Abstract: The present disclosure discloses a grid supply load predicting method, a system, and a storage medium. The method includes: determining a characteristic historical data set of a grid supply load; obtaining a grid supply load characteristic trend prediction result by inputting the characteristic data set of the historical grid supply load and the daily characteristic data of target influencing factor into a preset trend prediction model for grid supply load characteristic trend prediction; determining a grid supply load curve type; obtaining a grid supply load prediction model corresponding to the grid supply load curve type to take as a target prediction model; and obtaining a target grid supply load prediction result by inputting the historical data set of the grid supply load, the grid supply load characteristic trend prediction result and the daily characteristic data of target influencing factor into the target prediction model for grid supply load prediction.

Abstract: A non-intrusive load monitoring system, including a device classification and prediction sub-process, a new device identification sub-process, and a classifier self-training sub-process. By means of the configuration of these processes, the defect in accuracy of identification of a new device in the prior art is overcome; and when a device other than the devices in a device database is detected, data of the device can be intercepted and stored in the device database, so that the function of accurately identifying existing devices in a device database can be achieved, and the device database can be automatically updated when a new device other than the devices in the device database is discovered.

Abstract: An output-decision-based negative feedback control method and system. The method includes: receiving, by an output decider, output responses of at least two heterogeneous functional equivalents, and dividing numbers corresponding to the at least two heterogeneous functional equivalents into at least one set according to the output responses; determining, by the output decider, credibility of each set according to the output responses, and sending the at least one set and the credibility corresponding to each set to a feedback controller via decision information; generating, by the feedback controller, a first scheduling policy and/or a second scheduling policy according to the decision information; and sending, by the feedback controller, the first scheduling policy to an input proxy, and/or sending a change instruction to the heterogeneous functional equivalent indicated by the second scheduling policy. The method can prevent in advance and process a heterogeneous functional equivalent that may be faulty.

Abstract: A method for automatically sensing attack behaviors, the method including: distributing a service request from a network switch to a response module, where the response module includes a main controller configured for data interaction processing and an auxiliary controller configured for interactive data processing; generating, by the main controller and the auxiliary controller in the response module, respective response data according to the service request, respectively; and comparing the respective response data of the main controller with the respective response data of the auxiliary controller; if a result of comparison is inconsistent, indicating the network switch is abnormal, an administrator is informed, and the response data generated by the auxiliary controller is fed back to the network switch; and, if the result of comparison is consistent, the response data generated by the main controller is fed back to the network switch.

Abstract: Disclosed is an optimal power flow acquiring method for regional distribution network of small hydropower groups based on deep learning, which specifically includes the following steps: generating required data sets by adopting continuous power flow and power flow equation calculation methods; the data set is randomly divided into training data (80 percent) and test data (20 percent); training the built convolutional neural network model with training data to learn the mapping relationship between load and generator output power; inputting test data, and directly obtaining PG and QG from the trained convolutional neural network; and solving residual variables Vi and ?i with traditional power flow solver. The application can accelerate the solving speed of the optimal power flow problem with higher prediction accuracy.

Abstract: Provided is a system for verifying integrity of a machine learning model, the system includes at least one processor programmed or configured to determine whether an output of a machine learning model based on an input corresponds to a reference output of the machine learning model based on the input, serialize the machine learning model into a file, calculate a file integrity value of the file using a file integrity detection function, determine whether the file integrity value corresponds to a reference file integrity value of the file, and perform an operation with the machine learning model based on determining that the file integrity value corresponds to the reference file integrity value of the file. A method and computer program product are also disclosed.

Abstract: A data system may dynamically prioritize and ingest data so that, regardless of the memory size of the dataset hosted by the data system, it may process and analyze the hosted dataset in constant time. The system and method may implement a first space-efficient probabilistic data structure on the dataset, wherein the dataset includes a plurality of profile data. It may then receive update data corresponding to some of the plurality of profile data and implement a second space-efficient probabilistic data structure on the dataset including the update data. The system and method may then determine a set of non-shared profile data of the second space-efficient probabilistic data structure and prioritize the set of non-shared profile data of the second space-efficient probabilistic data structure over other profile data of the dataset for caching.

Abstract: A method for automatically sensing attack behaviors, the method including: distributing a service request from a network switch to a response module, where the response module includes a main controller configured for data interaction processing and an auxiliary controller configured for interactive data processing; generating, by the main controller and the auxiliary controller in the response module, respective response data according to the service request, respectively; and comparing the respective response data of the main controller with the respective response data of the auxiliary controller; if a result of comparison is inconsistent, indicating the network switch is abnormal, an administrator is informed, and the response data generated by the auxiliary controller is fed back to the network switch; and, if the result of comparison is consistent, the response data generated by the main controller is fed back to the network switch.

Abstract: An output-decision-based negative feedback control method and system. The method includes: receiving, by an output decider, output responses of at least two heterogeneous functional equivalents, and dividing numbers corresponding to the at least two heterogeneous functional equivalents into at least one set according to the output responses; determining, by the output decider, credibility of each set according to the output responses, and sending the at least one set and the credibility corresponding to each set to a feedback controller via decision information; generating, by the feedback controller, a first scheduling policy and/or a second scheduling policy according to the decision information; and sending, by the feedback controller, the first scheduling policy to an input proxy, and/or sending a change instruction to the heterogeneous functional equivalent indicated by the second scheduling policy. The method can prevent in advance and process a heterogeneous functional equivalent that may be faulty.

Abstract: Provided is a system for verifying integrity of a machine learning model, the system includes at least one processor programmed or configured to determine whether an output of a machine learning model based on an input corresponds to a reference output of the machine learning model based on the input, serialize the machine learning model into a file, calculate a file integrity value of the file using a file integrity detection function, determine whether the file integrity value corresponds to a reference file integrity value of the file, and perform an operation with the machine learning model based on determining that the file integrity value corresponds to the reference file integrity value of the file. A method and computer program product are also disclosed.

Abstract: The present disclosure provides a software-defined interconnection method and apparatus for heterogeneous protocol data, including: determining ports respectively corresponding to multiple network node devices connected with a software-definable network switching device; configuring each of the ports corresponding to a respective network node device according to a protocol type corresponding to the network node device, to obtain the first configuration information, and configure a port rate of the port according to a transmission rate of data of the network node devices, to obtain the second configuration information; receiving a data packet sent by the connected network node device through each of the ports; determining whether protocol conversion needs to be performed according to the protocol type of the destination port of the data packet, wherein if needed, the data packet sent from the port is encapsulated into the protocol type of the destination port, and sent to the destination port.

Abstract: A data system may dynamically prioritize and ingest data so that, regardless of the memory size of the dataset hosted by the data system, it may process and analyze the hosted dataset in constant time. The system and method may implement a first space-efficient probabilistic data structure on the dataset, wherein the dataset includes a plurality of profile data. It may then receive update data corresponding to some of the plurality of profile data and implement a second space-efficient probabilistic data structure on the dataset including the update data. The system and method may then determine a set of non-shared profile data of the second space-efficient probabilistic data structure and prioritize the set of non-shared profile data of the second space-efficient probabilistic data structure over other profile data of the dataset for caching.

Abstract: A method and an apparatus for forwarding a heterogeneous protocol message, and a network switching device are provided, the method includes: receiving a network message; parsing the network message to obtain a destination address of the network message; looking up in a preset forwarding table a port corresponding to the destination address and a first protocol corresponding to the port; judging whether the first protocol matches a second protocol of the network message; performing, if not, protocol conversion on the network message in accordance with a preset conversion rule, so that the protocol type of the network message matches the port; forwarding the protocol-converted network message through the port. In the present disclosure, protocol conversion can be performed on messages from different protocol networks, so that the network switching device implements communication between different networks, and has a flexible protocol processing mode and powerful scalability.