1353 results about "Confidence threshold" patented technology

The invention discloses a vehicle-mounted Beidou positioning deviation self-calibration system fused with inertial navigation information, and relates to the technical field of navigation and positioning. The method is used for solving the problems of positioning deviation accumulation and signal failure in a complex environment. The system realizes space-time alignment of inertial navigation and Beidou observation data through timestamp interpolation and coordinate system conversion, and constructs a multi-source synchronous data stream. A difference value between an inertial navigation calculation speed and a Beidou Doppler speed is analyzed based on a vehicle incomplete constraint model, and a weight factor is generated in combination with a confidence threshold to suppress abnormal observation. A geometric matching error is calculated through a high-precision lane map curvature and a vehicle steering angle, and candidate tracks are screened to generate transverse calibration parameters. The noise covariance is dynamically adjusted through a tight coupling filtering algorithm, the mode is switched to a lane constraint calibration mode when a Beidou signal is interrupted, inertial navigation errors are compensated through error boundary constraint, and stable output is achieved. Multi-source data deep fusion and adaptive robust calibration are realized, and system robustness and positioning continuity are improved.

The present disclosure relates to the technical field of target detection, and particularly relates to a model deployment method, an end-side device and a storage medium, which are used for solving the problem in the related art of the accuracy of a deployed model being low. The method comprises: performing target detection on a video frame image input into a first model, and acquiring a first target detection result and a first confidence; if the first confidence is greater than or equal to a first confidence threshold value, recording the video frame image and the first target detection result as samples in a training set; if the first confidence is less than the first confidence threshold value, performing target detection on the video frame image on the basis of a second model, and recording the video frame image and an acquired second target detection result as samples in the training set; and training the first model on the basis of the training set, and replacing the current first model with a trained first model for subsequent target detection. In this way, the accuracy and model generalization capability of a first model are improved.

The invention discloses a building electromechanical BIM model information rapid retrieval method and system, and the method comprises the steps: generating composite retrieval parameters fusing semantic keywords and three-dimensional coordinate constraints according to a multi-mode retrieval instruction inputted by a user; on the basis of the composite retrieval parameters, constructing a dynamic search space by utilizing a hierarchical graph convolutional network, and generating a candidate model index structure of multi-dimensional feature coding; inputting the candidate model index into a multi-objective optimization engine, performing real-time optimization on a search path by adopting a dynamic pruning algorithm driven by reinforcement learning, and outputting a candidate model set of which the confidence coefficient is higher than a preset confidence threshold after pruning; and on the basis of the candidate model set, associated equipment nodes are expanded through a knowledge graph embedding and complementing technology, and an enhanced retrieval result set containing the hidden associated equipment is generated. By utilizing the embodiment of the invention, efficient, multi-dimensional and multi-modal information accurate positioning and quick retrieval can be realized in a large-scale complex BIM model.

The invention provides a multi-round dialogue interaction method and system based on context reconstruction and multi-library retrieval, and the method comprises the steps: firstly obtaining a multi-round dialogue data set of a target user, which comprises a plurality of dialogue round sequences composed of user input statements and system response statements; performing context reconstruction processing on the multi-round dialogue data set to generate context reconstruction characteristics of each dialogue round sequence, covering current round semantic focus offset and historical round semantic dependence intensity, and retrieving a matched multi-database retrieval result from a preset heterogeneous database based on a dynamic context window, a candidate response statement set and confidence are included. Performing multi-strategy matching on context reconstruction features and retrieval results, generating a response generation strategy to adjust candidate response semantic priorities and confidence thresholds, and finally feeding back the response generation strategy to a dialogue service system to trigger response optimization operation, and updating heterogeneous database retrieval weights and context window dynamic adjustment parameters.

An AI-driven financial planning system for real-time market adjustment, consisting of: a neural inference coprocessor configured to execute deep financial forecasting models, including recurrent neural networks and attention-based encoders, on the device, and wherein the processor dynamically updates portfolio parameters in response to market signals exhibiting volatility differences above a statistical threshold calculated using an exponentially weighted moving standard deviation; a financial data acquisition module configured to continuously receive and analyze heterogeneous data streams, including market indices, interest rates, stock and bond price fluctuations, economic indicators, regulatory updates, and financial news sentiment feeds; a behavioral analytics engine configured to create a dynamically evolving user-specific financial behavior profile based on real-time analysis of transaction history, income-expenditure cycles, psychometric test results, and temporal lifestyle patterns using supervised and unsupervised machine learning algorithms; A goal optimization module configured to transform high-level, user-defined financial goals into quantitatively tracked multi-level goals. It uses a reinforcement learning framework that predicts optimal asset allocations across multiple time horizons. a real-time strategy simulation engine configured to perform Monte Carlo simulations and deep Q-learning-based assessments to simulate the resilience of proposed financial strategies under different macroeconomic regimes and trigger redistribution events based on predefined confidence thresholds; a compliance-aware execution interface configured to interact with financial institutions through encrypted API channels, ensuring policy enforcement using a smart contract validator and a hardware-enabled secure transaction signing unit; and a recommendation display unit configured to render dynamic dashboards for visualizing investments, reallocation warnings, confidence intervals, and sensitivity sliders, and where user interaction with the unit flows back into the behavioral model for real-time learning.

The invention relates to the technical field of water pollution traceability, and particularly discloses a river pollutant traceability system and method based on digital twinning, and the system comprises a water body sampling module which is used for obtaining the current water quality data of a preset region of a target river; the model construction module is used for constructing a digital twinborn model according to the river topographic data and the current water quality data and by fusing the historical hydrological data, the real-time meteorological data and the drain outlet distribution topological graph; the data analysis module is used for performing spatial-temporal feature extraction on a pollutant diffusion path in the digital twinborn model by utilizing a graph convolutional neural network, applying dynamic correction pollution source position probability distribution in combination with Bayesian inference, and generating a traceability path thermodynamic diagram; and the pollution traceability module is used for obtaining a pollutant traceability result according to the confidence coefficient threshold of the traceability path thermodynamic diagram. According to the method, the efficiency and the accuracy of tracing the river pollutants in the complex dynamic environment can be remarkably improved, and technical support is provided for ecological safety and precise treatment of a drainage basin.

The invention relates to the technical field of text classification, and provides a text classification method based on a large model and a rule engine, and the method comprises the steps: S1, storing multi-level rule classification labels, and constructing a classification rule template library; s2, receiving text data from various data sources, and preprocessing the text data; s3, performing rule matching on the text data based on the classification rule through a rule engine, and outputting a rule classification result; and S4, when any one of the following conditions is met, large language model classification is triggered: a, a classification rule is not matched; b, matching a classification rule, wherein the rule confidence is smaller than a rule confidence threshold; c, the text data length exceeds the preset text data length; d, matching a specific business scene label; outputting a model classification result; and S5, when the rule engine classification in the S3 and the large language model classification in the S4 are parallel, executing the strategy. The output reliability and the service continuity are guaranteed, and the method is suitable for scenes with high accuracy requirements such as financial compliance examination and the like.

The invention provides a thin film surface defect detection method and system, and relates to the technical field of defect detection.According to the thin film surface defect detection method and system, an intelligent secondary verification link is constructed by introducing a defect confidence evaluation mechanism based on form and energy distribution, so that the detection performance is fundamentally improved; according to the mechanism, real physical defects with regular forms and concentrated energy and pseudo defects caused by electromagnetic interference, instantaneous film wrinkles and the like can be accurately distinguished, and the problem of high false alarm rate caused by dependence on single signal strength in the prior art is effectively solved while the high detection rate of low-contrast defects is reserved; besides, the judgment model based on physical characteristics has natural robustness for background noise generated in high-speed motion, and an adjustable confidence threshold value endows the system with extremely high practical flexibility, so that the system can adapt to complex and changeable industrial environments and different quality control standards, and the method is suitable for large-scale popularization and application. And the accuracy, the reliability and the intelligent level of the whole detection system are obviously enhanced.

An apparatus and method for static image of time series measured data to time series translation is disclosed. The apparatus comprises at least a processor configured to receive a static image of time series measured data, convert that static image from its initial domain to a usable time series within another user-selected domain, then to validate the conversion against a confidence threshold.

The invention relates to the technical field of voice interaction, particularly discloses an AI intelligent robot voice interaction method and system, and aims to solve the problems of low voice interaction accuracy, insufficient reliability and lack of authority control in a complex noise environment. A dynamic noise feature library containing steady-state noise, impact noise and human voice interference features and a pre-stored gesture instruction library are constructed, audio signals are collected in real time, low-frequency-band, middle-frequency-band and high-frequency-band differential noise reduction is executed, Mel-frequency cepstral coefficient features are extracted, noise scenes are matched, corresponding voice recognition models are switched, and voice recognition is achieved. And calculating a confidence value of the voice instruction, outputting multi-modal verification data in combination with a dynamic confidence threshold, and outputting an authority control signal through voiceprint matching, authority verification and instruction consistency judgment. Through multi-modal fusion, dynamic adaptation and authority control, the voice recognition accuracy and interaction safety in a complex noise environment are remarkably improved, and the method is suitable for scenes such as factory intelligent inspection.

The invention discloses a multi-path thinking chain reasoning generation method based on fine-grained knowledge retrieval, which comprises the following steps of: performing deep semantic analysis on a user question by adopting a large language model, and extracting a relationship between entities contained in a question text to generate a triple set; the triple is dynamically divided into different confidence sets by setting high and low confidence thresholds; constructing a thinking chain framework by using a predefined path to obtain an initial reasoning path set; optimizing the initial reasoning path, and sampling an optimized reasoning path set by using Top-k scoring to obtain a reasoning path set with high semantic relevancy; dividing the reasoning path set into a plurality of reasoning path groups with complementary internal path information to obtain a plurality of candidate answers; and adopting a majority voting mechanism to select a plurality of candidate answers, and finally obtaining consistent answers. According to the method, the reasoning accuracy and interpretability can be remarkably improved, and the reliability and robustness of a final conclusion are greatly improved.

A generative mediator engine can perform a requested interaction with a conversational agent of a target entity on behalf of a user. An internal conversational platform can identify intents for the requested interaction. An external artificial intelligence engine can perform intent discovery when an intent is not identified above a confidence threshold. A discovered intent unknown to the generative mediator engine can be received from the external artificial intelligence engine and used, with input requirements determined by the generative mediator for the requested interaction, by a dialog generator to generate a sample dialog for the requested interaction. User feedback can be received after review of action items and expected inputs identified from the sample dialog. The generative mediator engine can perform the requested interaction with the conversational agent on behalf of the user and without receiving user intervention during the requested interaction.

The invention discloses a voice interaction system and method based on an artificial intelligence technology, and relates to the technical field of voice interaction, and the method comprises the steps: employing the double-layer cooperative recognition of a main channel Transform model and an auxiliary channel CTC-Attention mixed architecture, obtaining a text with a multi-dimensional feature label, carrying out the scene classification and dependency syntactic analysis through a bidirectional recurrent neural network, and obtaining a multi-dimensional feature label text; retrieving associated entities in real time and generating structured intention data in combination with a dynamic knowledge graph, executing three-dimensional analysis through an interpretability engine and generating a multi-dimensional traceability report, and triggering an intelligent decision fusion engine through a confidence threshold to generate structured enhanced interaction data; according to the method, scene classification and dependency syntactic analysis are carried out through the bidirectional recurrent neural network, the associated entities are retrieved in real time in combination with the dynamic knowledge graph, and the structured intention data are generated, so that the intention of the user is accurately understood, and personalized interaction services are provided.

The invention relates to a self-healing operation and maintenance method and device for a big data component, equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing and feature fusion of multi-modal data of the big data component, obtaining and inputting a high-dimensional component health state vector into a fault diagnosis large model, and outputting a target fault type and a root cause confidence coefficient; when the root cause confidence coefficient meets a preset confidence coefficient threshold value, determining a historical restoration strategy based on the target fault type, calculating cluster environment similarity based on the cluster environment characteristic parameters of the current cluster environment and the historical restoration strategy, and if the cluster environment similarity meets a preset cluster environment similarity threshold value, determining that the current cluster environment is abnormal. If yes, taking the historical restoration strategy as a candidate restoration strategy; if not, a candidate repair strategy is generated based on a reinforcement learning strategy generation engine, and the candidate repair strategy is executed based on the strategy level matched with the candidate repair strategy; compared with the prior art, the technical scheme of the invention can effectively solve the problems of leak detection, low efficiency, solidification and the like of traditional operation and maintenance.

The invention relates to the technical field of software test automation, in particular to a self-repairing UI automatic detection method based on multi-modal fusion, which mainly comprises the following steps: extracting visual features to obtain feature vectors, and constructing a virtual DOM tree with semantic tags to carry out similarity calculation to obtain structural similarity; and carrying out weight dynamic adjustment on the feature vector and the structural similarity through a feature fusion decision maker, outputting a fusion weight proportion under a current confidence threshold, fusing the feature vector and the structural similarity through the weight proportion to form a decision combination, and generating a new locator. Through the method, the problem of test case vulnerability caused by a dynamic user interface is solved, the detection speed of real-time sensing of interface change is improved, and meanwhile, the positioning success rate during use of a new positioner automatically generated by a positioning strategy is also remarkably improved.

The invention relates to an audio data processing method and device, electronic equipment and a vehicle, and is applied to the technical field of computers. The audio data processing method comprises the following steps: acquiring target voice data of a user; obtaining at least one grammar element corresponding to the target voice data, and determining a grammar structure integrity score corresponding to the target voice data based on the at least one grammar element; determining an environment interference score corresponding to the target voice data; determining a confidence score corresponding to the target voice data based on the grammatical structure integrity score and the environmental interference score; and under the condition that the confidence score is greater than or equal to the target confidence threshold, determining to execute a target control instruction corresponding to the target voice data, thereby determining whether the target voice data is an instruction for vehicle control by combining the grammatical structure integrity of the voice data and the environmental interference degree. The accuracy of vehicle control and the interaction experience of the user and the vehicle are improved.

The invention discloses an image recognition method and system based on multi-modal data fusion, and the method comprises the steps: synchronously collecting image data and non-image modal data for a target scene, carrying out the preprocessing of the image data and the non-image modal data, inputting the preprocessed data into a feature fusion network, and carrying out the recognition of the feature fusion network; layered visual features of image data are extracted through image branches of the feature fusion network, modal features of non-image modal data are extracted through modal branches, the extracted features are fused through an interaction layer, and a fusion feature vector containing cross-modal associated information is formed; and inputting the fusion feature vector into a classifier to identify information and confidence of a collection object in the target scene, and when the confidence is lower than a confidence threshold, performing feature extraction and recognition again to form collection-fusion-recognition-feedback closed-loop optimization. In this way, the internal relation and complementarity between different modal data are fully considered, so that the accuracy of image recognition is improved, and the adaptability of the recognition process is enhanced.

The invention provides a commodity identification method and device, and the method comprises the steps: carrying out the multi-view collection and preprocessing, and constructing a second color image set and a depth image set; performing three-dimensional reconstruction and volume estimation based on the second color image set and the depth image set, and judging a commodity packaging form; extracting a commodity packaging semantic and price information area based on the second color image set, constructing a space-semantic joint code, and generating global packaging semantic features; based on the second color image set, a multi-view visual coding network is adopted, and a visual prediction SKU index is output; carrying out structure-semantic fusion modeling based on multi-source heterogeneous features, and outputting commodity category labels; and matching a commodity price based on the commodity category label, generating an interaction prompt sign in combination with a confidence threshold, and triggering interaction feedback. According to the invention, commodity vision, space and price activity information are fused, the identification accuracy and price verification intelligence are improved, and the method is suitable for the field of intelligent cashier and retail automation.

The invention relates to the field of electric power material warehouse management and control, in particular to an electric power material digital intelligent professional warehouse management and control method, which comprises the following steps: acquiring electromagnetic spectrum data, identifying a central frequency point energy distribution diagram and an electromagnetic interference intensity level, constructing a three-dimensional electromagnetic field intensity distribution model of a professional warehouse, and dynamically adjusting the working mode of an RFID reader-writer; the method comprises the following steps: deploying a multi-modal check node in a storage area of a professional warehouse, and generating a composite check result through an infrared contour matching method and a UWB coordinate verification method when the confidence coefficient of electric power material data read by an RFID reader-writer is lower than a preset confidence coefficient threshold value; updating an electric power material inventory database according to a composite verification result, and performing electric power material inventory state analysis in real time through an edge computing node; the problems that inventory data distortion and material positioning deviation are caused by reduction of the accuracy rate of RFID in a strong electromagnetic environment, and inventory accuracy management of an electric power material warehouse is seriously restricted are solved.

Systems and methods for training and using large language models to respond to queries and cascading to new models when their performance falls below a threshold during the response generation cycle are described. The methods involve receiving and analyzing a query using heuristics to determine the query's categories and a level of granularity at which its response is to be evaluated. Selecting a language model based on the query analysis and during the generation of the response, evaluating the model's performance for its ability to predict the next segment in a response. The methods score the evaluation and use the score to determine whether the language model's performance exceeds a confidence threshold. If it does not, then during the response is being generated, e.g., at a point before the response generation is completed, cascading from the model to a different model that can perform at a higher confidence level.

The invention discloses a multi-modal perception and optimization method and system for a low-power-consumption AR device, and the method comprises the steps: collecting multi-modal data, task demands, resource state data and environment data for the AR device; lightweight processing is carried out on the multi-modal neural network model through model pruning, parameter quantification and distillation technologies; inputting the collected data into a lightweight multi-modal neural network model for dynamic reasoning to obtain a multi-modal recognition result; comprising the steps of executing modal adaptive weight acquisition based on task requirements and environment data; executing energy consumption constraint scheduling according to the equipment resource state data, dynamically selecting a reasoning path strategy, and obtaining corresponding modal feature output; multi-modal feature fusion is carried out, task reasoning is completed, and a multi-modal recognition result is obtained; early-leaving control is executed based on a middle-layer confidence coefficient threshold value in the reasoning process; and interactively outputting a real-time multi-mode identification result. According to the invention, energy efficiency and precision balance and multi-mode fusion low-power-consumption optimization can be realized.

The invention relates to the technical field of artificial intelligence and material engineering, and discloses a formula optimization method and device, equipment and a storage medium, and the method comprises the steps: carrying out the knowledge extraction of an obtained structured formula data set and unstructured technical literature data in response to a formula optimization target input by a user, and generating a table literature knowledge set; inputting the table literature knowledge set and the formula optimization target into a large language model to obtain a generated text corresponding to the formula optimization target; performing logic rule screening and risk assessment on the generated text through a knowledge fusion layer to obtain text output conforming to a confidence threshold, and generating a new formula scheme according to the text output; and performing multi-objective optimization on the new formula scheme based on a preset experimental cost constraint condition, and outputting an optimized recommended formula and a support evidence chain. By automatically fusing innovative components and process information in literatures, the output recommended formula has scientific basis and interpretability, and the practicability and innovativeness of an automatic formula are improved.

The invention relates to the technical field of artificial intelligence and the technical field of semiconductor detection, and discloses a wafer defect identification method based on feature fusion, which comprises the following steps: acquiring a first type of image and a second type of image of a wafer detection area, and performing alignment processing; performing feature extraction on the first type image and the second type image to generate a first type feature and a second type feature; fusing the first type of features and the second type of features to generate fused features; filtering the fusion features, and screening target fusion features meeting a confidence threshold condition; and determining a defect position and corresponding process layer information based on the target fusion feature. According to the invention, features of different types of images are fused, so that the characterization capability of wafer surface defects is enhanced, and the recognition precision of a detection system is improved; by screening the target fusion features meeting the confidence threshold, false detection and missing detection are reduced, and the detection reliability is improved.

The invention relates to the field of visual processing, and provides a visual classification processing method and device based on large model and multi-modal data fusion. The method comprises the following steps: inputting a to-be-classified input image and a corresponding category text description into a text encoder for multi-level feature extraction to obtain global text features and local text features; performing fine-grained cross-modal alignment on the local visual features and the local text features, calculating association weights between the image regions and the text phrases through a bidirectional cross attention mechanism, and generating aligned intermediate features; splicing and fusing the aligned middle features and the global visual features, and inhibiting background noise in a fusion result and reinforcing discriminative features in the fusion result through a feature mask algorithm in combination with the global text features to obtain multi-modal fusion features; and synchronously inputting the multi-modal fusion features into a multi-space classifier to generate respective classification results, and adaptively outputting an image classification result according to a confidence threshold in combination with a dynamic routing mechanism.

The invention relates to the technical field of artificial intelligence and medical decision systems, and discloses a reasoning agent construction method and system for intelligent medical treatment guidance, and the method comprises the steps: constructing a hierarchical modal completion network and a modal correlation knowledge graph; constructing a medical feature cross-modal mapping network based on comparative learning, and mapping different modal medical data to a shared feature space; according to the uncertainty of the complemented data, constructing an uncertainty quantitative model and automatically adjusting a diagnosis confidence threshold; aiming at different disease types and symptom combinations, constructing a disease modal incidence matrix and a modal reliability evaluation network; through weighted voting, evidence convergence and specialist authority evaluation, cross validation and collaborative decision-making of multiple specialist knowledge are realized, and a final diagnosis suggestion is formed; according to the invention, the problem of limited diagnosis capability caused by lack of medical data in a medical resource limited environment is solved.

Artificially intelligent systems and methods for financial coaching provide personalized, fiduciary-compliant financial guidance through advanced machine learning architectures with measurable performance criteria. The systems implement privacy-preserving processing pipelines that detect personally identifiable information using multi-layered pattern recognition including regular expressions for formatted data sequences, named entity recognition with confidence thresholds above 0.85, and contextual analysis algorithms. A multi-step artificial intelligence processing workflow includes automated language detection, emotional tone classification with confidence scoring, financial profile transformation using predefined templates, context-aware question rephrasing, and semantic similarity matching employing vector embeddings with financial domain vocabulary weighting applying multiplier values between 1.3-2.0. Specialized training methodologies expand datasets through mathematical transformation functions utilizing statistical standard deviations with incremental variations between 0.5-2.0. Mood-based escalation logic automatically transfers users to human advisors when emotional indicators exceed confidence thresholds above 0.8. The systems maintain response times below 5 seconds while providing regulatory compliance through curated content sources and predefined fiduciary instruction parameters.

According to some embodiments, a method includes accessing yard check data received from a plurality of sources. The yard check data includes a unit identification number of a particular shipping container of a plurality of shipping containers, a location of the particular shipping container within a container yard, and a confidence level. The method further includes storing the unit identification number and the location of the particular shipping container in a unit inventory of an inventory database. The method further includes comparing the confidence level of the particular message to a confidence threshold for the particular source. The method further includes electronically communicating, across a communications network, the unit identification number and the location of the particular shipping container for storage in a production database when the confidence level of the particular message exceeds the confidence threshold for the particular source.

The invention discloses a multi-platform data synchronization-based commodity middle platform management system and method, and relates to the technical field of data synchronization management, and the method comprises the steps: configuring card and coupon basic information and selling information at a middle platform background, creating a coupon mother set data model, generating a valuable coupon data model adaptive to a platform based on the coupon mother set, expanding the specific field of the platform, and carrying out the management of the middle platform; unified management and cross-platform synchronization of commodity information are achieved, meanwhile, commodity data are distributed to all platform interfaces through a GBS layer, a platform side auditing process is triggered, timing sequences are synchronized based on interface response portraits and user active period model decisions, timely synchronization and auditing efficiency of the commodity information are ensured, in addition, the auditing state is monitored in real time, and the auditing efficiency is improved. The method comprises the steps of performing automatic rollback on conflict operation, responding to platform rule change, triggering commodity off-shelf or information updating, calculating user cancel-after-verification confidence coefficient in real time in a user cancel-after-verification link, and triggering a dynamic cancel-after-verification strategy when the confidence coefficient is lower than a confidence coefficient threshold value, thereby effectively preventing and controlling risks.

The invention discloses a multi-stage loss optimization OCTA (Optical Coherence Tomography Angiography) image diagnosis method based on semi-supervised learning and knowledge distillation. The method comprises the following steps: (1) constructing a teacher-student model framework in combination with semi-supervised learning; (2) data set processing: randomly removing part of sample labels of the training set as label-free data; (3) training a teacher model by using the labeled data, then generating a pseudo label of non-labeled data through the teacher model, and only retaining a high-quality pseudo label meeting a confidence threshold; and (4) training a student model by using a multi-stage loss function, wherein the student model comprises supervision loss of label data, supervision loss of pseudo-label data and knowledge distillation loss. Dependence on annotation data is reduced through semi-supervised learning, the performance of the lightweight student model is improved through knowledge distillation, and the method is suitable for scenes with limited clinical resources.

A structural building design system for processing, interpreting and analysing holistically a multipage set of two-dimensional (2D) real-world building construction plans for a building and yielding near real time accurate material type, quantity, and specification outputs as required for construction of a compliant building from said plans, through computationally generating a mathematical feature vector space dataset, the system comprising: one or more processors configured to: receive a two-dimensional real-world architectural plan for construction of a structural building, wherein the two-dimensional real-world architectural plan includes objects comprising: architectural symbols, lines, shading, or text; perform pre-processing, of characteristics on or associated with the objects on the two dimensional real-world architectural plan on a pixel by pixel basis for measurement or adjacent multi-pixel basis for object detection, wherein the pre-processing comprises two or more of: object detection and recognition, semantic segmentation, or text recognition to identify a plurality of objects, on the two dimensional real-world architectural plan to identify the characteristic features thereof; computationally transforming at least one characteristic feature of known (learned) and unknown (unlearned) detected objects into a mathematical representation thereof to form part of a future vector space dataset; and wherein said transformations for learned and unlearned detected objects have both high level and low level classifications of identified characteristic features; performing a comparison of the future feature vector space dataset for the identified detected objects to confirm the detected objects meet a predetermined confidence threshold for the classification of each detected object; performing a correlation analysis of the characteristic features for detected objects meeting the pre-determined confidence threshold level, wherein the comparison and correlation analyses above include one or more of determining shape, position, adjacent objects and using said pre-processing and results of correlation analysis of the feature vector space dataset via the algorithms to provide output information regarding the 2D plan including producing autonomous, and highly accurate creation of at least one or more of the following outputs: near real time accurate building takeoffs; complete construction estimates; complete construction detailing; detailed bill of materials for the construction of the building; or a document summarizing differences or similarities between building plans.