12results about How to "Solve the island problem" patented technology

The invention relates to the technical field of intelligent medical treatment, and discloses a pancreatic cancer evolution simulation and dynamic intervention system based on multi-model driving, and the system comprises a multi-source data fusion module which is used for accessing and processing patient data, and forming a multi-dimensional feature set; the model fusion construction module is integrated with a model coupler and can construct a patient-specific integrated calculation model; the twinborn calibration generation module is used for generating pancreatic cancer digital twinborn matched with the initial state of the patient; and the evolution simulation and intervention module is used for running the pancreatic cancer digital twins, simulating the natural evolution trajectory of the tumor and carrying out quantitative simulation on the input virtual treatment scheme effect. According to the method, by constructing the patient-specific digital twins, normal form transformation from static diagnosis to dynamic prediction is achieved, and a quantitative evaluation basis can be provided for selection of clinical treatment schemes.

A barrier gate management system comprises a barrier gate management server, a barrier gate control mechanism for controlling opening and closing of a gate rod, and a near field communication device, the near field communication device sends an instruction signal to the barrier gate control mechanism, and the near field communication device sends the instruction signal to the barrier gate control mechanism corresponding to the near field communication device side. The method comprises the following steps: (S1) responding to an off-network barrier gate opening and closing request of a vehicle user side intelligent equipment terminal, and receiving first identity data and a barrier gate opening and closing instruction request sent by the vehicle user side intelligent equipment terminal by the near field communication equipment; (S2) the near field communication equipment locally verifies first identity data of the vehicle user, and sends a barrier gate opening and closing instruction to a barrier gate control mechanism according to a local verification result and a barrier gate opening and closing instruction request sent by a user side intelligent equipment terminal; and (S3) the near field communication equipment returns an identity verification result and / or an authorized passing state to the vehicle user side intelligent equipment terminal. When the parking area has the network disconnection scene, communication interaction with the near field communication equipment is carried out through first identity data sent by the vehicle user side intelligent equipment terminal, so that opening and closing of the barrier gate in the network disconnection scene are completed; the problems that in the prior art, in a network interruption scene, vehicle access of a user is blocked, manual rod lifting is needed, and the user experience feeling of property and vehicle users is poor are solved.

This invention relates to the field of photovoltaic power generation fault diagnosis technology, and in particular to a photovoltaic array fault diagnosis system and method based on artificial intelligence. By encrypting and preprocessing multi-source data, a multimodal dataset is constructed, providing a comprehensive and collaborative data foundation for subsequent analysis. The collaborative diagnosis model can analyze the battery and inverter status in parallel. By combining circuit equivalent models, spatial adjacency relationships, and theoretical power models for calculation, the diagnosis results have physical interpretability. A physical consistency verification rule based on power flow analysis is introduced to cross-validate and logically fuse the preliminary diagnosis results, generating reliable comprehensive fault diagnosis results. This approach solves the technical problem in existing technologies that focus on independent analysis of single components such as photovoltaic cells or inverters, lacking effective fusion and collaborative utilization of multi-source heterogeneous data, which limits the accuracy of diagnosis results.

The invention discloses an industrial equipment data acquisition and forwarding system and method based on multi-protocol adaptation, and the system comprises a protocol adaptation layer which is used for interacting with various types of industrial equipment and is integrated with a plurality of protocol drive plug-ins; the data standardization processing layer is used for receiving and analyzing the original data message, mapping the data of the original data message according to a preset mapping rule and converting the data into a unified data model; the data routing forwarding layer is used for receiving the standardized data objects of the data standardization processing layer and forwarding the standardized data objects meeting matching conditions to corresponding output channels according to a rule engine with the pre-configured matching conditions; and the configuration management module is used for configuring and managing the protocol adaptation layer, the data standardization processing layer and the data routing forwarding layer. According to the invention, the target path of the data is changed from hard coding to configurability, and the forwarding and storage paths of the data are configured through the configuration rule, so that the method is more flexible.

The embodiment of the invention provides a latent guest mining method and related equipment, and belongs to the technical field of latent guest identification. The method comprises the steps of obtaining platform behavior data of a first object; the platform behavior data is obtained by performing identity unification on original behavior data which are obtained from a plurality of different platforms and are associated with the first object; performing feature extraction according to the platform behavior data to obtain a car purchase behavior feature and a social behavior feature of the first object; the vehicle purchase behavior characteristics are related to individual vehicle purchase behaviors; the social behavior characteristics are related to group vehicle purchasing behaviors; determining an individual purchase intention of the first object based on the car purchase behavior characteristics; determining a group purchase impact of the first object based on the social behavior features; analyzing based on the individual purchase intention and the group purchase influence, and determining the customer state of the object; according to the embodiment of the invention, the automobile sales conversion rate can be effectively improved through the technical means of unification of multi-platform data identities and individual group double-aspect analysis.

The invention discloses an equipment specialty affair management method and management system based on a process group, and the management method comprises the steps: (1) analyzing the business processes of all corresponding equipment management according to different equipment management businesses and equipment specialties of an enterprise, determining the relevance between the business processes, and calculating the correlation coefficient and the correlation degree; (2) determining a core process and an affiliated process according to the correlation coefficient and the correlation degree, and constructing a process group; (3) determining and configuring structured form information of a core process and an affiliated process in the process group; (4) determining a trigger mechanism and a closed-loop rule of an affiliated flow in the flow group, and establishing automatic configuration of the structured data; and (5) establishing a flow group evaluation mechanism. According to the method, the flow group taking the core flow as the center is established according to the business correlation, so that flow integration and input and output data homology are realized, repeated input is reduced, flow closed-loop confirmation work is reduced, and the working quality and working efficiency of equipment management are improved.

The invention discloses a financial service decision-making method and device, electronic equipment and a storage medium, and relates to the field of artificial intelligence or other related technical fields, and the method comprises the steps: receiving a financial service request of a user side, and carrying out the analysis of the financial service request, and obtaining the service information and the service type; calling N artificial intelligence agents based on the service type, and combining the N artificial intelligence agents to obtain an artificial intelligence decision body; inputting the service information into an artificial intelligence decision-making body, and performing service decision-making through the artificial intelligence decision-making body to obtain a service decision-making result; and obtaining a confidence value of the intelligent decision body, pushing the service decision result to the service terminal for correction when the confidence value is smaller than a preset confidence threshold, and executing the corrected service decision result. According to the method and the device, the technical problem of relatively low service decision accuracy caused by adopting a rule-based single artificial intelligence model to assist in service decision in related technologies is solved.

The invention discloses a special equipment inspection data intelligent analysis method based on natural language understanding, and belongs to the technical field of special equipment safety supervision and big data analysis. The method comprises the following steps: receiving and analyzing a natural language request, and extracting a target use unit name and a risk analysis dimension; searching and constructing a device analysis group and matching a risk evaluation model; for each device in the group, extracting related data from the multi-source database; carrying out quantitative calculation by utilizing the matching model, and generating a single-equipment risk score and a risk factor label; carrying out aggregation analysis on a total-amount equipment result, and identifying an overall risk level, a dominant risk type and a high-risk equipment distribution characteristic of the unit; and finally, differential supervision suggestions are generated on the basis. According to the invention, full-process automation from natural language instructions to intelligent risk assessment and decision suggestions is realized.

The invention discloses a digital mission engineering development method based on a meta-model library. The digital mission engineering development method is used for modeling of a maritime search and rescue task complex system. The method comprises the following steps: constructing a system model for currently input maritime search and rescue task data and modeling problems based on a pre-constructed meta-model library of a maritime search and rescue task system; according to the method, standardized processes of digital mission engineering are set, including activities including scene conception, mission definition, process analysis, resource generation, experimental design, simulation deduction and evaluation optimization, the activities are refined into action sequences, each action is clearly defined, and a standard method and a working template are provided for the digital mission engineering. According to the method, the problems of incomplete scene description, unclear mission definition, nonstandard architecture design, insufficient verification and evaluation and the like during modeling of the maritime search and rescue system are solved, the standardized process of modeling of the maritime search and rescue task system is realized, the modeling speed and precision are improved, and rapid generation and iteration of system modeling in different scenes are supported.

The invention provides a method and device for constructing an additive manufacturing material database based on in-situ synchrotron radiation data, and relates to the technical field of material databases. The method comprises the steps that a relational database of laser additive manufacturing is constructed based on the synchrotron radiation principle of laser additive manufacturing, transient behaviors in the additive manufacturing process are captured in real time, the dynamic, defect evolution and phase change processes of a molten pool are monitored from microsecond to millisecond, and meanwhile a graphical user interface function is established based on the relational database of laser additive manufacturing. And a user is supported to carry out defect dynamic behavior analysis. According to the method, in-situ capture and data communication of defect dynamic behaviors in the laser additive manufacturing process can be realized remarkably through structured storage, correlation analysis and visual display, and material design, process optimization and quality control are supported.

A product quality prediction system based on federated learning includes a server and clients set up on multiple production lines. The server sends an initial product quality prediction model and fused model parameters to the clients. The fused model parameters are obtained by fusing the model parameters sent by each client. The clients include: multiple sensors for collecting product manufacturing process data; a training set construction module for acquiring sensor data and performing feature extraction to construct a local training set; a model training module for training the acquired product quality prediction model using a forward propagation federated training method based on the local training set; encrypting and sending the model parameters during training to the server and updating the local product quality prediction model according to the fused parameters; stopping training when training requirements are met to obtain a trained product quality prediction model; and a product quality prediction module for performing quality prediction based on the trained product quality prediction model.

The embodiment of the invention discloses a cluster visual monitoring and debugging system and method based on cloud edge collaboration and a medium. Comprising a data acquisition control module deployed at an edge side and used for acquiring multi-source heterogeneous monitoring data such as performance, links and logs of a cluster; a data storage module, wherein a built-in unified architecture server carries out unified modeling and storage on the monitoring data according to cluster topology; and the network data transmission module is responsible for cloud edge two-way transmission of data and forwarding of a control instruction. The cloud side comprises a container cloud platform and is used for data aggregation and instruction issuing; a cloud cluster virtual debugging environment maintained by the system serves as a digital twinborn body of a cluster and is used for high-simulation testing and change drilling; and the visual debugging module is deployed at a cloud end and / or an edge side, constructs a three-dimensional visual scene based on the monitoring data, supports state display and history playback, and directly regulates and controls cluster resources through interactive operation. According to the invention, unified integration of monitoring data, visual intelligent operation and maintenance, and simulation testing of safety are realized.