293 results about "Non critical" patented technology

A cache memory supports a plurality of insertion / promotion vectors (IPVs) and a replacement control circuit configured to update replacement data for the cache memory based on the plurality of IPVs. In one embodiment, the IPV is selected for a given request based a criticality status of a cache line accessed by the given request. For example, cache lines deemed important or critical for performance reasons may be replaced less frequently via a first IPV, while non-critical cache lines are replaced more frequently via a second IPV.

The invention relates to the technical field of numerical control, in particular to a control method and system of numerical control equipment and a medium. The method comprises the following steps: collecting workpiece surface temperature distribution data and obtaining a workpiece CAD model; importing the workpiece surface temperature distribution data into a workpiece CAD model for internal temperature conduction inversion to obtain a first workpiece internal temperature distribution diagram; identifying a non-key area of the workpiece; performing a pre-cutting test on the non-key area of the workpiece to collect the temperature in the cutting process; performing nonlinear thermal expansion modeling on the workpiece based on the cutting process temperature to obtain a workpiece thermal expansion characteristic model; and an internal temperature distribution diagram of the second workpiece is obtained, and the internal temperature distribution diagram of the second workpiece represents the initial thermal state of the workpiece before formal machining. The numerical control machining precision and the cooling system efficiency are improved, the machining cost and resource consumption are reduced, and the machining adaptability, flexibility and stability are enhanced.

The invention relates to the technical field of laser calibration, in particular to a laser optical axis calibration device and method, and the device comprises an optical axis offset detection module, a calibration parameter calculation module, a dynamic adjustment implementation module and a calibration effect evaluation module. According to the invention, the response speed and precision are enhanced by monitoring the output of the laser emitter in real time, evaluating the instant deviation of the optical axis and dynamically calibrating the optical axis, the deviation trend of the optical axis is predicted by using a data analysis technology, the future deviation path is effectively controlled, and non-critical calibration is reduced, so that the long-term performance and stability of equipment are improved; dynamic adjustment measures are adopted, the positions of an angle adjuster and an optical element are finely adjusted, so that high calibration precision is ensured, energy loss and equipment abrasion are reduced, calibration effect evaluation of a vibration sensor provides immediate feedback for operation, transparency of the calibration process is improved, an operator can make a more accurate decision based on detailed data, and the operation efficiency is improved. Therefore, the reliability and the operation efficiency of the calibration task are enhanced.

Methods, systems, and devices for wireless communications are described. A network entity (e.g., a base station or reader) may transmit an initial access trigger message (e.g., modulating a continuous wave transmission using amplitude shift keying (ASK)), and the trigger message may include an indication of a type of passive user equipment (PUE) or a PUE use case (e.g., high priority / latency-critical PUEs or low priority / latency non-critical PUEs). A receiving PUE may harvest energy from the initial access trigger message for transmitting an initial access response message including an identifier for the receiving PUE. The receiving PUE may transmit the initial access response message (e.g., on one or more initial access resources) using the harvested energy if the trigger message indicates the PUE's type of PUE or PUE use case. In some examples, the receiving PUE may ignore triggers for different types of PUEs or PUE use cases.

The invention discloses a high-real-time security measurement method for a secure and trusted controller. The method comprises the following steps: S1, determining key firmware in the controller; s2, carrying out integrity verification on the key firmware through the security chip; s3, verifying the integrity of the firmware in the external memory by using the security chip; s4, if the integrity verification of the key firmware is passed, generating an integrity report; if not, corresponding safety measures are taken; s5, for non-key firmware, a soft measurement strategy is adopted for verification; s6, when the firmware is updated, the security chip is used for recalculating the verification information; and S7, if the key firmware is updated, the system is restarted and the step S3 is executed again, and if the non-key firmware is updated, the non-key firmware is restarted. The safety of the system is improved, the starting performance is optimized, and the maintenance and updating process is simplified.

The invention discloses a slope catastrophe early warning method and system based on multivariate and multi-domain perceptual analysis, and relates to the technical field of data processing, and the method comprises the steps: generating an influence coefficient of a traffic flow on a tunnel slope system according to the traffic flow data in a tunnel and the related multivariate data of a tunnel slope; the influence degree of the traffic flow condition in the tunnel on the stability of the tunnel slope system is described. Related data are analyzed and reflected to determine key position information and non-key position information in the three-dimensional structure position model of the tunnel slope system, and a basis is provided for establishment of a subsequent numerical model. According to the key position information and the non-key position information of the three-dimensional structure position model of the tunnel slope system and the influence coefficient of traffic flow on the tunnel slope system, a numerical model of the tunnel slope system is constructed, and the early warning value of catastrophe of the tunnel slope system is analyzed, so that the accurate numerical model of the tunnel slope system is established. The timeliness and adaptability of slope catastrophe early warning are improved, and the safety of a tunnel slope system is guaranteed.

The invention discloses a manufacturing industry processing equipment information digitization system comprising a data acquisition layer which acquires data through a sensor which is connected to a data acquisition terminal in a wired or wireless mode; the data acquisition terminal is used for receiving the data acquired by the data acquisition layer, respectively marking the acquired data as key monitoring parameters and non-key monitoring parameters, and then transmitting the key monitoring parameters and the non-key monitoring parameters to the server; the invention relates to the technical field of manufacturing industry data processing, and can realize equipment fault point judgment when equipment fails, can more comprehensively capture subtle changes of equipment operation states in fault prediction, greatly improves the fault feature identification degree, and improves the production continuity by predicting the fault occurrence time and type. And the risks of capacity loss and sharp rise of maintenance cost caused by faults are reduced. Impact of faults on equipment is reduced, and the service life of the equipment is prolonged.

The invention discloses an electrical coupling system operation reliability evaluation method, which comprises the steps of analyzing a network topology, an energy flow direction and an element logic relationship of an electrical coupling system, and constructing a Bayesian network model of the system from a power supply source to a load point; establishing an element reliability model, and obtaining different failure rate information of the non-key element by changing different age limits and load rates; taking different failure rate information of the non-key elements as input data of nodes of the Bayesian network model, outputting an analytical model coefficient, and obtaining a reliability index in combination with a key element combination state probability; training an LLSVM model by taking different failure rate information of the non-critical elements as input and taking an analytical model coefficient as output; and during online application prediction, obtaining different failure rates of corresponding non-key elements by combining element long-term and short-term prediction results with the element reliability model, inputting the different failure rates into the trained LLSVM model, and outputting a reliability index. According to the method, effective, rapid and efficient reliability evaluation of the electrical coupling system is realized.

The present invention relates to the field of numerical control machine tool control technology, and particularly relates to a high-precision multi-axis machining composite numerical control machine tool control system, including: a collection module; a prediction module respectively determines a radius time series curve and a length time series curve according to the tool radius and the tool length, and constructs a tool wear prediction model in combination with the vibration amplitude and the preset movement path of the tool; an analysis module determines the tool wear degree according to the tool wear prediction model, and determines the critical influence path segment and the non-critical influence path segment according to the vibration amplitude; an optimization module determines the correction direction and the path correction amount according to the critical influence path segment, the non-critical influence path segment and the tool wear degree to determine the new tool movement path, and determines the adjustment method for the cutting speed / feed speed according to the tool wear degree. The present invention incorporates tool wear into the dynamic adjustment mechanism of the path, improving the machining accuracy.

The invention provides a processor resource management method and a related device. The electronic device may identify critical threads, critical dependent threads, and non-critical threads. When the key thread and the key dependent thread are operated, the electronic device can dynamically adjust the frequency of the processor according to the task processing conditions of the key thread and the key dependent thread, and accurate supply of processor resources to the key thread and the key dependent thread is achieved. An electronic device may constrained provisioning of processor resources provided to non-critical threads. The method can improve the utilization efficiency of processor resources. According to the electronic equipment, the electric quantity of the electronic equipment can be saved while the experience of smoothly using the electronic equipment is provided for a user, the situation that the temperature of the electronic equipment is too fast is reduced, and balance between equipment performance smoothness and the requirements of long endurance and rapid cooling is achieved.

The invention provides a factory monitoring method and system based on digital twinning, belongs to the field of intelligent control, and solves the problems that video monitoring in a factory has a monitoring dead angle and also has a large amount of non-key information, and the monitoring efficiency is poor. If undifferentiated analysis is carried out on video data in real time, a large number of computing resources are consumed on meaningless data, and in addition, the problem that personnel video information and a digital twinborn model are difficult to fuse in video monitoring is solved. The method comprises the following steps: acquiring a manual control signal of operation equipment in a factory; based on the manual control signal, determining manual operation information currently implemented by an operator for the operation equipment; and deducing the current operation action of the operator in the digital twin model through the manual operation information so as to simulate the three-dimensional simulation dynamic effect of the operator corresponding to the operation action.

The present disclosure relates to integration of integrated photonics-based optical gyroscopes and fiber-based optical gyroscopes into portable apparatuses that may include compass features. Novel small-footprint modularized fully integrated photonics optical gyroscopes are used for non-critical axes. However, for at least one critical axis, a fiber-optic gyroscope can be used to provide bias stability below 0.1° / Hr, which is directly correlated to predicting positional accuracy in the centimeter range. The positional accuracy results from the compassing ability of the gyroscope (referred to as gyrocompass) to calculate direction of heading using the earth's rotation.

The invention provides an FPGA lookup table merging and decomposing method and device, equipment and a medium, and relates to the technical field of integrated circuit netlist optimization. The method comprises the following steps: acquiring a mapped LUT network of an FPGA; then, candidate pair identification is carried out on the LUT network, conflict-free LUT candidate pairs with the optimal weight are selected to be merged, an LUT connection relation graph is constructed after merging, and a key path is identified; on the basis of the LUT connection relation graph, decomposing the LUT on a non-critical path into smaller cascaded LUT units according to a LUT priority sequence; and dynamically updating the network topology structure and time sequence information of the LUT connection relation graph after each decomposition to complete optimization of the FPGA netlist. According to the method, the number of LUTs can be remarkably reduced and the resource utilization rate of the FPGA can be improved on the premise of ensuring that the delay of the critical path is not changed.

The invention provides a forwarding proof obtaining method and device and a forwarding proof verification method and device, and belongs to the field of network security. In the application, the forwarding proof is obtained by combining the expected sequence position of the forwarding node in the forwarding path and the identity of the forwarding node, so that the position binding of the forwarding proof and the fault tolerance for the non-key node are realized. The forwarding proof is not only related to the identity of the forwarding node, but also related to the expected sequential position of the forwarding node. The correct forwarding proof can be calculated by the node with the correct identity when the data message is forwarded to the correct sequence position on the forwarding path, so that the forwarding proof is strongly bound with the real forwarding condition of the data message. If an expected node is skipped or redundant unexpected nodes are passed in the forwarding process, the identity of the node is not matched with the sequence position of the node, so that the forwarding proof obtained based on the identity of the node and the sequence position of the node cannot pass the verification, and the credibility of the forwarding proof is improved.

The invention discloses a cloud networking monitoring video lightweight processing method and a cloud networking monitoring video lightweight processing system based on U-ACE. The core of the method is to construct and operate a multi-task and multi-mode AI core engine U-ACE. The processing flow comprises the following steps of: sensing an input video in real time and judging a processing priority through a sensing and priority decision module PPM of the U-ACE; for high-priority content, a deep analysis and prediction enhancement module APEM of the U-ACE is used for analysis, prediction and enhancement; self-adaptive content compression is carried out on a non-high-priority or non-key area through an intelligent compression module (ICM) of the U-ACE; in combination with user preferences, enhanced display and layered rendering are carried out by using the user preferences of the U-ACE and an intelligent presentation module UPSM; and running parameters and strategies for dynamically adjusting the engine are fed back in real time through an engine self-optimization module ESOM of the U-ACE. The processing efficiency is improved, the resource consumption is reduced, and the key content presentation and the user experience are optimized.

The invention discloses an updating method and device for a deep reinforcement learning model, and belongs to the technical field of deep reinforcement learning models.The updating method and device for the deep reinforcement learning model comprise the following specific steps that firstly, environment interaction data are asynchronously collected through a plurality of parallel execution units; 2, sampling data from an experience pool based on a dynamic priority strategy, wherein the priority is jointly determined by TD-error and strategy similarity; 3, setting different updating frequencies for a key layer and a non-key layer of the network by adopting a hierarchical updating strategy; and step 4, gradually synchronizing parameters of the target network and the online network through a soft update technology. According to the method, the waiting time is reduced through an asynchronous updating mechanism, the resource utilization rate is improved, the efficiency is improved, the training speed is improved through dynamic priority sampling, convergence is accelerated, the calculation overhead is reduced through a block updating strategy, resources are saved, the error rate of a model passing an adversarial test is reduced, and the safety is improved.

The invention provides a convolutional neural network federated learning backdoor attack method based on gradient regulatory factors. The implementation steps are as follows: constructing a federated learning system; each client obtains a training sample set; each client performs iterative training on the local model; and the central server obtains a backdoor attack result. In the process that each client performs iterative training on a local model, a malicious client calculates a gradient regulation factor of each weight parameter through a normal loss value and a back door loss value of the local model, and updates the weight parameter based on the regulation factor; according to the method, the updating amplitude of the key weight parameters of the backdoor attack can be enhanced, meanwhile, updating of a large number of non-key weight parameters is weakened, the overall weight updating amplitude is limited, the backdoor attack can be effectively enhanced, the performance is similar to the gradient updating characteristics of a benign client side, and therefore the concealment and durability of the backdoor attack are improved.

The invention relates to the technical field of electric digital data processing, in particular to a BIM building model construction method for building engineering design, which comprises the following steps: determining a newly added building area of each construction stage relative to the previous construction stage based on the change condition of a BIM building model of a building project in different construction stages; key areas and non-key areas are screened out from all newly-added building areas; based on the construction complexity of the key area, the criticality of the key area is determined, and then the adaptive LOD hierarchy of the key area is determined; allocating a set LOD layer not higher than the adaptive LOD level for the non-key area; and constructing a BIM building model based on the set LOD hierarchy allocated to the non-key areas of all the construction stages and the adaptive LOD hierarchy of the key areas. According to the method, the self-adaptive LOD levels are determined for different key areas, so that the calculation amount is reduced while the construction quality of the BIM building model is ensured.

The invention belongs to the technical field of artificial intelligence, and provides a deep learning backdoor attack detection method and device based on a non-critical feature dynamic threshold. The method comprises the following steps: inputting a to-be-detected image to a second image classification model to obtain a non-key feature map and a classification result of the to-be-detected image; the second image classification model comprises a feature extraction network; the screening module is used for separating a key feature map and a non-key feature map from the feature map of the to-be-detected image by using the mask; the classifier is used for carrying out classification processing on the key feature map of the to-be-detected image to obtain a classification result; calculating a non-key feature score based on the non-key feature map of the to-be-detected image, and if the non-key feature score reaches a preset score threshold, setting the abnormal mark of the to-be-detected image as abnormal; and outputting the abnormal mark and the classification result of the to-be-detected image. According to the method, extremely fine adjustment of an attacker on some pixels of the image can be identified, and the method can adapt to hidden attacks such as feature space disturbance.

The invention discloses an intelligent processing method and system for clinical test data, and relates to the field of data processing, and the method comprises the steps: recognizing a high flow disturbance stage, and starting an uploading channel adjustment process; executing flow limiting operation in the high flow disturbance stage; obtaining a load pressure evaluation result; and completing data supplementation. According to the method, under the condition of data uploading peak or resource shortage in the clinical test process, the whole-process regulation and control of the disturbance data, such as current-limiting protection, back compensation and abnormal filing, is realized by effectively identifying the high-flow disturbance stage, dynamically adjusting the data uploading behavior and intelligently distinguishing the processing priorities of the key data and the non-key data. The method has the advantages of being high in adaptability, accurate in resource scheduling and safe and reliable in data, solves the problems of system congestion, uploading failure or important data lag in the data uploading stage, and improves the overall operation stability and the data integrity guarantee capability of a clinical test data processing system.

The invention relates to the technical field of electrical fault detection, in particular to an online fault judgment and automatic detection method for a marine cable, which comprises the following steps of: acquiring ground leakage current and load current in operation, extracting an active component of the leakage current and analyzing the nonlinear correlation between the active component and the load current to realize online early warning of insulation degradation; when the ship is in a working condition of allowing short-time power failure of partial non-critical loops, positioning a fault branch based on a normalization result of the ground capacitance variation before and after the power failure of each branch and the cable length; after power supply is recovered, the fault type is judged by combining the leakage current fundamental wave and phase voltage phase difference and the harmonic distortion rate; during power-off maintenance, high-frequency carrier waves are injected, the position of a fault point is determined according to current attenuation abnormity, correction is carried out by combining temperature and vibration factors, and the detection accuracy and the positioning precision are improved.

A technique monitors and detects a loss of utility power supplied by a main utility grid so that power modules of a virtual critical load panel (vCLP) may shed non-critical loads substantially before a battery inverter switches to backup power. grid detection module of the vCLP may be configured with profiles of industry standard power ranges utilized by a plurality of battery inverters to monitor and detect the loss of the utility power as an indication to switch to the backup power. The grid detection module employs the profiles in a manner that not only enables monitoring and detection of the loss of utility power but also enables shedding of the non-critical loads of the vCLP earlier than the switch to the backup power by the inverters.

A flight management system of an aircraft includes a first non-critical or open world module for creating an enhanced enriched flight plan, the first module being configured to create the enhanced enriched flight plan iteratively by the following steps: initialisation of pseudo-constraints; and iterative enhancement of the choice of pseudo-constraints, until an objective of enhancement or of absence of enhancement with respect to the last best choice of pseudo-constraints is reached, based on at least one optimisation criterion and on at least one parameter representative of the environment of the flight of the aircraft; the second critical or flight management avionics module certified to calculate a path, comprising at least one critical computer and / or at least one critical piece of software for calculating and delivering as output a secured path, based on the enhanced enriched flight plan supplied by the first module.

The invention discloses an elastic scheduling method and system for a power grid based on a dynamic security domain, and relates to the technical field of elastic scheduling methods, and the method comprises the steps: determining the boundary of a non-key constraint based on an elastic adjustment factor, a power grid operation event corresponding to the power grid, and a plurality of constraints; the corresponding optimization event is determined according to the dynamic security domain, the boundary of the non-critical constraint and the real-time working data of the power grid, and the elastic scheduling system of the power grid is determined based on the optimization event, the real-time load data of the power grid and the corresponding real-time power supply data, so that the accuracy of the elastic scheduling system of the power grid is improved; and the elastic scheduling effect of the power grid is further improved. Therefore, the plurality of sub elastic scheduling projects are determined according to the identification of the elastic scheduling mode, the power optimization event of the power grid is determined based on the project content of each sub elastic scheduling project, the corresponding project position and the real-time load data of the power grid, and the accuracy of the online optimization logic of the power grid is ensured.

The invention relates to a key area high-definition video transmission method and device under a limited network condition, and belongs to the technical field of video processing, and the method comprises the steps: obtaining a network transmission speed; sending the key picture information of the to-be-transmitted video to a receiving end, and receiving a key region mark returned by the receiving end; dividing the video to be transmitted into a key area and a non-key area according to the key area mark; respectively determining a first definition parameter corresponding to the key area and a second definition parameter corresponding to the non-key area according to the network transmission speed; executing a circulation step: obtaining a local high-definition video according to the first definition parameter and the second definition parameter; transmitting the local high-definition video to a receiving end, and judging whether the local high-definition video can be normally played or not based on the transmission monitoring information; and if the playing is abnormal, updating the first definition parameter and / or the second definition parameter according to the network transmission speed, and repeatedly executing the loop step until the playing is normal. The video transmission stability is improved.

The invention discloses a large model reasoning method and device, related equipment and a computer program product, and relates to the field of artificial intelligence, and the method comprises the steps: firstly obtaining a problem statement of a to-be-processed problem, then judging whether a current node is a key node or not according to an input token of a current reasoning node in a chain reasoning process of a large model based on the problem statement, and if yes, judging whether the current node is the key node or not; and if not, performing compression processing on the original output token of the previous node, and reasoning subsequent nodes based on a compression processing result. According to the method, whether the reasoning nodes are critical or not is judged, and then the output token of the non-critical reasoning nodes is compressed during subsequent reasoning, so that the input data volume needing to be processed in the subsequent reasoning process is reduced, the subsequent processing speed is increased, and optimization of the calculation efficiency of chain reasoning, particularly long chain reasoning is facilitated.

The invention discloses a freezing method for a load record of an intelligent flexible adjusting unit. According to the method, system configuration is analyzed and adjusted according to historical data, different scenes are rapidly adapted, and system adaptability and response efficiency are improved; the real-time current, voltage and electric energy quality are deeply analyzed, the load trend is predicted, and the adjustment accuracy and the intelligent decision-making capability are enhanced; recording intervals are automatically adjusted according to load changes, time periods and user characteristics, and fine control over data recording is achieved; non-critical data are intelligently identified and preferentially compressed, storage distribution is optimized, and the processing efficiency is improved; multi-format packaging is supported, and interoperability and data sharing convenience are improved; it is ensured that key data can be recorded outside an unconventional interval, response to abnormity and predictive maintenance is enhanced, and data integrity is ensured; through double verification, encryption and digital signature, the data security is ensured; efficient backup and disaster recovery are realized, data persistence and rapid recovery are ensured, and data security and service continuity are ensured.

The invention relates to the technical field of laser cutting, in particular to a waterproof material laser cutting path optimization method based on deep learning, which comprises the following steps: acquiring basic size data, thickness change rate distribution data and preloading stress distribution data of a target test piece, and identifying key / non-key path sections through discretized grid units; cutting is conducted on the non-critical path section through basic cutting parameters, edge size data are monitored in real time, and the parameters are dynamically adjusted to form verification cutting parameters; screening a first confidence path segment based on stable cutting quality, and extracting verification parameters to construct a confidence collection data set; key cutting parameters are determined through parameter fluctuation analysis and correlation calculation, and a confidence data set is formed; cutting the key path section by adopting the parameter; and finally, establishing a deep learning model based on the historical confidence data set, and outputting basic cutting parameters of the new test piece. According to the method, self-adaptive optimization of cutting parameters and paths is achieved, and the cutting precision of the waterproof material test piece is remarkably improved.

The invention relates to the technical field of safety control of mining equipment, in particular to a mining intrinsic safety type multi-mode cooperative control system. The system comprises a main controller, a sensor module, a voice alarm module and an equipment execution unit. According to the invention, by setting a hierarchical protection shielding function, an effect of accurately preventing and controlling major safety risks is achieved, based on a dual judgment mechanism of a comprehensive risk coefficient and an operation risk index, an early warning-alarm-emergency response three-level protection system is systematically constructed, and when risks of equipment temperature abnormity, speed overrun and the like are detected, the system can give an alarm. The method comprises the following steps: firstly, triggering voice alarm and reserving emergency disposal time, and automatically shielding a non-key protection function when the risk is upgraded to a system-level emergency state, thereby avoiding economic loss caused by whole-line production halt, breaking through the limitation of once-by-once stopping of a traditional safety system, and remarkably improving the intrinsic safety level of mine production.

The invention relates to a UPS device (10) configured to supply power simultaneously to a critical load (31) and a non-critical load (32), the UPS device (10) comprising: - a first converter (14) and a second converter (15) that are coupled in series, - an energy storage source (17) connected to that terminal of the first converter (14) that is coupled to the second converter (15), - an input (11) connected to a supply network (30), a first output (12) coupled at the output of the second converter (15) and to a critical load, and a second output (32) coupled to the input (11) and to a non-critical load, and - a bypass switch (16) connected between the input (11) and the first output (12). It comprises an electromechanical switch (18) connected between the input (11) and the first converter (14), and a control unit (20) configured to monitor the quality of the electric current of the electrical network (30) and control the operating mode of the device (10) so as to maintain the simultaneous supply of power to the critical load (31) and the non-critical load (32).