120 results about "Decision graph" patented technology

The invention discloses an enterprise big data mining method and system based on artificial intelligence, and the method comprises the steps: carrying out the dynamic mode alignment through employing a multi-mode hypergraph neural network according to enterprise multi-source heterogeneous data, and generating a time-space consistent multi-mode joint embedded tensor; inputting the multi-modal joint embedding tensor into an orthogonal adversarial manifold learning module, and generating a low-dimensional compact semantic embedding vector with enhanced category separability; performing space-time causal association mining on the semantic embedding vector, and outputting a space-time causal meta-path map containing the recessive commercial logic; and inputting the space-time causal element path map into a dynamic game adversarial interpretation framework, and finally outputting an enterprise-level intelligent decision map with anti-factual robustness. By utilizing the embodiment of the invention, the multi-modal data can be efficiently integrated and intelligently analyzed, and the accuracy and effectiveness of the mining result are improved.

The invention belongs to the technical field of rail transit intelligent monitoring, and particularly relates to an intelligent monitoring method and device for a rail transit air conditioning system. According to the method, the sensor array with the self-adaptive sampling frequency is used for collecting the multi-modal operation data, then the collected multi-modal operation data is preprocessed, the feature degradation track atlas is established, powerful data support is provided for subsequent fault early warning and diagnosis, and the fault diagnosis accuracy is improved. Historical abnormal events are introduced to dynamically correct the health state evaluation base line, the timeliness and accuracy of the evaluation base line are ensured, in comparative analysis of real-time operation data and the dynamic evaluation base line, a health degree scoring and dynamic threshold mechanism is adopted, quantitative evaluation of the health state of the air conditioner system is achieved, and the evaluation accuracy of the health state of the air conditioner system is improved. According to the method, a decision graph containing fault diagnosis and predictive maintenance suggestions is generated by analyzing the propagation path and time sequence relevance of abnormal parameters in the air conditioning system and combining an equipment topological relation graph, so that the troubleshooting and repairing efficiency is improved.

The invention relates to the technical field of data mining, and discloses a feasibility research data intelligent mining system which comprises a heterogeneous data acquisition module, a semantic association analysis module, a decision map generation module, a time sequence feature correction module and a knowledge distillation optimization module. The heterogeneous data acquisition module captures data features through a multi-source data sensing node and a dynamic dimension fusion network and constructs a multi-layer topology; the semantic association analysis module analyzes semantic association by using a concept topology modeling unit, a knowledge vector clustering unit and a multi-mode switching link; the decision graph generation module generates a core decision reference framework based on strategy optimization nodes and a rule inference engine; the time sequence feature correction module performs time sequence correction and noise compensation on the semantic association; and the knowledge distillation optimization module detects the deviation through the entity relationship evaluation network and feeds back the optimization decision framework. The system realizes multi-source data intelligent acquisition, semantic dynamic analysis and decision graph adaptive generation, and improves the accuracy and efficiency of feasibility research data mining.

The invention provides a prospecting prediction system based on multi-scale big data fusion, and the system comprises a geological big data management module and a big data mining analysis module which are respectively used for carrying out the management and mining analysis of geological, geophysical, geochemical, remote sensing and mineral data; the multi-element extraction module is used for carrying out geological element extraction on the mining analysis result output by the big data mining analysis module; the multi-scale grid calculation module is used for carrying out subdivision and assignment on the geological elements through a multi-scale multi-element grid technology; the intelligent prospecting prediction module is used for compiling a result map; the intelligent prospecting evaluation module is used for compiling an optimal decision diagram; the auxiliary decision-making module generates a prospecting result report; and the prospecting dispatching command module is used for synchronously updating the data in the prospecting dispatching command module. By integrating the prospecting prediction basic data set, the knowledge base, the model base and the algorithm base, flexible scheduling and convenient use of various data, knowledge, models and algorithm tools are achieved.

The invention relates to the technical field of AI intelligent customer service, in particular to an AI intelligent customer service multi-channel interaction and data analysis system which comprises a multi-channel access module, an interaction data preprocessing module, a multi-modal intention analysis module, a dynamic strategy generation module, a data capitalization management module and a visual feedback module. The multi-channel access module is used for receiving multi-source heterogeneous interaction data; the interaction data preprocessing module generates standardized interaction data; the multi-modal intention analysis module generates a multi-dimensional user portrait and a real-time session path; the dynamic strategy generation module outputs adjustment parameters of a dynamic service strategy; the data asset management module generates a data asset packet with a timestamp; and the visual feedback module is used for generating an interactive decision graph. According to the invention, the data security, the customer service intelligence level and the decision optimization capability are improved, and finally an efficient, accurate and safe intelligent customer service system is realized.

The invention discloses an intelligent monitoring method and system for the construction progress of a building decoration curtain wall. The method comprises the steps that multi-source construction data of a construction site are collected through Internet of Things equipment and a high-definition camera; constructing a multi-dimensional tensor model and extracting data association features by applying a local reversible mapping algorithm to generate a standardized feature data set; performing parallel computing through shared memory hierarchical process mapping by utilizing the standardized feature data set, and outputting a project progress state evaluation result; constructing a decision diagram based on the project progress state evaluation result, and generating a construction progress deviation report; predicting a construction progress trend by adopting a sample optimal private regression algorithm and generating an optimization scheme; and visually displaying the construction progress prediction report and carrying out early warning. According to the invention, the problems of low data integration efficiency, delayed decision response and weak multi-dimensional data association analysis capability of traditional curtain wall construction monitoring are solved, and intelligent and efficient monitoring of the construction progress of the building decoration curtain wall is realized.

The invention discloses an agent inference system based on multiple maps, and relates to the technical field of artificial intelligence, and the system comprises the steps: based on industry report, academic literature and business manual multi-source data, extracting entity-relationship-attribute, and constructing a knowledge map; dynamically capturing cooperation, report and task allocation relationships among entities, and constructing a production relationship graph; constructing a decision graph based on a field expert heuristic rule; based on the thinking engineering theory, human thinking modes and emotional states are analyzed, and a thinking map is constructed; integrating a knowledge graph, a production relation graph, a decision graph and a thinking graph, performing entity and mode alignment, mapping multi-source nodes and edges into the same vector space, introducing conflict resolution, and constructing a unified multi-mode heterogeneous knowledge graph; and guiding a large language model to generate a reasoning direction through path cue word injection, querying a constraint reasoning boundary, outputting an optimal reasoning result, and realizing agent reasoning of multiple maps. The method has the beneficial effect that the reasoning accuracy is improved.

A decision support system and a computer-implemented method of enterprise decision support include a data consolidation module, domain-specific machine learning models, an enterprise decision intelligence model, and a user interface layer. The data consolidation module collects data from both internal and external data sources. The domain-specific machine learning models generate decisions based on the collected data. The enterprise decision intelligence model integrates real-time trends and the decisions to provide context-aware recommendations. The enterprise decision intelligence model maintains a decision graph that connects decision variables of the domain-specific machine learning models in a causal relationship, with the domain-specific machine learning models interacting as an interconnected network. The decisions are influenced by affects of decision variables from other domain-specific machine learning models. The user interface layer facilitates interactive decision-making by way of the enterprise decision intelligence model and visualizing the recommendations.

The invention provides a tobacco area nitrogen management and efficiency evaluation system, which comprises a data acquisition module for acquiring meteorological data, soil nitrogen data, crop growth data and historical fertilization data in real time; the data preprocessing module is used for carrying out noise filtering, space-time alignment and multi-source data fusion; the dynamic nitrogen prediction module is coupled with the soil nitrogen migration and crop absorption model to generate a prediction value; the optimization decision module is used for generating an application strategy based on a dynamic programming algorithm and calculating the optimal application amount and the fertilization time; the efficiency evaluation module is used for calculating nitrogen utilization efficiency (NUE) and environmental benefit index (EBI) by combining the application amount, the environmental loss amount and the crop yield; the self-adaptive feedback module is used for dynamically correcting prediction model parameters according to the deviation degree of the NUE and the EBI; and the visual output module is used for generating a multi-dimensional evaluation report and a fertilization decision map. The nitrogen utilization efficiency and the environmental benefit index can be improved, the environmental risk is reduced, and the tobacco yield and quality are improved.

The invention discloses a data processing method and device of a data center network, equipment, a medium and a product, and relates to the technical field of data processing, and the method comprises the steps: obtaining network topology information and a data forwarding table rule corresponding to each equipment subnet in the data center network; performing parallel coding on each piece of network topology information and each data forwarding table rule based on a network parallel binary decision diagram to obtain a data forwarding path structure between subnets on each piece of equipment, the network parallel binary decision diagram being generated based on a lockless index and hierarchical cache; based on the path connectivity corresponding to the data forwarding path structure, constructing a data forwarding graph corresponding to the subnet on each device; and verifying the data reachability between the subnets on each device according to the data forwarding graph, and generating a data reachability verification result. According to the technical scheme, the concurrency limitation is overcome, high concurrency can be achieved, and the data plane verification efficiency of the data center network is improved.

The invention relates to the technical field of protein related data processing, in particular to a biological information processing system and method applied to synthetic biology. The method comprises the following steps: acquiring real-time proteomics data, and performing polymorphic context decoding to obtain a protein expression context matrix; analyzing interaction of behavior characteristics in the protein expression context matrix to obtain a multi-scale causal structure map; executing structure-function transformation rule extraction in the multi-scale causal structure atlas to obtain a function mapping unit set; performing evaluation based on the function mapping unit set so as to form a configuration decision diagram; simulating paths in the configuration decision diagram, and performing path adaptability scoring to obtain a path adaptability feedback table; and optimizing the path adaptability feedback table to obtain an optimal expression configuration set. According to the method, the precision, the stability and the controllability of protein information in the process of structural analysis, function recognition and regulation path construction can be improved.

The invention discloses an intelligent fire-fighting hidden danger identification method based on multi-modal fusion, and the method comprises the steps: collecting multi-source heterogeneous data in a building environment, and carrying out the unified processing of the data, and obtaining standardized multi-modal original input data; event anchor point detection is carried out on the multi-modal input, key events are extracted, and a corresponding multi-modal event sequence is generated; constructing an event graph containing time and causal edges, and outputting an alignment event stream by using a space-time coupling causal alignment module; importing BIM and air duct structure information to establish a spatial topological graph, and fusing event streams to generate spatial constraint features; constructing a self-evolution semantic decision map based on the fusion features, and dynamically adjusting node weights and edge connection relationships; and carrying out hidden danger identification and grade division on the real-time data by using the decision map, and outputting an early warning signal and positioning information. According to the method, high-precision identification and intelligent early warning of fire-fighting hidden dangers are realized through space-time causal alignment, spatial topology fusion and self-evolution semantic decision of multi-modal data.

The invention discloses a bearing part intelligent detection and management system based on machine vision, and relates to the technical field of bearing manufacturing and detection. The method is used for solving the problems of bearing surface foreign matter recognition and pollution source tracking optimization. The system comprises a phase order correction and reconstruction module which generates a regularization weight factor through a high-frequency phase gradient, dynamically inhibits an order jump error of a raceway steep region, and outputs a high-precision three-dimensional shape and a gradient field; the coupling analysis module synchronously calculates local curvature entropy and polarization scattering intensity to realize foreign matter type identification and accurate positioning; the stress mapping decision module maps the foreign matter coordinates to a Hertz contact stress field, calculates a service life attenuation value in combination with a stress amplification coefficient, and generates a sorting priority decision map; and the pollution source reverse optimization module is used for dynamically adjusting dust removal equipment parameters based on foreign matter coordinate space clustering and assembly equipment position association. According to the system, closed-loop intelligent management of foreign matter detection and pollution source control is realized, and the bearing product quality and the production efficiency are improved.

The invention relates to the field of power quality monitoring of a power system, in particular to a comprehensive power quality monitoring method and system based on an intelligent fusion terminal, and the method comprises the steps: obtaining and preprocessing power quality data of the intelligent fusion terminal, dividing electrical partitions, determining dominant harmonic components, and calculating the electrical distance between terminals in the same partition. And based on density peak clustering, combining feature difference and electrical distance weight fusion to obtain a similarity distance, determining an adaptive cutoff distance by using a low-density region adjustment factor, and drawing a decision diagram to identify a clustering center. Screening clustering results, analyzing electric energy quality characteristic dominance, identifying a mixed cluster and re-clustering, and positioning a disturbance source by using an attenuation law in combination with a significant clustering result and an electrical distance. According to the method, the accuracy of disturbance source positioning is improved through adaptive clustering and mixed cluster analysis, and the operation condition of the power grid and multiple disturbance sources can be accurately identified through the adaptive truncation distance and the adjustment factor of the low-density region.

The invention provides a method and a system for constructing a multi-agent collaborative decision graph for traffic control. The method comprises the following steps: generating a dynamic multi-dimensional traffic decision map through multi-source traffic, traffic resources and multi-source data of traffic events of a road network system, establishing a model, predicting and evaluating a simulation result and an optimization decision result of the road network system, iterating the map, and generating a strategy according to user vehicle priority association nodes in combination with the map. Generating feedback data for a decision instruction of a traffic environment, inputting the feedback data into a map for optimization and updating, simulating, deducing and selecting an optimal scheme according to a vehicle travel urgency degree, and generating an instruction execution scheme; therefore, active deployment, collaborative optimization and adaptive evolution of traffic control are realized, and the operation efficiency and safe operation capability of a road network system are improved.

The invention discloses a vehicle fault diagnosis method, device and equipment, a medium and a product. The method comprises the following steps: acquiring real-time vehicle diagnosis data, and extracting a diagnosis vector of the vehicle diagnosis data through a pre-trained language model; determining fault association knowledge according to the diagnosis vector and a fault vector database; constructing a fault diagnosis decision diagram according to the fault knowledge graph, the maintenance manual and the historical work order; based on a graph neural network model, extracting a fault reasoning sub-graph from the fault diagnosis decision graph according to the diagnosis vector; converting a node relationship in the fault reasoning sub-graph into a natural language reasoning chain; according to the vehicle diagnosis data, the fault association knowledge and the natural language reasoning chain, constructing prompt words; and generating a fault diagnosis result based on a large language model and the cue word. According to the embodiment of the invention, a more efficient and accurate solution can be provided for remote vehicle fault diagnosis.

This application relates to the field of quantum computing technology, and in particular to a method and apparatus for accelerating quantum circuit simulation based on decision graphs. The method includes: acquiring a decision graph corresponding to the matrices of several ordered quantum logic gates in a quantum circuit to be simulated; calculating the simulation computation cost corresponding to the matrices based on the decision graph; performing a phased fusion optimization process on the matrices of the several quantum logic gates according to the simulation computation cost to generate one or more fused equivalent quantum logic gate matrices; and performing quantum circuit simulation computation using the fused equivalent quantum logic gate matrices. This application uses gate fusion to reduce the number of matrices that need to be loaded into memory, thus reducing memory access latency. Gate fusion also reduces the number of MVM operations; for example, after fusion of k quantum logic gates, only m MVM operations are required, where m < k, significantly improving the speed of quantum circuit simulation.

The invention belongs to the field of face image processing, and particularly relates to a face recognition method, device and system based on improved density peak clustering. The method specifically comprises the following steps: acquiring a face image data set; calculating a distance matrix and k neighbor density; calculating the number of support points and the relative distance; calculating local density; constructing a decision diagram of the face image data set based on the relative distance and the local density, and selecting a plurality of front face feature vector points with a relatively large product of the relative distance and the local density as a clustering center; distributing the undistributed samples greater than the average collaborative similarity to the corresponding class clusters; distributing the maximum value of the neighbor information matrix and the corresponding undistributed sample to the corresponding class cluster; and adding all the undistributed samples into the corresponding class clusters to obtain a final recognition result of the original face image data set. According to the method, clustering centers of different faces can be accurately identified in a data set with density difference, and class clusters of any shape can be processed. The situation that one mistakenly distributed face image in the face image cluster affects distribution of the face images with the similar features around the face image is avoided, and the accuracy of face recognition is improved.

The invention relates to the technical field of power monitoring, and discloses a power module operation state monitoring method and system, and the method comprises the steps: carrying out the multi-frequency calibration of a high-frequency electrical signal of a power module, and obtaining a standardized multi-dimensional electrical signal; decomposing the standardized multi-dimensional electrical signal into a limited bandwidth modal component, and performing Hilbert transform to obtain a high-dimensional state feature vector; performing local linear embedding dimension reduction on the high-dimensional state feature vector to obtain a low-dimensional reference coordinate; acquiring a low-dimensional monitoring coordinate of the current high-dimensional state feature vector; constructing a decision diagram of the low-dimensional reference coordinates, and performing clustering center-to-center spacing analysis on boundary points in the decision diagram to obtain a dynamic adaptive threshold value; state deviation degree analysis is carried out on the power module, difference comparison is carried out on an analyzed state abnormity index and a dynamic self-adaptive threshold value, and a state abnormity early warning signal of the power module is generated; according to the invention, the efficiency of monitoring the running state of the power module can be improved.

1. Name of the Design Product: Graphical User Interface for Urban Bus Information Decision-Making for Electronic Devices. 2. Use of the Design Product: An electronic device. 3. Design Key Points of the Design Product: Lies in the content of the graphical user interface. 4. Picture or Photograph Most Illustrating the Design Key Points: Front View. 5. Use of the Graphical User Interface: Used for displaying and correlating bus operation data, conducting technical statistical analysis, and providing functions such as basic data, operation monitoring, operation decision-making, and safety services. 6. Description of the Changing States of the Graphical User Interface: Click the "Basic Information" button at the bottom in the Front View to enter the Changing State Diagram 1; click the third circular button icon from top to bottom on the left in the middle (the icon corresponding to the "Operation Group 1" pop-up menu in the Changing State Diagram 2) and the second circular button icon from top to bottom on the right in the middle (the icon corresponding to the "XX Bus Company" pop-up menu in the Changing State Diagram 2) in the Changing State Diagram 1 to enter the Changing State Diagram 2; click the area where "On-the-Job Personnel" and "On-the-Job Drivers" are located on the left in the Changing State Diagram 2 to enter the Changing State Diagram 3; click the area where "Number of In-Service Vehicles" and "Number of Trams" are located on the left in the Changing State Diagram 3 to enter the Changing State Diagram 4; click the area where "Number of Routes" and "Number of Stops" are located on the left in the Changing State Diagram 4 to enter the Changing State Diagram 5; click the "Stops" button in the upper left corner in the Changing State Diagram 5 to enter the Changing State Diagram 6; click the area where "Number of Bus Terminals", "Number of Maintenance Yards", "Number of Charging Terminals", and "Number of Electronic Signboards" are located on the left in the Changing State Diagram 6 to enter the Changing State Diagram 7; click the "Operation Monitoring" button at the bottom in the Changing State Diagram 7 to enter the Changing State Diagram 8; click any item below the "Real-Time Alarm" list on the right in the Changing State Diagram 8 to enter the Changing State Diagram 9; click the "Operation Decision-Making" button at the bottom in the Changing State Diagram 9 to enter the Changing State Diagram 10; click the "Safety Service" button at the bottom in the Changing State Diagram 10 to enter the Changing State Diagram 11; click the drop-down menu of "Operation Group 1" at the top in the Changing State Diagram 11 and select "Group" to enter the Changing State Diagram 12. 7. Other Situations Requiring Explanation: Other Explanation: The light gray blocks in the Changing State Diagram 9 are the content screens.

A minimal unsatisfiable set detection apparatus, which detects a minimal set from a set of observed events that are determined to be inconsistent with a knowledge base, includes a decision graph acquisition unit 31 that acquires a decision graph that outputs a boolean value of an observed event from the knowledge base, an observed event acquisition unit 32 that acquires a plurality of observed events determined to be inconsistent with the knowledge base, a minimal set covering derivation unit 33 that obtains a minimal set covering by converting a plurality of observed events, which are acquired by the observed event acquisition unit 32, into a minimal set covering problem using the decision graph, and an output unit 34 that outputs a calculation result by the minimal set covering derivation unit 33.

The application relates to the technical field of data mining, and discloses an intelligent mining system for research data, which comprises a heterogeneous data acquisition module, a semantic association analysis module, a decision graph generation module, a time sequence feature correction module and a knowledge distillation optimization module. The heterogeneous data acquisition module captures data features and constructs a multi-layer topology through a multi-source data perception node and a dynamic dimension fusion network; the semantic association analysis module analyzes semantic association by using a concept topology modeling unit, a knowledge vector clustering unit and a multi-modal switching link; the decision graph generation module generates a core decision benchmark framework based on a strategy optimization node and a rule reasoning engine; the time sequence feature correction module performs time sequence correction and noise compensation on semantic association; and the knowledge distillation optimization module detects deviation through an entity relationship evaluation network and feeds back an optimized decision framework. The system realizes intelligent acquisition of multi-source data, dynamic analysis of semantics and adaptive generation of a decision graph, and improves the precision and efficiency of research data mining.

The invention provides a flow scheduling control method and system based on a rule decision diagram, a medium and equipment, and the method comprises the steps: obtaining a service request of a user side, and carrying out the recognition based on the service request to obtain a service scene code; processing the service request by using the constructed service decision diagram to obtain a processing result, and performing result judgment on a function set in the service decision diagram based on the service scene code; the processing result is fed back to the user side, the processing result at least comprises an end result, and the end result is correspondingly from a check point or an end node in the service decision diagram. Through platform integration and link calling, flexible process arrangement and function scheduling are innovatively realized, corresponding operations can be automatically triggered according to different conditions and requirements by configuring the rule set, the rule can be flexibly configured in real time to perform function combination under complex services, the efficiency of the system is improved, and the system performance is improved. And the difficulty of complex service logic processing is reduced.

The application relates to a network reliability evaluation method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: based on all feasible lower bound vectors of a target network, using a multi-element decision graph to represent all feasible lower bound vectors meeting the service requirement of the target network, and obtaining a feasible lower bound decision graph; performing state space expansion on the feasible lower bound decision graph to obtain a link feasible capacity decision graph; based on a link set of the target network, using a multi-element decision graph to represent the capacity state of each link of the target network, so as to obtain a link state decision graph of each link; merging the link feasible capacity decision graph and the link state decision graph of all links of the target network to obtain a capacity reliability decision graph; and using the capacity reliability decision graph to obtain the capacity reliability of the target network. The method can expand the reliability evaluation method to a multi-layer network, so that the capacity reliability of a complex multi-layer actual network can be accurately described.

The embodiment of the invention discloses a substation operation and maintenance method and device based on multiple agents, equipment and a medium. A specific embodiment of the method comprises the steps of obtaining a real-time operation data set and an intelligent agent coordination graph in response to determining that the volume of a transformer substation is higher than a target volume; performing graph division on the agent coordination graph to obtain at least one agent set; for each agent group set, executing a generation step: for each agent group, generating group decision information; displaying the group decision information set on a decision page; generating an agent decision diagram; executing agent screening of the coordination agent set to obtain a coordination agent subset and agent decision information; generating operation and maintenance information; and according to the operation and maintenance information, controlling the main agent to indicate each coordination agent so as to carry out equipment operation and maintenance processing on the target substation. According to the embodiment, under the condition of reducing agent computing resources, accurate generation of the operation and maintenance information corresponding to the transformer substation can be achieved, and normal operation of the transformer substation is guaranteed.

1. Name of the product in this design: Graphical User Interface for Railway Packing and Assembling Decision-Making of Electronic Equipment. 2. Purpose of this design: An electronic device. 3. The key design features of this product are its graphical user interface content. 4. The image or photograph that best illustrates the design's key features: the front view. 5. The design for which protection is sought includes color. 6. Purpose of the graphical user interface: By predicting the future location and empty / load status of all online ladles in real time, based on the blast furnace distribution plan, and aiming to improve the ladle turnover rate, while considering the principle of safe tapping of iron in the blast furnace, it recommends reasonable ladle matching schemes for the iron tapping batches that are currently being tapped and the iron tapping batches that will be tapped in the future. It also allows users to view the locomotive's most recent tasks to be performed through detailed routing, thereby achieving rapid turnover of ladles. 7. Human-computer interaction method of graphical user interface: In the main view, click the "Detailed Route" button in the lower right corner to enter the interface change state diagram.

The present invention provides a prospecting prediction system based on multi-scale big data fusion, the system comprising: a geological big data management module and a big data mining and analysis module, respectively for managing and mining and analyzing geological, geophysical, geochemical, remote sensing, and mineral data; a multi-factor extraction module for extracting geological elements from the mining and analysis results output by the big data mining and analysis module; a multi-scale grid calculation module for segmenting and assigning values ​​to geological elements using multi-scale multi-factor grid technology; an intelligent prospecting prediction module for compiling result maps; an intelligent prospecting evaluation module for compiling optimal decision diagrams; a decision-making support module for generating prospecting result reports; and a prospecting scheduling and command module for synchronously updating data in the prospecting scheduling and command module. By integrating a basic prospecting and prediction data set, a knowledge base, a model base, and an algorithm base, the present invention enables flexible scheduling and convenient use of various data, knowledge, models, and algorithm tools.

The invention discloses a multi-point collaborative conference large-screen interaction management system, and relates to the technical field of network communication. The system comprises a shared content objectification analysis module for analyzing the shared content into an interactive content object tree; the user interaction intention pre-judgment module is used for monitoring user intention and generating an initial decision node serving as a discussion starting point; the parallel topic session management module is used for creating a parallel disseminating session with automatically inherited context; the decision-making process association generation module is used for displaying logic association of the topics by constructing a decision diagram; and the conference state machine control module is used for managing focusing, diverging and converging conference process states. According to the method, the core interaction, discussion branch and decision-making process of the conference are subjected to comprehensive structured, traceable and visual management, so that the collaborative efficiency and decision-making quality of complex issues are remarkably improved.

The invention belongs to the technical field of building environment simulation, and relates to a multi-scheme comparison urban landscaping microclimate effect evaluation method, which comprises the following steps of: obtaining an environment basic data set; generating a spatial relation fingerprint parameter group including a wind direction response chain parameter, a water-heat coupling degree parameter and a shadow conflict domain parameter; generating a design scene configuration file; outputting an average radiation temperature index set, a local wind speed distribution diagram and temperature abnormal region mark data with space coordinates; based on the average radiation temperature index set, designing a scene configuration file and a spatial relation fingerprint parameter set, constructing an effect separation model, and generating effect contribution decoupling data; and based on the effect contribution decoupling data and the building layout vector diagram, generating a two-color optimization decision map marked with a green optimization area and a red avoidance area. The problem that local wind speed is too low or heat is gathered due to improper layout is solved.

The application relates to the technical field of protein-related data processing, in particular to a biological information processing system and method applied to synthetic biology. The method comprises the following steps: acquiring real-time proteomics data, performing polymorphic context decoding, and obtaining a protein expression context matrix; analyzing the interaction of behavior characteristics in the protein expression context matrix, and obtaining a multi-scale causal structure graph; performing structure-function transformation rule extraction on the multi-scale causal structure graph, and obtaining a function mapping unit set; evaluating the function mapping unit set, thereby forming a configuration decision graph; simulating the path in the configuration decision graph, performing path adaptability scoring, and obtaining a path adaptability feedback table; and optimizing the path adaptability feedback table, thereby obtaining an optimal expression configuration set. The application helps to improve the accuracy, stability and controllability of protein information in the structural analysis, function identification and regulation path construction process.