556 results about "Value assessment" patented technology

The invention discloses a data-driven cross-domain intelligent asset knowledge reasoning and value evaluation method and system, and relates to the technical field of intelligent assets, and the method comprises the steps: extracting multi-domain data features of intelligent assets based on a deep learning model, and constructing a cross-domain intelligent asset knowledge graph; analyzing a user evaluation target based on a pre-trained language model, retrieving related assets in a knowledge graph, and performing knowledge reasoning by using an attention mechanism and a graph reasoning algorithm; and on the basis of the value evaluation model of reinforcement learning training, a final evaluation value and an evaluation report containing a confidence interval are generated in combination with a time sequence prediction algorithm. According to the method, multi-source heterogeneous data can be effectively fused, knowledge reasoning and value evaluation of cross-domain intelligent assets are realized, and the evaluation efficiency and accuracy are improved.

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 discloses a patent value evaluation method based on semantic mining and feature fusion, and the method comprises the following steps: firstly obtaining a field patent data set through retrieval and data cleaning, and defining a patent value label; determining patent value influence factors from two aspects of patent value evaluation indexes and text semantic features; relevant data are obtained and preprocessed; constructing and training a patent value evaluation model comprising four modules of multi-modal data input, feature extraction, feature fusion and patent value prediction; and evaluating and optimizing the performance of the model by adopting a multi-classification evaluation index, and evaluating the value of the target patent by using the optimal model. According to the method, key features in the patent value influence factors can be effectively mined, comprehensive and accurate evaluation of the patent value is realized, and data support and decision basis are provided for patent achievement conversion and management.

A computer-implemented method for artificial intelligence (AI) based risk / value assessment of a geographic area includes performing feature engineering to contextually enrich collected data. Three datasets are generated from the contextually enriched data, where a first dataset is generated by combining positive samples of the contextually enriched collected data with hard negative samples of the contextually enriched data, a second dataset is generated by combining the positive samples with soft negative samples of the contextually enriched data, and a third dataset is generated by combining the positive samples, hard negative samples, and soft negative samples. A machine learning model is trained to generate three different types of predictions for the risk / value assessment of the geographic area based on the three generated datasets.

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 belongs to the technical field of power grid big data, and particularly relates to a power grid regulation and control data asset value evaluation method and device, equipment and a medium. According to the method, key factors (data integrity, data diversity, data timeliness, data scale and data relevance) influencing the value of the power grid regulation and control data assets are determined, the weight coefficient of each factor is calculated by using an analytic hierarchy process, and then the value of the power grid regulation and control data assets is calculated. According to the scheme, a unified data value evaluation system is established, and a unified value measurement standard is provided for power grid regulation and control data assets by determining the key influence factors and the weight coefficients. Through data value evaluation, the value and distribution condition of each data asset can be clearly known, and efficient management and reasonable utilization of the data assets can be realized. Meanwhile, through data value evaluation, the potential value of the data assets can be found, and possibility is provided for value increment and achievement of the data.

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 embodiment of the invention provides an enterprise data asset assessment method and system, electronic equipment and a storage medium, and the method comprises the steps: receiving a business request, determining a data asset range based on the business request, and obtaining a data asset assessment object according to the data asset range; analyzing the data asset range to obtain data features of a data asset assessment object; constructing the data asset evaluation model based on the data features of the data asset evaluation object; and in response to the service request, performing value evaluation on the data asset evaluation object based on a constructed data asset evaluation model to obtain a value evaluation value. The data asset assessment is standardized and normalized, the data asset management capability is improved, the potential value of the data is revealed, and the data asset mobility is promoted.

The invention relates to a monitoring and early warning method and system for urban railway engineering construction, and solves the technical problem that safe and stable operation of key equipment cannot be effectively guaranteed due to the fact that judgment alarm is too single only according to an operation parameter threshold value of the equipment, and the method comprises the steps: employing a preset analytic hierarchy process for a risk assessment factor set, obtaining weights of different factors; calculating the risk values of the single factors one by one according to a preset different factor risk value evaluation method, and calculating the comprehensive risk value of the key equipment by adopting a preset comprehensive risk value calculation method according to the obtained risk values of the single factors and the weights of the different factors; and according to the category of the key equipment and the corresponding relationship between the risk value interval range in which the comprehensive risk value falls and the early warning processing scheme, analyzing and determining the early warning processing scheme, and executing the early warning processing scheme. The method has the following effects that the risk of key equipment is accurately evaluated, the early warning scheme is scientifically determined, and the engineering safety is guaranteed.

The invention relates to the technical field of artificial intelligence, in particular to a large model training system and method based on multi-modal medical data fusion, and the method comprises the steps: collecting multi-modal medical data, and carrying out the feature extraction and pre-calculation of the multi-modal medical data; performing fusion analysis on the information distinguishing value evaluation of the sample features and the label association strength to obtain a context adaptability regulation factor of the sample data; performing modal consistency analysis and modal significance analysis on the multi-modal medical data sample to obtain an information value deepening regulatory factor of the sample data; performing optimization adjustment on the original attention score through a context adaptability adjustment factor and an information value deepening adjustment factor to obtain a final attention score; and multi-modal data feature fusion and model training are performed through the final attention score, so that the accuracy of learning medical knowledge by the model is improved, and the final performance of the model trained by the training system on a target task is improved.

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 provides a data asset value evaluation method and device, equipment, a storage medium and a program product, and can be applied to the technical field of big data and data asset value evaluation. The method comprises the steps that evaluation model information corresponding to data assets is acquired based on a processing flow corresponding to the data assets, and the evaluation model information comprises technology development index information, financial prediction index information and application scene index information; constructing a data asset value evaluation reference model based on the technical development index information, the financial prediction index information and the application scene index information; and configuring the data asset value evaluation reference model by using the model parameter setting, and generating a data asset value evaluation application model. Wherein the model parameter setting is associated with the cost information and the income information of the data assets; and evaluating the value evaluation object by using the data asset value evaluation application model, and generating an evaluation result corresponding to the value evaluation object.

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 invention relates to a design optimization method based on big data analysis, and the method comprises the following steps: carrying out the improved point recognition of clustering analysis features through an improved point recognition algorithm, obtaining an improved point set, carrying out the primary optimization of a preliminary design scheme according to the recognized improved point set, and obtaining a first optimization design scheme; predicting the market demand of the first optimization design scheme to obtain a market demand prediction result; performing value evaluation on the market demand prediction result to obtain a value evaluation result; according to the method, association rule mining is carried out on a first optimization design scheme to obtain an improved strategy, and secondary optimization is carried out on the first optimization design scheme according to the improved strategy to obtain a second optimization design scheme, so that the problem that most traditional methods are lack of systematic association rule mining and improved strategy generation mechanisms is solved; the technical problems that in the iteration process of design optimization, key improvement points cannot be accurately recognized, the optimization process is delayed, and the development cost is increased are solved.

The invention relates to the technical field of finance, and discloses a dynamic financing risk management method based on a data model, and the method comprises the steps: S10, collecting pledge related data and creditor enterprise data from multi-source heterogeneous data, the pledge related data comprising a pledge BOM table and a raw material real-time market price; s20, the multi-source heterogeneous data are converted into isomorphic data corresponding to multiple items of information data, the isomorphic data are identified, a data model cluster is constructed through a statistical analysis method, and the data model cluster comprises a quality real-time valuation identification model; s30, dynamic value evaluation is carried out on the pledge through a real-time quality valuation identification model, the dynamic value evaluation is to calculate the dynamic cost sum of raw materials, the cost sum is correspondingly adjusted according to factors influencing the value of the pledge, and an valuation confidence interval is output and is a possible range of valuation; and S40, calculating a dynamic pledge rate LTV according to a lower limit value of the estimated value confidence interval, and generating a financing risk index.

The invention discloses a public building group intelligent operation and maintenance management value evaluation method, and relates to the technical field of management value evaluation. Collecting people flow density and internal energy consumption data of the building group in real time; establishing a value influence factor, and forming a core index of operation and maintenance value evaluation; based on the people flow density and the energy consumption data, predicting operation and maintenance demands of each region, and identifying high-demand and high-energy-consumption regions; operation and maintenance personnel and equipment are intelligently distributed according to the analyzed requirements; according to the effect of resource allocation, the instant improvement effect of resource use on the operation and maintenance value of the building group is evaluated; according to the method, operation and maintenance personnel and equipment are preferentially allocated to a high-demand area through an intelligent scheduling algorithm, efficient utilization of resources is realized, resource configuration is dynamically adjusted, timeliness and accuracy of operation and maintenance work are ensured, improvement of operation and maintenance values is quantified by evaluating the effect of resource use, operation and maintenance management values of a building group are improved, and the operation and maintenance efficiency of the building group is improved. And resource allocation of the building group in operation and maintenance management is enabled to be more scientific and efficient.

The invention relates to the technical field of finance, and discloses a dynamic financing risk management method, monitoring equipment is arranged in a monitoring warehouse, and the method comprises the following steps: S10, collecting multiple items of information data of pledges and credits from a multi-source heterogeneous data source; s20, converting the multiple items of information data into isomorphic data corresponding to the multiple items of information data, identifying the isomorphic data, and constructing a data model cluster by a statistical analysis method; s30, performing value evaluation on the pledge provided by the creditor to obtain a value evaluation result; s40, acquiring basic information of the pledge and inputting the basic information into a financial institution system; s50, updating the related data of the data model cluster according to whether the creditor accesses the pledge from the supervision warehouse or not; s60, analyzing and processing the data model cluster, outputting a data value matched with a preset risk mechanism parameter, and generating a plurality of risk indexes; and S70, carrying out risk determination based on the plurality of risk indexes, and when the risk is determined to be a high risk, carrying out financing risk early warning.

The invention relates to the technical field of carbon emission reduction, in particular to an intelligent urban carbon emission reduction project evaluation and fund flow distribution system, which comprises a data analysis module, a dynamic evaluation and adaptation module, a comprehensive value evaluation module and a fund flow distribution module, the data analysis module is responsible for collecting and processing environmental, economic and social data related to a carbon emission reduction project; the dynamic evaluation and adaptation module dynamically adjusts project evaluation standards and methods; the comprehensive value evaluation module comprehensively evaluates the environmental benefit, the economic benefit and the social influence of the project; and the fund flow distribution module considers the adjustment suggestions proposed by the dynamic evaluation and adaptation module and intelligently distributes limited fund resources. According to the invention, the project strategy can respond to the changes of the market and the environment in time, the resource distribution is optimized, and the success rate and sustainability of the project are 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 discloses a Chinese patent value evaluation method based on multi-index fusion, and belongs to the technical field of patent value evaluation. The method comprises the steps of 1, extracting patent text dimension features; comprising the steps of extracting short text features from a specification abstract based on a large language model GLM, and extracting long text features from a claim based on an HBert model; 2, extracting patent basic information index characteristics; comprising a technical dimension index, an economic dimension index, a legal dimension index and an enterprise dimension index; and step 3, based on the patent text dimension features and the patent basic information index features, performing patent value grade evaluation by using an XGBoost model.

The invention belongs to the technical field of data asset management, and discloses a data asset classification protection method based on data assessment pricing, which comprises the following steps: S1, data collection: collecting data assets to be assessed, priced and subjected to classification protection, the data assets comprise business data, customer data and financial data in an enterprise, and market data and partner data which are acquired from the outside; and S2, data asset assessment: comprehensively assessing the characteristics, usage scenarios and potential risks of the data assets to generate a pricing model of the data assets. According to the scheme, value evaluation is performed on the data assets, the data classification is determined in combination with business and safety sensitivities of the data assets, and corresponding safety protection strategies are formulated for different types of data, so that the management efficiency of the data assets is improved, safety resources are reasonably allocated, the overall safety cost is reduced, and the precision and effectiveness of data protection are improved.

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 relates to the technical field of supply chain finance, in particular to a dynamic pledge pool-based movable property financing intelligent management system, which comprises a storage end, a dynamic pledge pool and a bank end. A financing channel for reducing a financing program is provided for the user side by establishing a three-party mode of the storage side, the dynamic pledge pool and the bank side, the user side stores goods into the storage side, the dynamic pledge pool calculates the value and then synchronizes the value to the bank side, and the bank side loses money to the user side after financing, so that the financing efficiency is improved. The dynamic pledge pool can compare the financing amount of the user side with the value of the goods and calculate the amount of the flowable goods at the user side, the user side can output the flowable goods at any time, the pressure of the user side is reduced, and through the synergistic effect of all the modules, the management efficiency is improved, the safety is enhanced, and the financing convenience is improved. Moreover, the accuracy of value evaluation is ensured, and an efficient, safe and convenient solution is provided for the movable property financing business.

The invention relates to the technical field of novel energy storage, and discloses a multi-scene energy storage value evaluation method, device and equipment and a storage medium, and the method comprises the steps: determining a to-be-evaluated target scene, and constructing a multi-scene energy storage value evaluation system; different target scenes in the multi-scene energy storage value evaluation system correspond to different evaluation indexes; the types of the evaluation indexes comprise direct earnings and indirect earnings; obtaining the direct income of the energy storage power station in the target scene and the indirect income of the energy storage power station in other scenes except the target scene; and obtaining the energy storage value corresponding to the target scene based on the weight coefficient corresponding to the target scene, the direct income and the indirect income of each other scene. According to the scheme, automatic value evaluation of multi-scene energy storage is realized, and the values of multiple scenes are considered at the same time; and the efficiency and accuracy of value evaluation of multi-scene energy storage are improved.