Abstract: Provided are a method, apparatus and system for storing fault data, an embodiment of the fault data storage method including: acquiring fault data of a target electrical device; sending a consensus request for the fault data, usable to request that consensus personnel who use the consensus client reach a consensus for the reason why the target electrical device is faulty, to at least one consensus client; respectively receiving a consensus result from each consensus client, the consensus result being formed by the consensus client according to the triggering of the consensus personnel; determining, according to each received consensus result, whether the reason for why the target electrical device is faulty is due to device quality; generating, upon the reason for why the target electrical device is faulty being due to device quality, a first data block which contains the fault data; and storing the first data block into a blockchain.

Abstract: Provided are an electric motor, a laminated iron core, and a manufacturing method. In. an embodiment, the method includes S1: introducing inert gas into an additive manufacturing printing apparatus, pouring silicon steel metal particles into a fanning cylinder of the apparatus, and performing laser scanning on the silicon steel metal particles to gradually melt the silicon steel metal particles into at least one silicon steel metal layer; and S2: continuing to pour silicon steel metal particles into the forming cylinder, and stopping performing laser scanning on the silicon steel metal particles or reducing the laser power executing the laser scanning, such that the silicon steel metal particles do not entirely melt and form an insulating layer. Execution of steps S1 and S2 is alternated until a laminated iron core having a plurality of alternating silicon steel metal layers and insulating layers is formed.

Abstract: Various embodiments include a method for labeling a data point comprising executing a labeling operation on a target data set, wherein the target data set comprises a plurality of data points, each data point representing a service instance. The labeling operation comprises dividing the target data into subsets. For each subset, then: receiving input designating a mark for a first data point, illustrating the situation of the service instance represented by the data point; determining whether the similarity between the mark and a mark previously designated for a second data point in the target data set satisfies a preset condition; if the condition is not satisfied, taking the first subset as a target data set to re-execute the labeling operation; and if the condition is satisfied, setting, for each data point, a mark associated with the mark previously designated for a data point in the target data set.

Abstract: When a force sensor on a robot arm detects that the force of contact between an end of a calibration device and a calibration plate reaches a threshold, the robot arm stops, and the end of the calibration device performs marking at the contact position between the end of the calibration device and the calibration plate. The robot arm moves upward and stops at a position where the end of the robot arm is at a predetermined height. At this position, a camera at the end of the robot arm photographs marks on the calibration plate, records the coordinates of the marks in the camera coordinate system, and records the coordinates of the end of the calibration device in the robot coordinate system. A calibration transformation matrix is calculated according to the recorded coordinates of at least three marks.

Abstract: Provided are an electric motor, a laminated iron core, and a manufacturing method. In an embodiment, the method includes S1: introducing inert gas into an additive manufacturing printing apparatus, pouring silicon steel metal particles into a forming cylinder of the apparatus, and performing laser scanning on the silicon steel metal particles to gradually melt the silicon steel metal particles into at least one silicon steel metal layer; and S2: continuing to pour silicon steel metal particles into the forming cylinder, and stopping performing laser scanning on the silicon steel metal particles or reducing the laser power executing the laser scanning, such that the silicon steel metal particles do not entirely melt and form an insulating layer. Execution of steps S1 and S2 is alternated until a laminated iron core having a plurality of alternating silicon steel metal layers and insulating layers is formed.

Abstract: A robot hand-eye calibration method includes: controlling a tail end of a robot arm to sequentially move to at least three respective positions above a calibration plate; controlling a laser provided on the robot arm, at each position, to project on the calibration plate; recording coordinates, in a robot coordinate system, of an end point of the tail end of the robot arm during projection; controlling a camera on the tail end of the robot arm to photograph the projection on the calibration plate; recording the coordinates of the projection in the camera coordinate system; and calculating a calibration transformation matrix, according to the coordinates recorded, of at least three projections on the calibration plate in the camera coordinate system and respective coordinates of the end point of the tail end of the robot arm in the robot coordinate system during each respective projection.

Abstract: The present disclosure relates to a power management method and apparatus, a computing device, a medium, and a product. The power management method includes a monitoring step, a prediction step, an error calculation step and an adjustment step including adjusting power supply plan or a power demand of a user when at least one of a first error is greater than a first predetermined threshold or a second error is greater than a second predetermined threshold.

Abstract: A point cloud model reconstruction method, apparatus, and system are disclosed. In an embodiment, the method includes randomly selecting four non-coplanar reconstruction points in a point cloud model; iteratively selecting other reconstruction points successively until a reconstruction condition is met, and reconstructing the point cloud model based on all reconstruction points. A reduction degree of a reconstructed point cloud model is: Reward=?k·(PointNum?4)+g·VolRate, where PointNum represents a number of current selected reconstruction points, VolRate represents a ratio of a volume of a solid shape, k represents a proportion of the number of the selected points, and g represents a proportion of the volume ratio. The reconstruction method further includes adjusting a ratio of g to k based on user requirements to adjust the reconstruction condition.

Abstract: A method, device, storage medium, processor and terminal are for identifying security threats. In an embodiment, the method includes collecting a plurality of security-related security events, each security event containing a plurality of fields; for a first security event of the plurality of security events, searching one or more second security events related to the first security event from the plurality of security events according to one or more fields of the plurality of fields of the first security event, one or more second security events and the first security event forming event graphs; calculating the weights of the event graphs; and sorting the event graphs according to the weights.

Abstract: The invention relates to the technical field of industrial robots, and particularly relates to a coordinate system calibration method, a device, and a computer readable medium.

Abstract: The present invention provides a point cloud model reconstruction method, apparatus, and system. The method includes the following steps: randomly selecting four non-coplanar reconstruction points in the point cloud model; keeping iteratively selecting other reconstruction points successively until a reconstruction condition is met, and reconstructing the point cloud model based on all reconstruction points, where a reduction degree of the reconstructed point cloud model is: Reward=?k·(PointNum?4)+g·VolRate, where PointNum represents a number of current selected reconstruction points, VolRate represents a volume ratio of a volume of a solid shape formed by all of the current reconstruction points to an original volume of the point cloud model, k represents a proportion of the number of the selected points in the reduction degree, and g represents a proportion of the volume ratio in the reduction degree.

Abstract: A digital twin modeling and simulation method includes generating a manufacturing model ontology, acquiring field data, and generating a semantic model instance based upon the manufacturing model ontology and the field data. In an embodiment, the method further includes searching for the attribute of the field data according to the type of the field data, extracting data from the semantic model instance according to the search result, and simulating the semantic model instance on a simulation platform. A digital twin modeling and simulation mechanism provided by an embodiment has the flexibility of wide application and reduces the dependence on experts in this field.

Abstract: An address identification method, apparatus, system, storage medium, a processor and a terminal are disclosed. In an embodiment, the method includes: defining a screening library including at least one expected attribute value describing an expected state value of a device parameter to be addressed in an operating mode of an industrial device; acquiring a data group including an actual state value generated in the operating mode and an address where the actual state value is stored; for each address, extracting an actual attribute value, stored in the address, of the actual state value; comparing the actual attribute value with the expected attribute value, determining the actual state value corresponding to the actual attribute value which complies with the expected attribute value, and determining, from the data group, an address corresponding to the selected actual state value; and taking the selected address as a final address and outputting same.

Abstract: In robot teaching programming, a robot teaching programming method, apparatus and system, and a computer-readable medium, can realize the programming of a robot simply, and are not restricted in terms of robot types. A robot teaching programming system includes a movable apparatus for imitating movement of an end effector of a robot in a working space of the robot; a robot teaching programming apparatus for recording first movement information of the movable apparatus in a first coordinate system and converting the same to second movement information in a second coordinate system of the robot, and then programming the robot according to the second movement information. Using a movable apparatus to simulate an end effector of a robot has the advantages of ease of operation, and no restrictions in terms of robot types. Teaching programming is accomplished through simple coordinate transformation, and there is no need for advanced programming skills.