258 results about "Value assessment" patented technology

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 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.

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 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 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 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 discloses a cross-platform member unified management and service optimization system. A data fusion processing module breaks a data island, fuses multi-platform member data, and constructs a complete member portrait; the member value evaluation module comprehensively and accurately evaluates the member value by using a member value evaluation formula based on the comprehensive data, insights the contribution and potential of the member value, and provides a basis for a marketing strategy; the member layering module divides levels according to values and dynamic thresholds, enterprises can formulate personalized management strategies accordingly, resources are reasonably distributed, and the management efficiency and the member satisfaction degree are improved; the service recommendation module calculates and recommends personalized services according to a matching degree formula in combination with the hierarchy and historical behavior data; all the modules are tightly matched, a complete closed loop is formed from data integration, value evaluation, hierarchical division and service recommendation, the member experience is effectively improved, and the recognition sense and stickiness of members to enterprises are enhanced.

The invention discloses a project and intellectual property achievement association evaluation method and system, and relates to the technical field of evaluation methods, and the method comprises the following steps: S1, data acquisition: obtaining project data, intellectual property achievement data and product data; s2, association model construction: constructing a project-intellectual property achievement-product association model based on the project data, the intellectual property achievement data and the product data; s3, matching degree calculation: calculating a multi-dimensional matching degree between the project and the intellectual property achievement based on the association model; s4, performing dynamic correction; and S5, value evaluation. According to the method, through quantitative analysis of a claim modification track in the review opinion reply process, especially the core index of the calculation technology abandon-retention ratio, patents which are subjected to large limitation shrinkage and of which the core protection range is seriously shrunk can be effectively discriminated, and association degree overestimation caused by neglecting the review game process is avoided.

The technical scheme of the invention discloses an orthopedic implant patent value evaluation system, which is characterized by comprising an orthopedic implant evaluation reference standard data acquisition module, an orthopedic implant patent data integration module, a multi-source data feature construction module, an orthopedic implant patent comprehensive value evaluation model and an evaluation and result output module. The invention provides an orthopedic implant series patent value evaluation and analysis system for identifying high-quality, convertible and investable orthopedic implant patents, and can combine legal, economic, technical and other patent evaluation indexes with clinical research data, supervision systems, clinical guidelines and the like. Dynamic evaluation and prospective prediction are carried out on the value of a series of orthopaedic implant patents, market data and a machine learning algorithm are introduced, an industrial exclusive evaluation platform is constructed, a system value of achievement transformation-patent value evaluation is established, and scientificity, adaptability and practicability of an evaluation result are ensured.

The embodiment of the invention provides an online reinforcement learning method and system, a storage medium and a degree product, and the method comprises the steps: controlling a robot to execute a target operation task through a strategy model, so as to obtain corresponding first training data; receiving first interaction information from a human expert to construct corresponding second training data; performing time difference loss calculation based on the first training data and the second training data to train a value evaluation model, and obtaining value evaluation information output by the value evaluation model; performing advantage calculation based on the second training data and the corresponding value evaluation information to obtain advantage information; performing fusion loss calculation based on the advantage information to train a strategy model; the fusion loss comprises advantage weighting strategy loss and near-end strategy optimization loss. According to the embodiment of the invention, the success rate of the robot on the target operation task can be improved under the conditions of lower human teaching cost and fewer environment interaction samples.

The application discloses a multi-index fused Chinese patent value evaluation method and belongs to the technical field of patent value evaluation. The method comprises the following steps: step 1, extracting patent text dimension features; including: extracting short text features from the abstract based on a large language model (GLM), and extracting long text features from the claim based on an HBert model; step 2, extracting patent basic information index features; including technical dimension indexes, economic dimension indexes, legal dimension indexes and enterprise dimension indexes; and step 3, performing patent value grade evaluation by using an XGBoost model based on the patent text dimension features and the patent basic information index features.

The invention discloses a film and television copyright one-key distribution and popularization full-automatic transaction system and method based on AI evaluation and a smart contract. The method comprises the following steps: receiving copyright content; calling an AI model to carry out value evaluation and originality verification, and generating an evaluation report; generating an electronic protocol based on the validated evaluation report; converting the protocol into a smart contract and deploying the smart contract to a block chain; responding to the one-key distribution instruction, and distributing the content and the contract interface to a playing platform; obtaining under-chain income data through an oracle machine; and the smart contract automatically verifies and executes account division. According to the system, the AI and the block chain technology are deeply fused, automation, intelligence and transparency of the whole movie and television copyright transaction process are achieved, the transaction cost and the trust cost are greatly reduced, and a new generation of infrastructure is provided for the movie and television copyright transaction market.

The invention discloses a digital asset value evaluation method and system for power dispatching. The method comprises the steps of firstly obtaining a digital asset set in a target scheduling scene, then determining internal attributes of the digital asset set based on a power system physical topology and an operation rule, and determining external value characteristics of the digital asset set based on a power market environment and a scheduling decision demand. Thirdly, value calculation is carried out on various digital assets by utilizing the model in combination with real-time and historical scheduling operation data, and a basic value score is output; and generating a comprehensive value evaluation result and a value label according to the score in combination with a preset rule and an adjustment factor. Finally, the evaluation result and the label are applied to a power dispatching auxiliary decision support system, and a quantitative basis is provided for dispatching plan optimization, data asset internal pricing and external transaction. According to the method, accurate, dynamic and business value evaluation of the power dispatching digital assets is realized, and value release and efficient utilization of data elements are effectively supported.

The invention discloses a remainder value evaluation method and device for second-hand electronic equipment and a medium, belongs to the technical field of electronic equipment value evaluation, and aims to solve the problems that in existing remainder value evaluation of the second-hand electronic equipment, manual detection evaluation is generally adopted, the subjectivity is high, the remainder value detection dimension is shallow, the labor time consumption cost is high, and the evaluation efficiency is high. And dynamic data support is also lacked. The method comprises the following steps: carrying out structural data acquisition processing related to a physical appearance mode on second-hand electronic equipment to be evaluated; performing data test processing based on a hardware performance mode under non-invasive flaw detection on the second-hand electronic equipment; using mode quantitative analysis under federal local learning is carried out on the software system using mode of the second-hand electronic equipment; carrying out equipment use mode scoring processing under the microscopic state analysis network; and carrying out residual value evaluation processing in a related market residual value evaluation interval between the comprehensive state score and the multi-dimensional value influence factors of the second-hand electronic equipment.

The invention relates to the technical field of commodity value evaluation, in particular to a commodity value evaluation model construction method and system based on a small sample scene. Comprising the following steps: establishing a plurality of sub-data sources according to equipment category parameters, and constructing a federal collaboration layer according to all the sub-data sources; acquiring a first-level shared feature packet generated by the federal collaboration layer, correcting the first-level shared feature packet according to a preset transfer learning layer, and generating a second-level feature packet according to a correction result; an initial evaluation model is constructed according to the secondary feature package, and a commodity evaluation model is constructed by the time sequence layer according to the initial evaluation model; a plurality of sub-data sources are established on the basis of equipment categories, a feature space alignment mechanism of cross-category equipment is established by establishing a mapping transmission model of each sub-data source, local feature sharing of different equipment categories is realized through federated learning, and the number of dependencies of evaluation and cross-category evaluation errors of the model are reduced.

The invention discloses a personalized recommendation method and device, equipment and a storage medium, and relates to the technical field of deep learning, and the method comprises the steps: carrying out the mapping of a target user feature and a target post feature through a target double-tower model, and generating a user embedding vector and a post embedding vector; constructing an initial state feature based on the user embedding vector, the post embedding vector and an interaction embedding vector corresponding to the interaction post, and determining a corresponding initial recommendation post based on the initial state feature through a target policy network; and performing value evaluation on the initial state features and the initial recommendation posts through a target value network, and determining corresponding target recommendation posts in the initial recommendation posts. The target value network and the target strategy network are combined with the interaction post to evaluate the value of the initial recommendation post of the user, so that the recommendation result is accurately matched with the instant interest of the user, the long-term preference can be considered, and the problem of content convergence of personalized recommendation is solved.

The invention provides a data processing method, electronic equipment and a vehicle, and relates to the technical field of data processing, and the method comprises the steps: obtaining the correlation evaluation data of each stored memory data, carrying out the multi-dimensional value evaluation of the memory data according to the correlation evaluation data, and determining a value evaluation score. And determining a corresponding forgetting strategy according to the value evaluation score, and performing storage management on the memory data according to the forgetting strategy. Different forgetting strategies are adopted for storage management of data with different evaluation values, high-value data are reserved, low-value data are deleted or compressed, part of storage space is released, and continuous expansion of the storage space is avoided. In the subsequent data retrieval process, the retrieval efficiency of the memory data can also be improved, and continuous reduction of the system efficiency is avoided. Through dynamic storage management of the memory data in the method, self-optimization and evolution of the memory management system are realized, and the requirements of users for data management are met.

The invention discloses a cross-prompt automatic composition scoring method based on data evaluation and meta learning. The method comprises the steps of obtaining source prompt word data; performing value evaluation on the source cue word data to obtain a value probability of a cue word sample; obtaining a target value sample set according to the value probability; performing synonymous conversion enhancement on the target value sample set to obtain an enhanced sample set; processing the enhanced sample set to obtain enhanced training data; based on the enhanced training data, meta-learning cross-prompt automatic composition scoring model training is carried out, a target scoring model is obtained, and the target scoring model is used for carrying out automatic scoring on compositions.

The invention relates to the technical field of intelligent talent evaluation, and discloses a multi-dimension-based talent team evaluation method and system, and the method comprises the steps: collecting original talent data, and obtaining a standardized multi-source fusion talent data set through a deep semantic fusion method of a domain ontology; adopting a five-dimensional evaluation framework design and a dynamic weight learning method to obtain a multi-dimensional evaluation index system with a comprehensive initial weight; adopting a multi-factor adaptive weight adjustment method to obtain personalized dynamic weight configuration; a progressive mixed evaluation calculation method is adopted to obtain a comprehensive evaluation score; a multi-level time sequence evolution analysis and development potential prediction method is adopted to obtain a talent comprehensive value evaluation result; and an intelligent visual analysis and multi-dimensional decision support method is adopted to obtain a complete talent evaluation output result. According to the method, multi-dimensional dynamic evaluation and development potential prediction of talent teams can be realized, and scientific support is provided for talent management decision.

The invention relates to a value evaluation method and device for existing building reconstruction, a storage medium and electronic equipment. The value evaluation method for existing building reconstruction comprises the steps of obtaining building information of a target building; based on the building information of the target building and a building value evaluation model, carrying out value evaluation on the value of the building to obtain a building value evaluation result; wherein the building value evaluation model comprises at least one of a culture value evaluation standard, a safety value evaluation standard, a use value evaluation standard, a science and technology value evaluation standard, an environment value evaluation standard and an economic value evaluation standard, and the building value evaluation model is formulated based on characteristics of different projects; wherein each evaluation standard corresponds to a plurality of evaluation grades from comprehensiveness to gradual progression, and each evaluation grade comprises a plurality of evaluation indexes.

The invention belongs to the technical field of oil field economic evaluation, and discloses an evaluation method for ascertaining an undeveloped full life cycle reserve value. The method comprises the following steps: obtaining development data and public evaluation parameters of a target block; calculating an extracted yield value NPV1; calculating the value of unextracted reserves without risk consideration; calculating a reserve risk coefficient R1 and a yield risk coefficient R2; calculating an unextracted reserve value NPV2; and calculating the full life cycle reserve value of the target block. According to the method, full-life-cycle reserve value evaluation is fully considered, the application of the yield risk to the reserve value is considered, the two-point equation is adopted to calculate the reserve risk coefficient, and the method is very scientific and accurate.

The invention relates to a quantitative evaluation method and device for subject operation data, equipment, a storage medium and a program product. The method comprises the following steps: screening out comparable reference subjects; fitting industry operation data through a unary regression model based on all reference subjects of the industry to which the comparable reference subject belongs; calculating an average income growth rate and an average income level of the industry; estimating risk parameters of the comparable reference subject based on a unary regression model, and calculating a right value quantized value of the comparable reference subject; calculating an operating value quantized value of the comparable reference subject by combining liability data and cash holding data of the comparable reference subject; respectively calculating the ratio of a preset number of comparable reference subjects to the financial parameters corresponding to the subject to be evaluated to obtain financial parameter adjustment weights; value adjustment parameters are obtained through weighted summation calculation, and the operation value quantification interval of the subject to be evaluated is determined. The method can improve the objectivity and practicability of the evaluation of the operating value of the subject who is not listed or lacks public market data.

The invention relates to a developer contribution evaluation method fusing project influence, and the method comprises the steps: collecting original data in an open-source community, and locally constructing a structured data set based on the original data; calculating the ecological importance of the project by adopting a weighted index and improved algorithm method based on a data construction result; carrying out value evaluation based on the submitted code, and introducing project influence as a weight; evaluating the basic value of each submitted code; a code submitted by a developer is systematically evaluated from three dimensions, and the specific analysis process is as follows: modifying a scale dimension: constructing a developer contribution degree calculation model based on a developer contribution value by quantifying the line number and the range of code change, and measuring the value of the developer in a project; the ranking weight of the project in the open source ecosystem is introduced, so that the effects of comprehensively evaluating from two dimensions of code features and global status of the project and more comprehensively and more accurately measuring contribution of a developer are achieved.

This invention relates to the field of fair value assessment technology, specifically to an intelligent method and system for assessing the fair value of data assets by integrating multi-dimensional features. It solves the problems of existing methods for assessing the fair value of data assets, which cannot process data according to its source, thus affecting assessment quality, and are also inconvenient for selecting key features during value assessment. The system includes a data acquisition module, a data preprocessing module, a feature vector extraction module, a model evaluation module, and an application demonstration module. The data acquisition module is used to capture raw data from multi-dimensional data sources related to the data asset and input it uniformly. The data preprocessing module is used to clean, transform, and standardize the input raw data. This invention achieves a more accurate, efficient, and comprehensive assessment of the value of data assets through technological integration and multi-dimensional analysis.

This application belongs to the field of infrastructure operation and maintenance, and relates to a task early warning method, device, computer equipment, and storage medium. The method includes: acquiring real-time logs corresponding to each task; for each task, extracting multi-dimensional task value assessment information from the task's real-time logs; calculating the task value assessment value based on the multi-dimensional task value assessment information; configuring an early warning strategy for the task according to the task value assessment value; and issuing anomaly warnings for the task based on the early warning strategy. Furthermore, the multi-dimensional task value assessment information can be stored in a blockchain. This application achieves refined management of early warnings.

The invention discloses an AI-based game material comment analysis method, system and device and a medium, and the method specifically comprises the steps: carrying out the multi-dimensional sentiment analysis of current material comment data based on a special game sentiment dictionary, and outputting a sentiment tendency analysis result; constructing a player clustering model according to the emotional tendency analysis result and the game behavior data, and establishing a social behavior and comment association model according to the emotional tendency analysis result and the social behavior data; extracting positive and negative feedback information for different player groups according to the player clustering model and the association model; and calculating an optimization priority of each material dimension according to the positive and negative feedback information and a preset player value evaluation model, associating the negative feedback information of the high-value player group with abnormal points in the game behavior data, and generating a material iteration suggestion. According to the method, accurate game material comment analysis is realized, customized iteration suggestions are generated, and the game material optimization efficiency and quality are improved.