451 results about "Value assessment" patented technology

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 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 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 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 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 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 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 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 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 belongs to the technical field of meteorological data value evaluation, and discloses a block chain-based meteorological data multi-model fusion value evaluation system and method, and the system comprises an infrastructure layer, a data processing layer, a block chain layer, a multi-model fusion calculation layer and an application layer. The value evaluation method comprises the following steps: S1, collecting and processing meteorological data; s2, data uplink and right confirmation are carried out; s3, multi-model fusion value evaluation; and S4, triggering the smart contract. The block chain technology is introduced into the meteorological data transaction to solve the transaction data caching, copying and retaining problems of the data asset transaction platform, the privacy and security of the data are protected, the rights and interests of the data asset traders are prevented from being infringed and occupied by the data asset transaction platform, the mechanisms such as ownership authentication and data secrecy are realized, and the data transaction efficiency is improved. And data right confirmation traceability is embodied.

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 relates to the technical field of data networks and data security, and discloses a data asset value evaluation method and system based on dynamic network analysis, and the method comprises the following steps: S1, constructing a data asset catalog, extracting a data feature vector of each data node, forming a data network, connecting the data nodes through an association relationship, distributing a weight for the incidence relation between the data nodes; s2, performing quality evaluation on each data node, calculating a quality value, evaluating a matching value based on a matching degree between the data node and a user demand, and calculating an access frequency of each data node, the access frequency being used for subsequent value calculation; and S3, based on the historical data and the access log, training the graph neural network model. According to the method, the value evaluation theory based on the data relation is adopted, engineering implementation is integrated, and the effect of more scientifically and reasonably evaluating the value when the data enters the table is achieved.

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 provides an agent skill autonomous learning method and system, a storage medium and electronic equipment, and the method comprises the steps: proposing a plurality of candidate targets based on scene information; any candidate target is selected for value evaluation; when the value evaluation result accords with the expectation, taking the candidate target of which the value evaluation result accords with the expectation as a selected target, and performing task planning according to the selected target to obtain a new task sequence; executing tasks in the task sequence based on the skill library; when the skill library does not support task execution, new skill learning is confirmed, a skill model is trained for the new skill, and the skill library is updated. Based on the value-driven normal form, the agent is enabled to autonomously generate a motivation for learning new skills, so that the autonomous evolution of the skills is realized, the adaptive capacity of the agent in coping with a new environment is enhanced, the agent can actively recognize the defects of the agent in the aspect of skill learning, a new learning task is generated, and the autonomous learning capacity of the agent is greatly improved.

The invention relates to the technical field of data product pricing, and discloses a data pricing method based on multi-dimensional value evaluation, a terminal and a storage medium. The method comprises the steps of performing desensitization processing on data needing to be priced based on a differential privacy mechanism, generating a privacy budget value and a privacy risk value of the data, and obtaining a privacy score according to the privacy budget value and the privacy risk value; generating a quality score of the data according to the evaluation index of the desensitized data on the specified task; calculating a dynamic weight coefficient meeting the optimal privacy budget value by using a dual-objective optimization algorithm, wherein the dynamic weight coefficient is used for balancing a privacy protection objective and a data quality objective; and based on the privacy score, the quality score, the dynamic weight coefficient and the cost information of the data provider, generating a data pricing formula by using a meta-learning model, and outputting a pricing result. According to the invention, fair and reasonable data pricing is realized, the expectation of the data purchaser for the data and the leakage risk of the data provider are balanced, and the healthy development of the data transaction market is promoted.

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 embodiment of the invention provides a merchant comprehensive value evaluation method and system based on artificial intelligence and terminal equipment, and belongs to the technical field of artificial intelligence. The method comprises the steps of obtaining historical consumption information and an initial evaluation text and initial score information under the historical consumption information; performing text emotion consistency analysis on the initial evaluation text to obtain a target analysis result; determining a target data state according to the target analysis result in combination with the initial score information; calculating an information association degree between the historical consumption information and the initial evaluation text; deleting the initial evaluation text according to the target data state and the information association degree to obtain a target evaluation text, and obtaining association score information from the initial score information; performing data clustering on the target evaluation text to obtain a target clustering result, and correcting the associated score information according to the target clustering result to obtain target score information; and determining a merchant value evaluation result according to the historical consumption information and the target evaluation text in combination with the target score information.