesia depth index; performing deviation analysis on the basis of the electromyographic signal and the index to obtain an electromyographic response deviation index; performing graphical feature mapping on the real-time electroencephalogram signal to generate a real-time time-frequency map; when the deviation index exceeds a safety threshold value, performing graph pattern matching with a pattern template library to calculate a similarity score; and outputting the current anesthesia state mode in a classified manner. According to the method, a multi-modal signal graphical feature fusion technology is adopted, and a dynamic map generation and pattern matching mechanism is combined, so that the problem of complex pattern recognition of physiological signal graphic data can be solved, and the accuracy and timeliness of anesthesia state classification are improved.

A computer-implemented system for multidimensional generative value assessment and applicant selection, consisting of: a data collection unit configured to electronically receive applicant data consisting of structured academic records, work experience records, digital documentation, and unstructured narrative responses generated from generative self-assessment instruments and contextual interviews; a feature extraction unit coupled to the data acquisition unit, configured to apply computer-assisted text processing, semantic analysis, and token-level attribute identification to transform narrative responses and structured data into multidimensional feature vectors that represent generative indicators of innovation, mentoring, collaborative performance, resilience, social contribution, ethical consistency, and predicted institutional impact; a weighting calculation unit configured to assign weight values ​​to the extracted feature vectors based on a digital generative profile definition matrix that includes dimensions, sub-criteria, indicators, documentation requirements and importance coefficients, with the weighting being distributed across the generative dimensions defined in the digital matrix and configurable according to the institutional context; a quantitative rating unit configured to calculate a generative rating score by aggregating weighted feature vectors derived from self-assessment inputs, interview-based ratings, document analyses, and authenticity predictions, with the aggregation including normalization, nonlinearity correction, conflict handling, and artifact frequency balancing to obtain a consolidated score; a proof verification unit configured to electronically validate referenced digital evidence by performing content extraction, metadata verification, pattern matching, and cross-document correlation to determine authenticity, credibility, and contextual relevance with respect to the calculated feature vectors; a classification determination unit configured to assign a classification level to an applicant by comparing the generative assessment score with a set of system-defined calculation thresholds, including at least a lower threshold, a middle threshold and an upper threshold, the classification levels representing different generative maturity states and determining subsequent eligibility for selection; a decision generation unit configured to produce a digital output data set that includes classification level, feature aggregation summaries, evidence validation results, and recommended organizational actions, wherein the decision generation unit encodes the data set in a digitally signed, tamper-proof format and stores it on a non-volatile storage medium; and A system control unit acts as an operational interface to all other units and is configured to orchestrate data flow, scheduling, process state transitions, and event logging to ensure verifiable traceability, consistency, and auditability of the evaluation and selection processes.