74 results about "Data market" patented technology

The invention discloses an intelligent financial risk early warning method and system based on a management decision, and relates to the technical field of data intelligence, and the method comprises a multi-source heterogeneous data collection module which obtains enterprise financial data, supply chain data, market public opinion data and industry reference data in real time through an API interface, risk keywords are extracted from news, social media and policy documents through a natural language processing technology according to the market public opinion data; the streaming data processing engine is constructed based on an Apache Flink framework, performs windowing processing on the real-time data stream, calculates the dynamic fluctuation ratio of financial indexes by sliding a time window, and compares the dynamic fluctuation ratio with a preset industry risk threshold value; and a risk decision fusion model, a dynamic threshold adaptive module and a man-machine collaborative early warning terminal. According to the method, millisecond-level financial index fluctuation monitoring is realized through a streaming computing framework, a knowledge graph and a natural language processing technology are fused, a risk entity and a causal chain are extracted from unstructured data, and a multi-dimensional risk portrait is constructed.

The invention discloses a power transaction market risk dynamic assessment system, which comprises a multi-modal sensing module used for collecting physical equipment operation data, market transaction data and environmental parameters in real time, and performing space-time alignment processing through edge computing nodes to generate a fusion data stream; the self-adaptive evaluation engine is in data connection with the multi-modal sensing module and is used for analyzing multi-dimensional risk association in the fused data stream; the invention belongs to the technical field of electricity markets, and aims to solve the problems that in the prior art, historical data static analysis is relied on, spatial-temporal scales of multi-source data are not uniform, and data cannot be given in time. The technical effects are that a closed loop is formed through sensor data, a multi-mode sensing module (alignment processing), an adaptive evaluation engine (risk analysis), a digital twin system (simulation strategy) and a dynamic feedback module (optimization system), automatic calibration and learning are realized, and the risk prediction accuracy and the reliability of the scheme are improved.

The invention discloses a financial risk control method and system, and relates to the technical field of enterprise financial management. The method comprises the following steps: acquiring related data; the related data comprises financial data, market data, operation data and external data; wherein the external data comprises an industry report, competitive product data and policy and regulation change data; constructing a knowledge graph according to the related data; the knowledge graph comprises financial risk types and corresponding risk levels and risk influence factors; based on the knowledge graph, constructing a financial risk prediction model by using a credit risk model and a quantification algorithm; the credit risk model adopts a KMV model; the quantization algorithm comprises a historical simulation method and a Monte Carlo simulation method; and performing real-time monitoring on a target enterprise by using the financial risk prediction model, and performing visual display on a prediction result in a set period. According to the invention, the risk monitoring accuracy and timeliness can be improved.

The invention relates to the technical field of engineering cost based on AI, in particular to an engineering cost database platform based on AI intelligent identification and a use method, and by arranging the engineering cost database platform based on AI intelligent identification comprising a measurement and calculation and AI module and an interactive display module, precipitation and index analysis can be carried out on measurement and calculation data, and the calculation and calculation efficiency is improved. And through data matching, the data in the list can be automatically compared, and the tedious work of manual comparison is avoided. By setting a database, an index library and a material price library, project cost data, including a standard list, project amount data and market price data, of already-completed construction projects of an enterprise can be collected and sorted, and then various indexes influencing the construction project cost are abstracted from the project cost data; and the method is used in the cost consultation service to realize rapid measurement and calculation of the cost of the project to be constructed, and assists the whole process cost management and control of the construction project at the same time; and finally, continuous improvement of the cost consultation service value is realized.

The invention, which relates to the technical field of intelligent inventory management, discloses an intelligent inventory management system for household appliance spare and accessory parts, comprising a problem diagnosis module used for generating an inventory management first-level problem label and an inventory management second-level problem label; the intelligent demand prediction module is used for carrying out classification demand prediction according to the problem label generated by the problem diagnosis module and carrying out dynamic parameter adjustment on a prediction value; the dynamic classification management module is used for carrying out intelligent warehouse division, supplier collaborative optimization and digital agreement on the household appliance spare and accessory parts; and the intelligent storage module is used for upgrading hardware and carrying out software integration operation. According to the invention, historical data, market trend and seasonal fluctuation are utilized to accurately predict the requirements of spare and accessory parts. Classification is performed according to the value and demand fluctuation of the spare and accessory parts, and fine management is implemented. Through a VMI + JMI mixed mode, a supply chain is optimized to be cooperatively combined with an RFID technology and automatic storage equipment, so that the warehouse management efficiency is improved.

The invention relates to the technical field of digital management, in particular to a data analysis system based on digital enterprise management. The system comprises a data acquisition module, a model establishment module, an inventory adjustment module and a risk assessment module. According to the invention, competitor data, market promotion data and raw material supply chain data are collected through the data acquisition module, a neural network model for predicting enterprise sales is established by using the model establishment module, and the inventory adjustment module calculates the order quantity of the product by using an economic order quantity model according to the predicted enterprise sales. The maximum inventory level of an enterprise is determined through a reorder point algorithm and safe inventory setting, the risk assessment module comprehensively analyzes various indexes by using an analytic hierarchy process to obtain a comprehensive risk assessment result, an inventory optimization strategy is formulated according to the assessment result, and the risk of the enterprise is optimized. The inventory turnover rate is improved, the customer satisfaction is improved, and the market competitiveness is enhanced.

The invention discloses an electric power economic dispatching model solving method and device, a storage medium, a program product and computer equipment, and the method comprises the steps: constructing a safety constraint mathematical model based on an overload generator tripping type electric power system safety and stability control device, and determining an original feasible region of an overload generator tripping type condition section according to the safety constraint mathematical model; reconstructing the original feasible region through the introduced cut plane to obtain a reconstructed feasible region; solving is carried out based on power monitoring and data acquisition SCADA real-time acquisition data, market clearing data and a reconstructed feasible region to generate a new cut plane through iteration, so that on the premise of guaranteeing precision, power market intra-day real-time rolling clearing can be rapidly solved, and the efficiency is improved. The method effectively guarantees the speed and precision requirements of power market construction for safety constraint economic dispatching, ensures that the market clearing result has the actual performability of a power grid, and especially solves the problem that the real-time clearing of the power market is difficult due to overload generator tripping type condition sections.

The embodiment of the invention discloses a data space and a data interaction method. The data space comprises a plurality of modules, and an efficient intelligent data circulation system is constructed. The data market platform stores data resource description, and the data resource library is responsible for storing data governance information. And the AIAgent module converts query of a data demander into a structured request through a natural language processing technology, and performs data matching. And a matching result generates a data use protocol through the contract management module, so that the data use compliance is ensured. And the user interaction module is used for displaying and confirming protocol content and ensuring consensus of a data provider and a data demander. Once confirmed by the protocol, the data resource description will be delivered to the data demander through the AIAgent delivery engine. According to the invention, the data interaction and delivery efficiency is greatly improved, manual intervention is reduced, intelligent scheduling, development and utilization of data resources are realized, and data transaction circulation is effectively promoted.

The invention discloses a bridge engineering bidding and bidding quotation risk early warning and cost dynamic regulation and control system and method. The system comprises a data acquisition module, a data preprocessing module, a risk early warning module, a cost dynamic regulation and control module, a linkage execution module, a data storage and update module and a man-machine interaction module. On one hand, a multi-dimensional data acquisition system of bridge exclusive basic data, market dynamic data, policy and regulation data, bidding transaction data and construction pre-judgment data is covered, cooperative operation of static feature risk association factor extraction and dynamic time sequence risk change trend capture is realized, and the accuracy of risk assessment is improved; and meanwhile, a risk-cost closed-loop linkage mechanism is established, a cost-risk association database is constructed, and differential cost regulation and control strategies are formulated in combination with management and control targets of different stages of bidding and tendering, so that the technical problems of low regulation and control efficiency and poor adaptability are solved.

The invention discloses an industrial system data resource aggregation utilization platform system and method, and belongs to the field of intelligent platforms. The industrial system data resource aggregation utilization platform system comprises a multi-source heterogeneous data fusion center module, enterprise production data, supply chain data, market dynamic data and policy and regulation data are converted into a mapping data model of a unified structure, and dynamic weight reconstruction is carried out on the correlation degree between nodes through a graph neural network; by setting a multi-source heterogeneous data fusion center module, on the basis of a vector space mapping mechanism and a graph modeling framework, enterprise production data, supply chain data, market dynamic data and policy and regulation data are converted into a graph data model of a unified structure, and a graph neural network is introduced to carry out dynamic weight learning on the correlation degree between graph nodes, so that the dynamic weight learning is realized. Therefore, the problem of data islands existing in a traditional system is effectively solved, and the aggregation utilization efficiency of industrial data resources is improved.

The invention specifically relates to a security transaction intelligent analysis and decision support system and method. The method comprises the following steps: data integration; transaction behavior analysis: analyzing the transaction information data of the user to obtain a transaction evaluation value; evaluating the market environment; evaluating the risk adaptation degree; and decision support. According to the method, user transaction behavior data, market environment characteristics and psychological stress indexes are integrated, and a multi-dimensional risk assessment model is constructed; the position dispersion degree is quantified, and three-dimensional evaluation of the risk bearing capacity of the user is realized in combination with geometric models of stop loss operation values and risk lever values; psychological factors such as decision hesitation time consumption and order modification frequency are brought into risk calculation, and the comprehensiveness and accuracy of risk assessment are remarkably improved.

The invention relates to an intelligent ship repair business opportunity prediction method and system based on multi-source data fusion, and the method comprises the steps: collecting multi-source data including ship basic data, market dynamic data and enterprise internal data; automatically screening out business opportunity ships with high potential ship repairing business opportunities by setting multi-dimensional screening rules such as a special inspection window rule, a geofence rule and a tonnage limitation rule; data of multiple dimensions including historical ship repair record scores, historical common route record scores, customer level scores, to-be-detected approaching time scores, company affiliated country scores, ship type scores, ship size scores and the like are constructed; dynamically calculating the total score of each business opportunity ship in the initial business opportunity list by combining a static weight and a dynamic adjustment mechanism; and finally, automatically converting a natural language query request input by a user into a structured format query condition by utilizing a natural language processing technology in combination with a knowledge base and an intention recognition conversion method so as to screen out a ship matching result, thereby realizing prediction of ship repair business opportunities.

The invention discloses a data right confirmation method based on subject and object binding and a data living body. The data right confirmation method comprises the following steps: data right confirmation based on subject and object binding; and behavior control is operated based on data bound by the subject and the object. Data right confirmation based on subject and object binding is realized through an active data object ('data living body 'for short), data rights such as data ownership and use right are clearly defined, and a technical means is provided for guaranteeing rights and interests of data subjects and effectively controlling data operation behaviors, so that safe circulation of data in a legal and compliant framework is ensured, and the data security is improved. Efficient circulation and reasonable utilization of data elements are guaranteed, data market vitality and resource allocation efficiency are improved, and robust development of digital economy and digital China is promoted.

The invention relates to the technical field of intelligent production management and control, in particular to an intelligent production management and control system based on multi-point monitoring, which comprises a cross-dimension sensing network, a data middle station and an intelligent cooperative control module, the cross-dimension sensing network is used for collecting multiple types of monitoring data in the whole production process, and the multiple types of monitoring data comprise equipment operation data, biological characteristic data, environmental parameter data, market public opinion data and fault characteristic data; the data middle station is respectively connected with the cross-dimension sensing network and the intelligent cooperative control module and is used for carrying out fusion processing and association analysis on multiple types of monitoring data; and the intelligent cooperative control module is used for obtaining a result according to the correlation analysis of the data in the data. According to the invention, the multi-type monitoring data in the whole production process is acquired through the cross-dimension sensing network, so that the problems of incomplete production process control and poor collaboration caused by the adoption of single-dimension data monitoring in most traditional production control systems are solved.

The invention provides a reinforcement learning dynamic pricing method based on game feedback, and relates to the technical field of online data market dynamic pricing. Through combination of leader-follower strategy interaction of the Stackelberg game and the exploration-utilization balance principle of the dobby machine, the problems of price adjustment lag, large income fluctuation and insufficient strategy stability in a non-stable environment are solved. By introducing a data freshness evaluation mechanism, data weight is dynamically adjusted by integrating data timeliness attenuation and market correlation analysis, it is ensured that data input into a model always has high real-time performance and strong correlation, and the problem of decision lag caused by static data input in a comparison scheme is avoided; the exploration-utilization balance principle and the reinforcement learning model of the dobby machine are fused, the exploration rate is dynamically adjusted, a historical optimal strategy can be fully utilized in a non-stationary environment, a potential better scheme can be explored, and the static optimization bottleneck that a game model is limited to a preset objective function in a comparison scheme is broken through.

Provide a system. 【Solution means】 Means for inputting the target sales amount and profit rate, Means for acquiring past sales data, customer data, and market trend data, Means for making a sales prediction based on the acquired data, Means for calculating the number of sales required to achieve the target, Means for generating an appropriate product list, Means for automatically generating the sales prediction and product list in document form, Means for outputting the document, A system including the above.

Methods and systems for a data marketplace in a fluid conveyance device includes a self-organizing data marketplace. The self-organizing data marketplace includes at least one data collector and at least one corresponding fluid conveyance device in an industrial environment, wherein the at least one data collector is structured to collect detection values from the fluid conveyance device; a data storage structured to store a data pool comprising at least a portion of the detection values; a data marketplace structured to self-organize the data pool; and a transaction system structured to interpret a user data request, and to selectively provide a portion of the self-organized data pool to a user in response to the user data request.

The invention discloses an engineering cost real-time evaluation and early warning system based on multi-source data fusion and dynamic learning, and the system comprises a multi-source data collection module which is used for collecting construction process data, historical engineering data, market information data, and design drawing data; the preprocessing module is used for preprocessing and fusing the collected multi-source data to form a multi-dimensional feature vector with a unified timestamp; the hybrid neural network evaluation module is used for inputting the multi-dimensional feature vector into a pre-constructed hybrid neural network model for cost evaluation; the dynamic optimization learning module is used for carrying out online learning and dynamic optimization processing on the hybrid neural network evaluation module based on a transfer learning strategy; the block chain evidence storage module is used for storing key data generated in the evaluation process; and the progress monitoring and early warning module is used for monitoring the construction progress in real time, calculating progress deviation and triggering corresponding early warning. Accurate evaluation, real-time early warning and dynamic optimization of the construction cost are realized.

The invention relates to the technical field of decision management, in particular to an intelligent delivery decision management method and system for ROI dynamic optimization. Firstly, user behavior data, market bidding data, content feature data and time sequence data are obtained and preprocessed; then, constructing a multi-dimensional ROI prediction model based on a space-time attention mechanism, capturing time sequence dependence by using a time attention head, mining spatial relevance through a spatial relation graph, and outputting ROI prediction values of different putting strategies; thirdly, constructing a budget allocation module by adopting a deep Q network algorithm, modeling a delivery decision into a Markov decision process, and generating an optimal delivery strategy; and a multi-level feedback closed-loop optimization mechanism is established and comprises a real-time feedback layer, a short-term adjustment layer and a long-term optimization layer, so that strategy dynamic adjustment of different time scales is realized. The method can accurately predict the ROI, intelligently distribute the budget, quickly respond to the market change, and remarkably improve the advertisement putting effect and the resource utilization efficiency.

The application provides a kind of virtual power plant intelligent scheduling method and system based on space-time network model, belong to intelligent power plant technical field.The method of the application includes collecting real-time data from distributed power station and weather station;The collected data is preprocessed;Using space-time neural network unit, construct space-time network model based on encoder-decoder, and import the data after preprocessing, predict the expected working power of each device of virtual power plant;According to the prediction result of space-time network model, combined with the real-time state of virtual power plant and actual constraint condition, intelligent scheduling decision is made.Through the end-to-end design of space-time network model, the electrical data, weather data, date data, equipment parameter data, market operation data and geographic location information are comprehensively considered, and the detailed scheduling strategy and scheme can be directly obtained, which not only improves the reliability and economy of power supply, but also helps to optimize energy structure, promote energy saving and emission reduction and sustainable development.

The application discloses an anti-nasal allergy gel production optimization method based on data analysis, and particularly relates to the field of resource allocation and production optimization, and is used for solving the problem that the response to regional and seasonal demand changes in the existing anti-allergy gel production process is lagging behind, and the raw material supply constraint and the production line switching cost are difficult to balance. The method collects raw material supply chain data, market sales records and environmental allergen time series data, constructs a unified space-time correlation data basis, predicts the anti-allergy response intensity in the future production cycle by using a recurrent neural network, and forms product dosage form preference weights based on association rule mining. On this basis, combined with the raw material inventory and the delivery cycle constraint, a plurality of executable candidate production schemes are generated, and further, the schemes are selected according to the real-time state of the production line and the switching cost, so as to realize the fine configuration and efficient execution of the anti-nasal allergy gel production process.

The invention discloses a financial product evaluation method and device, equipment and a storage medium, and relates to the field of artificial intelligence. The method comprises the following steps: acquiring attribute data, market data and a user state of a financial product; determining a risk index, an income index and a fluctuation period according to the attribute data and the market data, and determining a user index according to the user state; determining a risk weight, an earning weight and a user weight according to the fluctuation period, and optimizing a product evaluation model according to the risk weight, the earning weight and the user weight; and inputting the risk index, the income index and the user index of the financial product into an optimized product evaluation model, so that the optimized product evaluation model determines an evaluation result of the financial product. According to the technical scheme, the financial products are automatically and accurately evaluated through the weighted multi-dimensional indexes.

A data market system located at the Internet includes a storage circuit, an input surface circuit, and a processor. The input surface circuit is used for receiving a query and an encrypted data packet. The processor is used for controlling the input surface circuit to receive the encrypted data packet and to store the encrypted data packet in the storage circuit. The processor processes the encrypted data packet according to the query. The encrypted data packet corresponds to at least one of anonymization application programming interface, blockchain, differential privacy, and homomorphic encryption provided by the data market system.

The invention relates to a value investment and transaction decision-making method and system based on behavioral finance, relates to the field of investment management, and solves the problems of low data processing and analysis efficiency and lack of scientificity of decision-making in related technologies. Comprising the following steps: in response to a preset transaction triggering condition, obtaining financial data of each investment target starting the triggering condition, and determining a market performance score of the investment target according to the financial data of the investment target and a preset weight; determining a quality factor QMJ index according to the financial data of the investment target; determining internal value data of the investment target according to the financial data of the investment target by using a cash flow discounting model; and determining a transaction decision of the investment target according to the financial data, the market performance score, the quality factor QMJ index and the intrinsic value data of the investment target. According to the technical scheme, systematization, scientization and rigorization of investment research and transaction decision making are achieved, the accuracy and efficiency of investment decision making are improved, and the investment risk is reduced.

The present disclosure is directed to a universal data language (UDL) translator. Specifically, the systems and methods disclosed enable input data from a variety of sources to be translated into a UDL that can be consistently analyzed and compared against other sources of data. For example, an entity may upload input data that has a plurality of data terms and definitions (e.g., header column in a spreadsheet). These terms may be duplicative and / or inaccurate with respect to the underlying data. If the entity wishes to compare and transact data within a data marketplace, the entity may not fully comprehend what data it is missing and / or what data another entity may have to offer for trade. To remedy this problem of business semantic management, the present invention discloses steps for creating a UDL and a UDL translator so that any input data can be translated to UDL.

The invention relates to the technical field of engineering cost management, in particular to a digital building engineering project cost management system. The system comprises a data acquisition layer, an intelligent analysis layer and a decision output layer, the data acquisition layer is used for sequentially and continuously acquiring physical quantity data, BIM component-level engineering quantity data, market price data and carbon emission activity data of a construction site; the intelligent analysis layer integrates a learning cost prediction model, a BIM-cost mapping engine, a cost deviation traceability knowledge graph and a reinforcement learning simulation module; the decision output layer deploys a dynamic budget adjustment mechanism of block chain evidence storage and executes a carbon cost measurement model; according to the dynamic budget adjustment mechanism of the block chain evidence storage, each budget adjustment is submitted to a permissible block chain network in a smart contract form, and a budget change voucher which cannot be tampered is generated after consensus of multiple nodes; and executing the carbon cost measurement model for calculating the marginal carbon cost in real time and dynamically injecting the carbon quota transaction price into the cost prediction model.

The invention discloses an energy storage carbon emission reduction prediction method and device based on a large language model. The method is applied to an energy storage battery and comprises the steps that to-be-used data generated in the energy storage battery in the emission duration is obtained, and the data at least comprises one or more of operation and measurement type numerical data, unit and emission related structural data, market and carbon policy type text data and external environment load type data; determining a target feature tensor to be input into the target language model according to the to-be-used data, inputting the target feature tensor into the pre-trained target language model, and outputting the carbon emission and confidence attribute of the to-be-used data within the prediction duration; the target feature tensor at least comprises one or more of the charging power, the discharging power, the state of charge, the target marginal emission factor, the electric quantity transaction attribute, the environment feature and the electric energy policy file at the discrete time point. The energy storage carbon emission reduction amount is predicted based on the large language model, and the prediction accuracy is improved.

The present disclosure is directed to a universal data language (UDL) translator. Specifically, the systems and methods disclosed enable input data from a variety of sources to be translated into a UDL that can be consistently analyzed and compared against other sources of data. For example, an entity may upload input data that has a plurality of data terms and definitions (e.g., header column in a spreadsheet). These terms may be duplicative and / or inaccurate with respect to the underlying data. If the entity wishes to compare and transact data within a data marketplace, the entity may not fully comprehend what data it is missing and / or what data another entity may have to offer for trade. To remedy this problem of business semantic management, the present invention discloses steps for creating a UDL and a UDL translator so that any input data can be translated to UDL.

The paper book dynamic content optimization and environment-friendly publishing system based on artificial intelligence comprises an artificial intelligence content optimization engine, an environment-friendly material adaptation module, a dynamic printing control unit and a user feedback analysis network. An artificial intelligence content optimization engine analyzes reader behavior data, market trends and text semantic features, dynamically generates personalized content templates and optimizes typesetting and layout; the environment-friendly material adaptation module is used for matching an optimal environment-friendly paper combination through a multi-objective optimization algorithm on the basis of book types, printing batches and regional resource data; the dynamic printing control unit is connected with the digital printing equipment and receives the combination of the content template and the optimal environment-friendly paper in real time; the user feedback analysis network is deployed in an NFC chip or a two-dimensional code embedded in a book, and continuously collects reader page stay duration, two-dimensional code scanning frequency and physical page breakage data. According to the method, the problems of paper book content staticization and low efficiency in an environment-friendly material selection process can be solved.

Disclosed is a system and several associated methods providing a decentralized fair data marketplace wherein said system keeps time-sensitive (real-time) and location-based data. The system includes a decentralized blockchain for recording participants' registrations, storing transactions such as data production, and sell / buy activities, and ensuring authenticity and privacy of transferred data. Data is gathered through crowdsourcing (data pooling) and data generators are rewarded for willingly contributing datapoints using a dynamic pricing engine designed specifically for time-sensitive location-based datapoints. The set of novelties put together in this invention aims at stimulating a fair and participative data economy where individual contributions, valuation and reward sharing processes are conducted transparently, securely and in a tamperproof manner through use of blockchain, leading to an incentivized and quality data curation.