12results about How to "Reduce collection frequency" patented technology

The invention provides a high-altitude energy storage monitoring system. The system comprises an environmental data acquisition module, an intelligent temperature control analysis module, an intelligent energy scheduling module, a health monitoring module, a self-adaptive adjustment module and a system optimization module. The invention provides an intelligent and environment-adaptive energy storage system monitoring and maintenance strategy, the temperature control strategy, the energy distribution and the maintenance plan can be dynamically adjusted according to the real-time change of the high-altitude environment, the efficient operation of the energy storage system is realized, and the service life of equipment is prolonged. A sensor technology is used for collecting multi-dimensional environment data, and an intelligent algorithm is combined to monitor the operation state of the equipment in real time, predict the health state of the equipment, optimize energy scheduling and formulate a self-adaptive maintenance strategy, so that the fault risk and the maintenance cost are reduced, and the stable operation of the system in a complex environment is ensured.

The invention discloses an unattended transformer substation inspection method based on image recognition, and relates to the field of intelligent inspection. Visible light and infrared thermal imaging cameras are installed according to the layout of a transformer substation to respectively capture equipment visual information and monitor temperature distribution; and setting an acquisition frequency adjustment strategy according to equipment electrical parameters, a temperature change rate and the like to dynamically acquire data. A generative adversarial network (GAN) is utilized to assist in fault diagnosis, a deep learning model is also established, and a model for classification judgment is obtained through training after equipment state image collection, preprocessing and data set division. And meanwhile, a cloud edge cooperative computing architecture is constructed to efficiently process image data, a real-time interaction feedback mechanism is designed to remotely display related conditions of equipment in real time, the running state of the equipment can be monitored more comprehensively and accurately, and the inspection efficiency, the operation and maintenance management efficiency and the like are improved.

The invention provides a temperature rise test method of a low-voltage switchgear assembly and related equipment, and relates to the technical field of throttling devices. By receiving equipment scale data and outputting related data, a basis is provided for subsequent processing. And then, a series of sub-steps are executed circularly, so that the intelligentization of temperature monitoring is realized. When the temperature exceeds the threshold value, the system can improve the data acquisition frequency, and it is ensured that the temperature change at the critical moment is monitored more densely. Meanwhile, by analyzing the temperature rise trend and identifying the potential fault area, the system can find abnormal conditions in time. In addition, by comparing the current temperature rise trend with the historical temperature rise trend, the system can judge whether cooling measures need to be executed or not. And finally, through temperature stability analysis, the system can adjust a data acquisition strategy according to actual conditions. The dynamic adjustment method can effectively cope with the temperature change characteristics of different scales of equipment, and the detection efficiency and accuracy of temperature abnormity are improved.

The invention provides an Internet of Things data acquisition and abnormity early warning method and system, and relates to the technical field of Internet of Things data processing, and the method comprises the steps: constructing a digital twin model for a physical entity at a cloud end, and issuing prediction data to an edge node; acquiring sensor data by an edge computing node at a basic frequency, and computing a residual error between real-time data and predicted data and an information entropy of a residual error sequence; dynamically adjusting the data acquisition frequency of the edge node based on a two-dimensional decision space formed by the residual error and the information entropy; and when the residual error and the information entropy both exceed the threshold values, potential abnormity is judged, an edge side abnormity analysis program is triggered, and abnormal event information is uploaded. According to the invention, through cloud edge cooperation and intelligent decision making, the acquisition frequency is reduced in a stationary period, resources are saved, the frequency is improved when an abnormal symptom appears, monitoring is enhanced, and optimal balance between resource consumption and monitoring precision is realized; double criteria are adopted to effectively filter noise interference, and the false alarm rate and the missing report rate are reduced.

This invention relates to the field of pipe wall monitoring technology, and discloses an intelligent pipe wall thickness monitoring system, method, device, medium, and product. The system includes: a data acquisition unit configured to acquire electrical level data to obtain a first dataset; a second dataset acquired by acquiring electrical level data from each Hall sensor; a judgment unit configured to compare the electrical level data corresponding to each sensor array in the second dataset with the electrical level data corresponding to each sensor array in the first dataset, and determine whether corrosion exists in the monitored pipe based on the comparison result; a processing unit configured to adjust the acquisition time interval according to an adjustment coefficient; and an adjustment unit configured to determine whether to adjust the adjustment coefficient of the acquisition time interval. This invention solves the problem of large errors caused by reliance on manual labor, improves the efficiency and accuracy of pipe monitoring, and enhances its adaptability and intelligence.

The invention provides a competition risk-based sow reproduction dynamic early warning regulation and control method and system, and the method comprises the steps: collecting the basic data of a sow at a current observation moment t, extracting a feature vector, calculating the risk ratio of an individual sow to a standard sow based on the feature vector, calculating the accumulated morbidity of each reproduction abnormality event from breeding to the current observation moment t, and carrying out the early warning regulation and control. Calculating the predicted cumulative morbidity of the sow from the current observation moment t to a period of time in the future, determining a risk level according to the predicted cumulative morbidity, generating a control instruction, sending the control instruction to a hardware execution layer through a communication bus, driving the hardware execution layer to execute intervention, and dynamically adjusting the acquisition frequency of a sensor according to the risk level; and body weight and backfat feedback data are obtained after intervention, and if a preset threshold value is not reached, intervention parameters are automatically upgraded, so that closed-loop regulation and control are realized. According to the method, the competing risk model is constructed by taking the abnormal reproduction events as the mutual exclusion events, so that the risk probability is more accurate, and the sow breeding process can be accurately intervened.

The application discloses a kind of mechanical automatic transmission rotational speed synchronization self-adjusting predictive control method, comprising: the initial length of calculating brake combination signal sending to brake torque reaches constant value time point;Brake shaft speed drop value during brake torque constant time point is calculated to brake torque start to produce brake torque;Brake torque is zero during brake torque from constant brake torque start to reduce, and brake shaft speed drop value;According to the initial value of dynamic speed difference threshold value calculated according to the above results;Test the extreme value of brake friction plate separation torque reduction stage duration;According to the initial value of the extreme value of brake shaft speed curve parameter in separation torque reduction stage;According to the size relationship between the difference between the target speed of brake shaft before braking and current speed and dynamic speed difference threshold value, the rotational speed synchronization of transmission is controlled.The application improves control accuracy, shortens gear shifting time, improves gear shifting smoothness, enhances the robustness and stability of control method under time-varying working condition.

The present invention discloses an air transport urine volume monitoring system based on a biosensor, and belongs to the field of medical care technology. The present invention solves the problem that the existing urine volume monitoring method is not suitable for patients in air transport. By establishing different etiology types and their corresponding urine volume critical values, the patient's urine volume data can be compared with the critical value under the same etiology, thereby ensuring the accuracy of the patient's urine volume data comparison; and setting the corresponding alarm threshold, when the patient's urine volume is lower than or exceeds the corresponding alarm threshold, the buzzer in the micro display terminal will sound an alarm, thereby reminding medical staff to check in time, thereby improving the effectiveness of the patient's urine volume monitoring; by pre-setting the collection time node according to the air transport time and the patient's previous urination time, the biosensor and the flow sensor are controlled regularly to collect the patient's urine volume data, while ensuring that the urine volume monitoring is in a normal state, the equipment collection frequency is reduced and the use cost is optimized.

The invention relates to the technical field of pipe wall monitoring, and discloses an intelligent pipeline wall thickness monitoring system, method and device, a medium and a product, and the system comprises a collection unit which is configured to collect level data to obtain a first data set; collecting the level data of each Hall sensor again to obtain a second data set; the judgment unit is configured to compare the level data corresponding to each sensor array in the second data set with the level data corresponding to each sensor array in the first data set, and judge whether the pipeline to be monitored is corroded or not according to a comparison result; the processing unit is configured to adjust the acquisition time interval according to the adjustment coefficient; the adjustment unit is configured to determine whether to adjust the adjustment coefficient of the acquisition time interval. The problem that errors are large due to the fact that the manual degree is large is solved, the pipeline monitoring efficiency and accuracy are improved, and the adaptability and the intelligent level of the pipeline monitoring system are improved.

The invention provides a multi-modal fusion method for automatic driving of 3D obstacles. The multi-modal fusion method comprises the following steps: S1, acquiring driving data of a driving vehicle and information of obstacles around the vehicle by using a multi-modal sensor; s2, preprocessing the driving data of the autonomous vehicle and the data of obstacles around the vehicle, which are acquired by the multi-mode sensor in the step S1; s3, extracting features of the obstacles around the vehicle according to the preprocessed data of the obstacles around the vehicle; s4, a vehicle speed threshold value is set, when the vehicle speed is higher than the set threshold value, the collection frequency of the multi-mode sensor in the step S1 is increased, and when the vehicle speed is lower than the set threshold value, the collection frequency of the multi-mode sensor in the step S1 is reduced; and S5, according to the relationship between the vehicle speed and the speed threshold in the step S4, selecting a data layer fusion strategy to fuse the data or selecting a feature layer fusion strategy to fuse the data.

The invention relates to the technical field of data processing, in particular to a food production workshop safety risk early warning method and system.The method comprises the steps that a plurality of sensors are used for collecting production data in a biscuit production workshop and transmitting the production data to a display screen, and a risk assessment model is trained according to the production data; analyzing the safety risk of the biscuit production workshop by using the trained risk assessment model to output an analysis result, setting a risk early warning trigger threshold according to a historical analysis result, and giving an alarm when the production data reaches the risk early warning trigger threshold; determining the early warning stability of the safety risk based on the variance of the transmission delay duration of the production data; if the early warning stability does not meet the requirement, determining the size of a data packet of production data; and if the accuracy of the production data does not meet the requirement, determining the acquisition frequency of the production data. According to the invention, the early warning stability of the safety risk is improved.

The invention discloses a green certificate issuing full-coverage application support-based data information real-time sharing method, relates to the technical field of data sharing, and solves the problem that a large difference exists between a real-time sharing progress and an acquisition progress due to the fact that shared data among a plurality of different acquisition devices is not comprehensively analyzed. According to the method, the sharing rate of the single group of abnormal sharing equipment is fully optimized based on the identified abnormal equipment, and the acquisition frequency is properly reduced preferentially based on the condition of repeated data acquired by the corresponding acquisition equipment, so that the acquisition quantity of the acquisition equipment is ensured to be properly reduced; for multiple groups of abnormal equipment, associated conflict equipment is locked by adopting a pairwise test analysis mode, then sharing processing is performed on the associated conflict equipment by adopting a periodic sharing mode, and for unconfirmed abnormal shared equipment, optimization is performed by adopting an optimization mode of a sharing rate of a single group of abnormal shared equipment, so that a better real-time sharing effect is achieved. The overall treatment effect is improved.