361 results about "Value assessment" patented technology

The invention provides a spartina alterniflora ecological environment damage value assessment method, medium and system, and belongs to the technical field of spartina alterniflora ecological environment damage assessment. A time-space damage analysis model based on a graph convolutional network is utilized to process a complex factor interaction relationship, a time weight factor correction model is established to quantify the dynamic change of a cumulative effect, and a biological inhibition factor effect model is combined to analyze the synergistic effect of multiple inhibition mechanisms such as local reed competition, natural enemy insect control, salt stress and the like. Accurate damage quantification and prevention and control strategy formulation are realized through the regional ecological environment damage value comprehensive evaluation model and the inhibition factor optimal configuration model, and the technical problem that the accuracy of ecological damage value evaluation is insufficient due to the fact that a multi-ecological factor interaction mechanism in the spartina alterniflora invasion process cannot be effectively analyzed in the prior art is solved.

The invention discloses a server fault diagnosis method and device, a storage medium and a program product, and relates to the technical field of server fault localization, and the method comprises the steps: carrying out the preprocessing of original log data, determining a state space according to the obtained preprocessed log data, a fault classification model containing a value network and an action network is obtained through reinforcement learning in advance, the value network in the fault classification model is used for carrying out value evaluation on the state space, and the action network in the fault classification model is used for carrying out action probability distribution calculation on the state space according to a target value obtained through value evaluation; and determining the target fault type according to the calculated probability distribution result, thereby solving the technical problem that new features or anomalies cannot be effectively judged, effectively improving the quality and integrity of log data, improving the server fault diagnosis efficiency and accuracy, realizing intelligent identification of the fault type, and improving the fault diagnosis efficiency and accuracy. The method has the technical effects of good training stability and generalization ability.

The invention discloses an AI-based enterprise special asset diversified value-added service method and system, and the method comprises the steps: receiving multi-source heterogeneous data of enterprise special assets, and obtaining an enterprise special asset data graph containing asset attributes, market environments and historical disposal records; inputting the enterprise special asset data atlas into a pre-trained AI-based deep value evaluation model, and generating an asset value evaluation report including basic values, potential value-added paths and risk factors; according to the asset value evaluation report, utilizing a multi-target optimization algorithm to generate a plurality of preliminary value-added scheme sets for different value-added targets in parallel; and inputting the initial value-added scheme set into an intelligent matching engine, carrying out scheme feasibility verification and suitability screening, and outputting an optimal customized value-added service scheme package. By utilizing the embodiment of the invention, the accuracy of asset value analysis and the potential mining efficiency can be improved, and dynamic optimization and risk controllability of an asset value-added path are realized.

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.

The invention discloses a news image value evaluation method and system based on predicate logic and multi-modal fusion, and the method comprises the steps: obtaining multi-modal data of a news image, and extracting an audio and visual data key frame through a self-adaptive triggering mechanism; semantic analysis is performed on the key frame through a predicate logic analysis engine, a subject-action-scene triple is generated in combination with text data, and dual verification is performed through spatial position and semantic association; calculating a news value score of the verified triple data based on a three-dimensional dynamic evaluation model; the three-dimensional dynamic evaluation model fuses propagation intensity, information entropy compensation and ontology value; and performing visual adjustment and optimization on evaluation parameters and rules to realize optimization of the dynamic evaluation model. According to the method, massive multi-modal data can be efficiently processed, and the response time to emergencies is greatly shortened; the overall event checking efficiency is improved; the evaluation result is accurate, efficient, transparent and reliable, and the user can understand and trust the model decision conveniently.

The invention discloses an energy storage power station multi-agent collaborative management method based on deep reinforcement learning, and relates to the technical field of intelligent control. The energy storage power station multi-agent collaborative management method based on deep reinforcement learning comprises the following steps: S1, acquiring energy storage collaborative perception data, and preprocessing the energy storage collaborative perception data; s2, dividing three types of control units, evaluating the operation coordination deviation degree of the energy storage power station, dynamically selecting the action strategy of each control unit, and constructing an action value evaluation model; s3, constructing a state, action and reward triple sample, and iteratively optimizing the action value evaluation model round by round; s4, deploying each control unit into a simulation operation environment, and executing action decision and state interaction feedback; after each action decision is executed, whether a strategy correction mechanism is triggered or not is judged. The problems that subsystems in an existing energy storage power station are not coordinated in time, battery state estimation errors are difficult to feed back in time, and then strategy lag and equipment operation imbalance are caused are solved.

Disclosed herein are computer-implemented systems and methods for code value assessment. For example, in one aspect, the system comprises, an input module configured to receive program code submissions from developers, a processing unit equipped with Text-Based Models with Contextual Understanding (TBM-CUs) configured to evaluate the functional meaning, purpose, and value of submitted code segments and distinguish between code contributions from human programmers and machine learning systems, a visualization module configured to present the assessed value of the analyzed code over various time periods and dimensions, offering insights into trends, patterns, and comparative performance, a communication module configured to translate programming code meaning or function into plain language summaries for non-technical stakeholders, and an AI peer review module configured to automatically review, accept or reject code contributions.

The invention relates to the technical field of patent value evaluation. The invention provides a Markov decision-based patent value evaluation method, which is used for constructing a state-action-reward-transfer decision-making model for patent life cycle dynamic characteristics. Through multi-source data processing, state construction, strategy screening, value function iteration and optimal strategy generation, quantitative evaluation of patent dynamic values and operation suggestion output are realized, and the problem that strategy evolution and dynamic earnings cannot be described by an existing method is solved.

The invention provides an online value evaluation system based on real estate, and belongs to the technical field of online evaluation, and the system comprises an online data obtaining module which is used for obtaining a basic feature vector and multi-modal evaluation data of a target real estate online; the feature extraction and mining module is used for carrying out space mapping on the explicit features and the defect features and carrying out potential association mining on the implicit features, and obtaining high-association implicit description in combination with the basic feature vector; the evaluation processing module is used for identifying real evaluation, false evaluation and fuzzy evaluation, and correcting the fuzzy evaluation after preliminary analysis according to a spatial mapping result and high-correlation implicit description in combination with social platform subject discussion data; and the value evaluation module is used for obtaining a comprehensive value according to the basic value reference line, the real evaluation and the correction evaluation, and outputting a value evaluation report. The problems that real estate evaluation is difficult to distinguish virtuality and reality and is fuzzy and difficult to solve are effectively solved, and the evaluation result can be comprehensive and accurate.

The invention relates to the technical field of artificial intelligence and data processing, and discloses a digital customer value evaluation method and system based on artificial intelligence, and the method comprises the steps: obtaining customer behavior data, and processing the customer behavior data into a behavior vector sequence; calculating a customer behavior entropy representing the uncertainty of the next behavior of the customer and evaluating a potential value interval representing the value tendency so as to jointly form a two-dimensional dynamic state of the customer; according to the two-dimensional state, a differentiated intervention strategy mode is determined through a value gating decision-making mechanism, and an execution intervention action is generated; and tracking an intervention effect through a closed-loop feedback link, and carrying out self-optimization updating on a model in the system. According to the method, accurate and multi-dimensional dynamic perception can be carried out on the customer state, differential intervention is realized, the effectiveness of decision making is continuously improved through self-learning, and the automation and intelligence level of customer operation is remarkably improved.

The present invention relates to an artificial intelligence-based energy and power data asset evaluation method, comprising the following steps: S1: acquiring multi-source heterogeneous data from the energy and power industry; S2: constructing a database based on a time series database and distributed storage to store the multi-source heterogeneous data; S3: constructing an energy knowledge graph based on the multi-source heterogeneous data; S4: constructing a multidimensional value assessment model to quantify the economic benefits, quality indicators, usage, and potential mining capabilities of data assets; and S5: establishing a dynamic monitoring system to conduct real-time evaluation of the value of data assets throughout their life cycle based on the multidimensional value assessment model and the energy knowledge graph. This invention effectively improves the utilization efficiency of energy data assets.

The invention provides an urban rail transit station development intensity multi-objective optimization method in a TOD mode, and relates to the technical field of urban planning and intelligent transportation, and the method comprises the steps: analyzing the spatial relation between various types of land and urban function nodes according to the data after standardization processing, and carrying out the location value evaluation; constructing a development intensity evaluation model according to the location value evaluation, calculating the initial average plot ratio of each type of land, and deducing an initial development intensity parameter in combination with the current situation data; establishing a development intensity optimization target system according to a TOD planning principle, performing iterative optimization on the initial development intensity parameters by adopting a multi-target genetic algorithm, and outputting an optimal solution set; and carrying out comparative analysis on the optimal solution set and a current planning index, and obtaining a plot ratio index in combination with a regional development planning requirement. According to the method, a technical framework of subjective and objective collaborative optimization can be constructed by fusing GIS spatial analysis, an analytic hierarchy process and a non-dominated sorting genetic algorithm.

The invention discloses a data asset inventory and blood relationship analysis method, and belongs to the technical field of cloud computing and data asset management. The method comprises the following steps: constructing a unified asset information base by deploying a distributed acquisition agent cluster; performing unified semantic modeling on the metadata to generate a standardized data asset description file; analyzing the data processing log to construct a blood relationship map containing time sequence attributes and dynamic relationship strength; performing multi-dimensional anomaly detection and influence propagation analysis based on the atlas; and carrying out value evaluation and classification on the data assets in combination with topological importance, business value and risk level. According to the method, the problems of metadata dispersion, blood relationship static lag, abnormal influence assessment difficulty and the like in the prior art are solved, automatic checking, blood relationship dynamic tracking and asset value intelligent assessment of the data assets are realized, and the accuracy and decision support capability of data asset management are remarkably improved.

The invention discloses a plane 2D point cloud map updating method based on dynamic value evaluation, which belongs to the technical field of intelligent navigation, and comprises the following steps: acquiring sensor data of a current frame, and matching the sensor data with a currently maintained global map to obtain a matching result; based on the matching result, updating and evaluating the target unit in the map, and fusing an instant cleaning criterion based on space consistency and a long-term value criterion based on historical state information; according to a result of the update evaluation, determining an update action on the target unit, the update action at least comprising a deletion operation; an update action is performed to update the global map. According to the method, the accurate map updating decision is realized through intelligent fusion of instant and long-term double criteria, and the global scoring and fault-tolerant control mechanism is combined, so that the long-term consistency and high precision of the map are ensured, the operation efficiency and stability of the system are remarkably improved, and reliable and adaptive navigation support is provided for the robot in a dynamic environment.

The invention discloses an open source intelligence information value evaluation method and system, and belongs to the technical field of open source intelligence information value evaluation. The method comprises the following steps: acquiring open source intelligence information; calculating an information source score, an information quality score, an information applicability score, an information influence score and an information timeliness score of the open source intelligence information; and integrating the information source score, the information quality score, the information applicability score, the information influence score and the information timeliness score to obtain a value evaluation result of the open source intelligence information. The open source intelligence information value evaluation method can solve the problems of insufficient multi-dimensional analysis, non-uniform evaluation standards, low efficiency of the evaluation process and the like in the current open source intelligence information value evaluation, can efficiently and accurately perform value evaluation on the open source intelligence information by constructing a comprehensive evaluation index system and combining an intelligent algorithm, and improves the evaluation efficiency of the open source intelligence information. Therefore, information support with high reliability and practicability is provided for decision makers.

The invention relates to a digital auxiliary repairing system for a historical building. The system comprises a building data acquisition module, a data primary processing module, an automatic damage evaluation module, a repairing auxiliary model construction module and a historical building repairing auxiliary module. According to the invention, original data is obtained through data acquisition; a primary processing method of multi-source data alignment, building value evaluation, point cloud feature extraction and damage pre-labeling is adopted; an improved clustering model is adopted to carry out automatic damage assessment, a self-adaptive clustering mechanism is introduced, and a scientific basis is provided for repair decision making in combination with the historical value of the building; a repair decision transformer model is adopted as a repair auxiliary model, cultural relic protection specifications are converted into prior constraints, polar coordinate position coding is combined, perception of a building symmetric structure is enhanced, and finally collaborative optimization of a three-dimensional target is achieved.

The invention discloses an intelligent data value mining method and system, a storage medium and a computer program product, and relates to the technical field of data asset management.The method comprises the steps that an enterprise internal source data document is obtained, a pre-constructed localized deployment agent is adopted to analyze the enterprise internal source data document, and a key metadata set is extracted; calling a pre-trained dynamic consanguinity analyzer to perform consanguinity analysis on the key metadata set to obtain a data value evaluation matrix; loading a pre-trained asset prediction model to perform hierarchical prediction analysis on the data value evaluation matrix to obtain a table entry feasibility prediction result, the table entry feasibility prediction result including a table entry success rate and a recommended asset type; and generating an intelligent data value disclosure report based on the entry success rate and the recommended asset type. A localized deployment agent, a data asset prediction engine and a dynamic blood relationship analyzer are mutually combined, and the data asset value and compliance risk of an enterprise are efficiently and accurately evaluated.

The invention discloses a software value evaluation method and device, electronic equipment and a storage medium, and can be applied to the technical field of software data analysis. The first user behavior data of each piece of to-be-collected software is collected through the preset collection rule, the unified collection process of the user behavior data in all pieces of to-be-collected software can be achieved, the comprehensiveness of data collection is effectively improved, the sampling rule does not need to be independently set for each piece of to-be-collected software, and the data collection efficiency is improved. According to the method, the design and maintenance cost adopting rules is effectively reduced, then first user behavior data is preprocessed to obtain user behavior feature vectors, then aggregation operation is performed on the user behavior feature vectors through preset aggregation rules to obtain target aggregation data, and usage amount statistics is performed according to the target aggregation data to obtain a usage amount statistics result; and then software value evaluation is carried out according to a usage amount statistical result, so that the accuracy of software value evaluation can be effectively improved.

The invention relates to the technical field of intellectual property value evaluation, in particular to an intellectual property value evaluation method, which comprises the following steps of: establishing a composite feature representation system containing technical semantics, keywords and claims, establishing a dynamically updated industry hotspot vector library, and performing vector space alignment to obtain an intellectual property value evaluation result. And generating a comprehensive score of a 0-1 interval based on a distance weighting model of a Gaussian kernel function, and displaying a result through an interactive system. The system comprises a composite feature extraction module, an industry hotspot modeling module, a vector space alignment module, a distance weighted evaluation module and a result display module. The method solves the problems that a traditional evaluation method is high in subjectivity and single in feature, objective quantification of patent value is achieved, and the method is suitable for scenes such as intellectual property management and technology transfer and transformation.

The invention relates to the technical field of real estate assessment, and provides a land real estate assessment method and system based on big data, and the method comprises the steps: building a data collection layer, obtaining multi-source land real estate data through a big data platform, setting a geographic boundary, and verifying the data integrity; combining a value influence factor system to set a feature acquisition dimension, and optimizing a weight to obtain a multi-dimensional feature scheme; a model construction layer is established, dynamic modeling is performed based on market fluctuation parameters, key feature values are extracted, and an evaluation reference value is calculated by adopting a gradient lifting algorithm; establishing an evaluation analysis layer, collecting and cleaning real-time transaction data, and comparing an evaluation reference value to generate a value deviation report; and a decision optimization layer is constructed, fluctuation causes are analyzed, model parameters are optimized, and finally an evaluation report is generated. The accuracy and timeliness of land real estate value evaluation can be improved, and a scientific basis is provided for market decision making.

The invention relates to an electric power data asset value evaluation method and system based on a data value chain, and the method comprises the following steps: obtaining multi-source electric power data asset information, carrying out the data preprocessing, and obtaining a unified data asset record; performing multi-dimensional feature extraction on the data asset record to obtain multi-dimensional feature information; constructing a power data asset value map based on the multi-dimensional feature information; community detection and key node identification are carried out on the power data asset value map, association information and value transmission paths among different data assets are determined, and candidate data asset paths are obtained; according to the node attributes and the associated information, performing path value scoring on the candidate data asset paths, and generating a path sorting result; and screening out a data asset combination with a high comprehensive value from a path sorting result to obtain an electric power data asset value evaluation result. Compared with the prior art, the method has the advantages that the systematicness and the globality of evaluation and the accuracy and the operability of an evaluation result are remarkably improved.

The invention discloses a robot remote adaptive data acquisition control system and method based on dynamic value evaluation. According to the system, dynamic information value factors of data points are calculated in real time through a local processing unit of the robot, and the factors integrate the data change rate, the uncertainty, the task context and the abnormal probability. Based on the factor, the system dynamically adjusts the sampling frequency, the compression strategy and the transmission priority. The remote center is responsible for data reconstruction and fusion and utilizes machine learning closed-loop optimization to acquire parameters. According to the invention, the problems of resource waste and key information acquisition omission caused by fixed frequency sampling are solved, and the optimal balance of data acquisition efficiency and quality under a limited bandwidth is realized.

The present invention is a patent value assessment method and system based on deep learning, which relates to the field of patent assessment technology, wherein the method comprises the following steps: obtaining a patent indicator data set; extracting semantic features of the patent indicator data set using a pre-trained large language model to generate a text vector; completing the indicators of an unlabeled patent data set based on a semi-supervised learning paradigm, and constructing a training set and a test set; constructing an evaluation model based on deep learning, iteratively training the evaluation model using the training set, and evaluating the model based on the test set and preset performance evaluation indicators to obtain a trained evaluation model; obtaining patent indicator data to be evaluated, inputting the patent indicator data to be evaluated into the trained evaluation model, and obtaining a predicted score for the patent indicator data to be evaluated; and obtaining an evaluation result of the patent indicator data to be evaluated based on the predicted score.

The invention provides an intelligent scheduling method and device for a scheduling platform. The method comprises the following steps: evaluating the task value of each task by using a value evaluation model; evaluating resource consumption during task execution through a resource evaluation model on the basis of resource requirements during task execution, evaluating the total amount of all resources, and collecting uncompleted tasks; building a backpack problem model based on the task value, the resource consumption, the total amount of all resources and the uncompleted tasks of each task; and solving the knapsack problem model in combination with the scheduling score card to obtain an optimal task combination, and allocating the tasks to corresponding resources or execution units for execution according to the optimal task combination. The dependency, the weight and the resource consumption sequence among the tasks are optimized by automatically analyzing the task values of the tasks; and calculating an optimal task combination in real time by adopting a knapsack algorithm model according to the scheduling progress and the server resource remaining condition. And dynamic changes of task characteristics and resource consumption are fully considered, so that the efficiency and the stability of the system are improved.

The invention relates to the technical field of data asset checking, and discloses an intelligent data asset checking system. The system comprises a hierarchical relationship judgment module, a storage state monitoring module, a safety state monitoring module, an asset value calculation module, a hierarchical disposal execution module, a storage strategy optimization module and other auxiliary modules. The hierarchical relation judgment module generates a topological graph, identifies an abnormal path and activates other monitoring modules; the storage and security state monitoring module outputs a health degree evaluation result and a security threat report respectively; the asset value calculation module is combined with the multi-dimensional data to generate a retention or migration decision; and the hierarchical processing execution module optimizes the storage position and the access authority accordingly. The full-process intelligent management of the data assets is realized, and the problems that omissions are easy to occur and a topological structure is difficult to present due to dependence on manual sorting in existing data asset checking, storage and security state monitoring are disjointed with a checking process, and a decision and an actual business demand are disjointed due to single value evaluation dimension are solved.

The invention belongs to the technical field of data processing, particularly relates to a value evaluation method, system and equipment for an academic literature data set and a storage medium, and aims to solve the problems of lack of complete evaluation standards and single evaluation dimension in the prior art. The method comprises the steps of obtaining a to-be-evaluated academic literature data set; determining a value evaluation index system, and constructing a score calculation model with index weights based on the value evaluation index system; determining a statistical magnitude required for calculation and evaluation of a single literature in the academic literature data set; determining the score of each level index in the value evaluation index system according to the statistical magnitude and a score calculation model; and determining a total value evaluation score of the academic literature data set according to the score of each level index and the corresponding index weight, and determining a value evaluation report according to the total value evaluation score. According to the method, the defect of single evaluation dimension in the prior art is overcome, and quantitative and accurate evaluation of the value of the literature data set is realized.

The invention relates to the field of data asset value evaluation, and discloses a data asset value evaluation system and method based on a multi-period excess income method. The system comprises a data acquisition module, a dynamic discount rate calculation module, an excess income evaluation module and the like. Three innovative parameters including a data asset quality attenuation coefficient, a synergistic effect coefficient and a market environment adjustment coefficient are introduced into the system, and the time-varying characteristic, the synergistic value and the market sensitivity of the data assets are accurately quantified. The system can accurately evaluate the data asset value, provides a scientific basis for enterprise data asset management and investment decision, and is suitable for data asset value quantification demands of multiple types of enterprises such as the Internet.

The invention provides a data element value measurement and pricing control method, system and device, and belongs to the technical field of data asset value evaluation and management. Pricing information similar to data product fields is collected; analyzing the data element supermarket, and recording data quality, data volume, data timeliness and data application scene information at the same time; constructing a preliminary pricing model, and performing a simulated pricing test on the fields of the known prices by using the preliminary pricing model; determining a final price of the data product; formulating and executing a data directory putting mode; a putting monitoring mechanism is established, and the page view, the click rate and the inquiry amount of the data catalog are tracked in real time to know market response; setting an order quantity monitoring index and a market comparison analysis dimension; and collecting order quantity data, sorting and analyzing to generate a monitoring report. Through a value measurement and pricing management and control method, data products are priced by comprehensively considering various factors, and reasonable flow and configuration of data elements are promoted.

The invention discloses a social relation chain value evaluation method and system based on multi-dimensional data modeling, and the method comprises the steps: integrating a social platform API, a user behavior log, geographic position data and equipment information through a data collection module, carrying out the cleaning and standardization of a preprocessing module, dividing a geographic region through a space-time modeling module based on K-means clustering, and carrying out the calculation of the value of a social relation chain. An exponential decay function is introduced to dynamically update an interaction weight, an emotion analysis module adopts a BERT model to carry out emotion classification on comments, a role classification module marks community roles through a rule engine, and finally a value calculation module fuses feature parameters of data of all dimensions to construct a propagation value evaluation model. According to the method and the system, the propagation value evaluation accuracy is remarkably improved, the misjudgment rate is greatly reduced, the social e-commerce propagator screening efficiency, the local life service precise popularization coverage rate and the brand propagation positive word-of-mouth recognition accuracy are improved, and scientific decision support is provided for social fission marketing.

A process of evaluation index (100) creation for organizations, comprising intellectual assets (150), published capital values (110) and indirect values (180) of each organization, the process includes obtaining unorganized information (202) gathered by surrounding information capturing devices, converting unorganized information (202) into organized data (204), calculating asset segment wise projected impact factor (PIF (154)) as a cognitive output (208), calculating evaluation index (100) of each prescribed organization by combining projected impact factors with a base evaluation index (102) of published capital values (110), the projected impact factor assessed for validity of the cognitive output (208) and for machine learning algorithm (220), the evaluation index (100) dynamically projects gross national valuation, wherein the converting of unorganized information (202) into organized data (204) is a continuous, generative and re-generative cognitive process based on unfiltered inputs from a plurality of public devices including SIRI, Alexa, read,ai, Gemini.