27results about How to "High acquisition frequency" patented technology

The invention relates to the technical field of electric power design, in particular to an intelligent dispatching system of a transformer area power supply grid. According to the method, a self-adaptive sampling strategy of real-time load fluctuation and distributed energy output characteristics is adopted; when the system detects the rapid change or abnormal fluctuation of the load, the data acquisition frequency is automatically improved, so that the capture of key information is ensured; when the load is stable, the sampling frequency is reduced, and the collection of redundant data is reduced; adaptive sampling can dynamically adjust the acquisition frequency, it is guaranteed that important data are not lost at the critical moment, and meanwhile the system burden is reduced; a hierarchical data processing architecture is adopted, data processing is divided into an edge layer and a cloud layer, the edge layer is responsible for conducting primary processing on data collected in a transformer area, including denoising, feature extraction and the like, and the data quality is guaranteed; only the key data subjected to primary processing is uploaded to a cloud end for data analysis and decision optimization; therefore, the burden of data transmission and processing is reduced, and the real-time response capability of the scheduling system is improved.

The invention discloses a Yangtze estuary terrain evolution analysis and dynamic simulation method based on multi-source data fusion, and relates to the technical field of terrain evolution analysis, and the method comprises the following steps: setting a preliminary expected monitoring period by a satellite remote sensing platform according to the monitoring demands of a mud flat region, so as to regularly obtain the image data of the mud flat region, the real-time monitoring of the mud flat area is realized; and after a new satellite image is acquired each time, registering the new satellite image with the satellite image acquired in the previous time. According to the method, the mud flat area is divided into a rapid evolution type and a slow evolution type, and the initial period is maintained for the stable change area, so that resources are saved; the period of a violent change area is shortened, the data acquisition frequency and the response speed are improved, the problem of traditional monitoring lag is effectively solved, rapid terrain change can be captured in time, the monitoring efficiency is optimized, the ecological damage risk is reduced, and accurate decision support is provided for mud flat protection and management.

The invention provides a data acquisition method and device based on a multi-order queue and electronic equipment, and the method comprises the steps: obtaining production data generated by industrial control equipment; according to the acquisition frequency, the production data is distributed to each level queue of the multi-level queue, the queue number of each level queue is adjusted according to the real-time data volume of each queue in each level queue, and each level queue is used for caching the production data of different acquisition frequencies; according to a data packaging strategy, the production data in the multi-order queue is packaged, and the data packaging strategy comprises a timing packaging strategy and a quantitative packaging strategy; and uploading the packaged production data to an industrial Internet of Things platform. Production data acquisition is performed through the multi-order queue, so that the processing pressure of a single queue can be effectively relieved, the data transmission frequency is reduced, and the resource consumption is reduced, thereby improving the efficiency and reliability of data acquisition.

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 a lightweight real-time data system, and relates to the technical field of data processing, a system management module, a data governance module, a data acquisition module, a data service module and a data asset module form a whole system structure, and a data quality evaluation model ensures the accuracy, integrity and consistency of data. The data acquisition module efficiently manages a data source; the data service module provides safe and reliable data access; the data asset module reasonably allocates resources, provides a decision basis and periodically updates and maintains; the lightweight design is adopted; universal data acquisition, calculation processing, data management and data service functions are integrated, repeated related code writing is reduced, flexible configuration and rapid deployment are realized, a business application construction mode is innovated, and lightweight processing enables the data system to adapt to global tourism management platforms and scenic spot management platforms of different scales and types.

The invention relates to the technical field of transformer area line loss supervision, in particular to a transformer area line loss intelligent supervision system and method, and the system comprises a high-frequency data collection module, a data preliminary screening module, a line loss recognition and positioning module, a control center, and a load balancing module. According to the invention, the intelligent electric meters and the sensors are installed at each power consumption point, each distribution transformer and each branch line, so that full-coverage power data acquisition is realized, and the data acquisition frequency is improved to a millisecond level; the system can obtain the operation data of the power system in real time; meanwhile, an edge computing device is integrated in the high-frequency acquisition module, data can be processed and analyzed in real time, real-time monitoring of the power system is achieved, and the line loss problem is found and processed in time; denoising and smoothing processing are carried out on data by adopting wavelet transform, main features are extracted by utilizing principal component analysis (PCA), a Gaussian mixture model is trained according to historical data, modeling is carried out on feature vectors, probability density is calculated, and the depth and precision of data processing are improved.

This invention relates to the field of logistics distribution network monitoring technology, and discloses a method for order anomaly response based on logistics status. The method includes: acquiring parcel and vehicle data from network points and stitching them together to obtain first node load characteristics; obtaining second node load characteristics through random forest classification; adjusting the RFID tag acquisition frequency to obtain a first acquisition frequency and acquiring a first set of abnormal orders; spatially clustering the first set of abnormal orders to obtain initial clusters; calculating the stacking density based on their boundary contours and area; analyzing the average physical distance and scanning blind zone distribution characteristics of the initial clusters with high stacking density to obtain the RFID wake-up period; comparing with order tracking data to obtain tracking delay results; reducing the dimensionality of the temperature and humidity sensor distribution density and acquired data to obtain a second resource allocation matrix; matching a preset frequency mapping interval to obtain a second acquisition frequency and acquiring a second set of abnormal orders. This method can solve the problem of delayed anomaly response caused by mismatch between fixed acquisition frequency and node load.

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 invention discloses a data acquisition method and system for a sensor network, and relates to the technical field of sensor networks, and the method comprises the steps: constructing a standard change formula and extracting a standard data set for calculation, and can respectively calculate the standard change rate V of each processing equipment state parameter. And on the basis, when the calculated standard change rate Vi (t) exceeds a set change rate threshold value Vth, the system judges that equipment operation is abnormal, and an event triggering stage is entered. At the stage, the operation frequency of the sensor module can be increased by 50%, so that more abnormal state parameters such as power fluctuation P, vibration frequency F and noise spectrum S are collected. And abnormal characteristic values Yc are calculated for the abnormal state parameters and are summarized into an abnormal characteristic set O, so that the identification capability of the system on equipment abnormity is further enhanced, potential faults are found in time, a basis is provided for a subsequent fault response stage, and the risk of fault occurrence is reduced.

The invention relates to the technical field of energy system analysis, in particular to an energy system carbon footprint analysis method based on input-output balance, and the method comprises the steps: obtaining a keyword of an energy product; collecting target parameters of the energy product to obtain consumption data of the life cycle of the energy product, and performing screening processing; transmitting the acquired consumption data to an input-output model to construct a direct consumption matrix; solving the carbon footprint of each energy product by using an input-output balance analysis model; the method comprises the steps of determining whether the life cycle of a single energy product meets the standard or not, drawing a time-energy consumption curve, and secondarily determining whether the life cycle of the single energy product meets the standard or not based on the slope of the curve. And a time-energy consumption curve is drawn, the energy consumption condition is analyzed from the time dimension, and the analysis accuracy is further improved.

The present invention provides a lightweight real-time data system, which relates to the technical field of data processing. A system management module, a data governance module, a data acquisition module, a data service module and a data asset module constitute the entire system structure. A data quality assessment model ensures the accuracy, integrity and consistency of data. An anomaly detection model can timely discover abnormal values. The data acquisition module efficiently manages data sources. The data service module provides safe and reliable data access. The data asset module reasonably allocates resources, provides a basis for decision-making and regularly updates and maintains. A lightweight design is adopted, and general data acquisition, calculation and processing, data management and data service functions are integrated to reduce repetitive related code writing, realize flexible configuration and rapid deployment, and innovate business application construction modes. Lightweight processing enables the data system to adapt to global tourism management platforms and scenic area management platforms of different scales and types.

The invention relates to the technical field of quality prediction, in particular to a fermentation quality prediction management system for enzyme production based on data processing. The system comprises the following modules: an environmental parameter acquisition module, a microbial activity monitoring module, a fermentation state identification module and a prediction regulation and control module, wherein the environmental parameter acquisition module acquires environmental condition parameters in a fermentation tank in real time through a multi-parameter sensor array; an enzyme activity index and nutrition consumption rate data are obtained from the microbial activity monitoring module; the microbial activity monitoring module is used for judging a microbial behavior track, correcting track offset and identifying characteristics of each fermentation stage; and the prediction regulation and control module predicts the product quality, and optimizes fermentation process parameters according to the deviation between the prediction result and the target quality. According to the method, prospective control over the enzyme production quality is achieved, the intelligent level of the whole fermentation process is improved, and a flexible and efficient fermentation quality control mechanism is formed.

This application provides a control method, device, smart glasses, medium, and program product for smart glasses. The control method includes: periodically acquiring eye-tracking images of the wearer according to a default cycle using an eye-tracking device during the wearing of the smart glasses; determining the wearer's eye usage data based on the eye-tracking images; adjusting the acquisition cycle of the eye-tracking images based on the eye usage data; and providing eye health reminders. By combining real-time eye usage information with eye health reminders, it is beneficial to improve the eye habits of smart glasses wearers and enhance eye protection. Simultaneously, by dynamically adjusting the acquisition cycle of the eye-tracking images based on eye usage information, eye-tracking image acquisition can be performed at a lower frequency when eye usage is healthy, thereby reducing the power consumption of eye monitoring.

The invention discloses a control system of an industrial power quality control cabinet, and particularly relates to the field of industrial automation, which comprises a real-time data acquisition module, an industrial load characteristic identification module, a signal preprocessing module, a balance control module, a system fault diagnosis module, an emergency response module and a control decision output module. According to the control system of the industrial electric energy quality control cabinet, the acquisition frequency and precision are improved through the real-time data acquisition module, the parameter acquisition range is expanded, and more comprehensive and detailed power grid electric energy quality information is provided for the system; a plurality of characteristic identification technologies are adopted through an industrial load characteristic identification module, a dynamic tracking unit tracks load changes in real time, and diversified and complicated industrial load characteristics and influences of the changes on electric energy quality are accurately identified; and the emergency response module adjusts according to the reactive power compensation amount and the filtering control parameter calculation formula to improve the response speed of the sudden electric energy quality problem.

The application introduces a roadside traffic signal data acquisition and processing system and method, wherein the roadside traffic signal data acquisition and processing system comprises: a traffic signal controller for controlling the traffic light at the road intersection; and a signal protocol conversion device for acquiring the data of the traffic signal controller and sending the acquired data to a data center. The signal protocol conversion device comprises a voltage acquisition unit for acquiring the voltage data in the traffic signal controller and a pulse detection unit for acquiring the pulse data in the traffic signal controller, and the voltage acquisition unit and the pulse detection unit are connected with the traffic signal controller respectively. Based on the above system design, the roadside traffic signal data acquisition and processing method designed by the application has the advantages of wide voltage acquisition range, high acquisition frequency, short processing time, wide adaptability and high reliability.

The present invention relates to the field of smart agricultural technology, specifically to an intelligent crop growth environment monitoring and control method and system based on the Internet of Things. The present invention sets corresponding initial acquisition parameters for each acquisition environment, collects crop growth information according to the initial acquisition parameters of each acquisition environment, then screens out unqualified crop growth information, then inputs the unqualified crop growth information into an acquisition parameter model to train the acquisition parameter model, obtains a trained acquisition parameter model, and finally continuously optimizes the initial acquisition parameters in combination with the predicted crop information output by the trained acquisition parameter model to obtain optimized acquisition parameters, and collects crop growth information according to the optimized acquisition parameters. The present application continuously optimizes the acquisition parameters to increase the acquisition frequency of unqualified crop growth information, so as to collect more abnormal data and facilitate finding the root cause of unqualified crop growth information.

The present invention relates to the technical field of open channel flow measurement, and in particular to a non-full pipe integrated flow measurement device and method, the device comprising a flow measurement module, a liquid level measurement module, a device box and a controller, the flow measurement module is arranged on both sides of the device box, and is used to measure the flow velocity value signal of the water flow in the device box, the liquid level measurement module is arranged at the water outlet of the device box, and is used to measure the liquid level value signal of the water flow in the device box, the controller is respectively connected to the flow measurement module and the liquid level measurement module, and is used to calculate the water flow rate passing through the box according to the acquired flow velocity value signal and liquid level value signal. The purpose of the present invention is to provide a non-full pipe integrated flow measurement device and method, so as to solve the problems that the existing open channel flow measurement equipment can only measure the flow of the full pipe or the non-full pipe flow measurement is inaccurate, has a single function and a low integration.

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 relates to the technical field of wind power, in particular to a wind power access control system based on intelligent algorithm optimization. According to the system, a low voltage is generated through a power generation module, the low voltage is transmitted to a power transformation module through a first transmission module, the low voltage is boosted to a high voltage through the power transformation module, then a high voltage is transmitted to a power utilization module through a second transmission module, and a voltage value of the high voltage in the power utilization module is periodically acquired through an acquisition module. The analysis module can quickly determine whether the wind power access meets the standard based on the voltage value, can determine the reason when determining that the wind power access does not meet the standard, generates a corresponding correction mode based on the reason, generates a corresponding instruction according to the corresponding correction mode through the instruction generation module, and sends the instruction to the optimization module; the operation parameters in the corresponding modules are adjusted through the optimization module, so that the qualified rate of wind power access meeting the standard is improved, and the reliability aiming at the follow-up production and life electricity demands is improved.

The present invention discloses a data collection method for distributed computing equipment, which collects data at a first frequency; determines whether the data collected at the first frequency meets a preset first numerical condition; if so, sends the data collected at the first frequency to a server at a second frequency lower than the first frequency; if not, directly sends the data collected at the first frequency to the server and issues an alarm. The application of the embodiments of the present invention can achieve timely monitoring and reporting of cracks or deformations that appear instantly in pipelines, greatly reducing maintenance costs.

The present invention discloses a laser projection equipment control method, device, laser projection equipment and storage medium, and belongs to the field of projection equipment control technology. The first sensing device of the present invention detects whether there is a moving object approaching the laser projection equipment in a first detection area. When a moving object approaches the laser projection equipment, the acquisition frequency of the second sensing device is increased to the first target frequency, and then the second sensing device detects whether the moving object is in the second detection area based on the first target frequency. In the present invention, when the first sensing device detects that an object or a human body is approaching the laser projection equipment, the second sensing device is "awakened" by the first sensing device, and the acquisition frequency of the second sensing device is increased, so that the second sensing device can timely and accurately collect the sensor signal of the moving object when the moving object approaches the projection equipment, thereby triggering the corresponding eye protection instruction, and the eye protection function of the laser projection equipment responds quickly and is highly practical.

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 embodiment of the invention discloses a risk control method and device, a storage medium and electronic equipment, and the method comprises the steps: determining a to-be-evaluated target, an area where the to-be-evaluated target is located, and a long period and a short period of data collection, and collecting basic data used for representing the economic environment of the area according to the long period. And according to the short period, collecting fluctuation data used for representing economic fluctuation of the region, according to the basic data, determining a basic risk of the region in the long period, and according to the fluctuation data, determining a fluctuation risk of the region in the short period. And according to the basic risk of the region in the long period, determining the basic risk of the region in each short period falling into the long period, and according to the basic risk and the fluctuation risk of the region in each short period, determining the final risk of the region in each short period, thereby determining the risk of the to-be-evaluated target.

The utility model discloses a ray testing device, which comprises a support body, a detection channel for rays to pass through is formed in the support body, and the detection channel penetrates through the support body along a first direction; the detector assembly is arranged outside the supporting body, is positioned on one side of the supporting body along the first direction, and can receive at least part of rays emitted from the detection channel and form detection data capable of representing a ray dose value; and the data acquisition board is arranged outside the supporting body and is electrically connected with the detector assembly so as to collect detection data sent by the detector assembly. According to the ray testing device, the basic function of ray dose rate testing can be achieved, the price of the supporting body, the price of the detector assembly and the price of the data acquisition board are low, and the testing cost is reduced to a great extent. The acquisition frequency of the detector assembly is far higher than that of the existing dosimeter equipment, and the change and fluctuation conditions of the ray dose rate can be accurately reflected, so that the test accuracy is improved.

The invention discloses a real-time monitoring and early warning method and device for an electrochemical energy storage power station. The method comprises the following steps: acquiring multi-parameter time sequence data; carrying out fusion analysis and state prediction on the data; establishing and online updating a self-adaptive safety threshold value of each parameter; matching the real-time observation sequence with a pre-constructed multi-mode fault evolution knowledge graph to obtain a mode matching confidence coefficient; predicting the probability that the battery is in a dangerous state in the future, and taking the probability as state prediction confidence; calculating the standard exceeding amplitude and duration of the current parameter value relative to the safety threshold, and obtaining threshold standard exceeding confidence; carrying out weighted fusion on the mode matching confidence coefficient, the state prediction confidence coefficient and the threshold exceeding confidence coefficient, and calculating to obtain a comprehensive risk confidence coefficient; and according to the comprehensive risk confidence, triggering an early warning signal and an emergency response measure of a corresponding level. According to the invention, the safety of the energy storage power station is effectively improved, the stable operation of the energy storage power station is ensured, and the accuracy, robustness and adaptability of early warning are improved.