Research and application of real-time monitoring and early warning thresholds for multi-temporal agricultural products information

Monitoring and early warning is an important means to effectively prevent risks in agricultural production,consumption and price.In particular,with the change of modes of national administration against the background of big data,improving the capacity to monitor agricultural products is of great significance for macroeconomic decision-making.Agricultural product information early warning thresholds are the core of agricultural product monitoring and early warning.How to appropriately determine the early warning thresholds of multi-temporal agricultural product information is a key question to realize real-time and dynamic monitoring and early warning.Based on the theory of abnormal fluctuation of agricultural product information and the research of substantive impact on the society,this paper comprehensively discussed the methods to determine the thresholds of agricultural product information fluctuation in different time dimensions.Based on the data of the National Bureau of Statistics of China(NBSC)and survey data,this paper used a variety of statistical methods to determine the early warning thresholds of the production,consumption and prices of agricultural products.Combined with Delphi expert judgment correction method,it finally determined the early warning thresholds of agricultural product information in multiple time,and carried out early warning analysis on the fluctuation of agricultural product monitoring information in 2018.The results show that:(1)the daily,weekly and monthly monitoring and early warning thresholds of agricultural products play an important early warning role in monitoring abnormal fluctuations with agricultural products;(2)the multitemporal monitoring and early warning thresholds of agricultural product information identified by the research institute can provide effective early warning on current abnormal fluctuation of agricultural product information,provide a benchmarking standard for China's agricultural production,consumption and price monitoring and early warning at the national macro level,and further improve the application of China's agricultural product monitoring and early warning.

Real-time monitoring of weld penetration quality in robotic arc welding process

It is of great significance to develop an intelligent monitoring system for weld penetration defects such as incomplete penetration and burn-through in real-time during robotic arc welding process. In this paper, robotic gas metal arc welding experiments are carried out on the mild steel test pieces with Vee-type groove. Through-the-arc sensing method is used to capture the transient values of the welding voltage and current. The raw data of the captured welding current and voltage are processed statistically, and the feature vector SIO is extracted to correlate the welding conditions to the weld penetration information. It lays foundation for intelligent monitoring of weld quality in robotic arc welding.

The Improvement of Earthquake Real-Time Monitoring System of Chinese National Digital Seismic Network

The earthquake real-time monitoring system of the Chinese National Digital Seismic Network has been in operation since"the Ninth Five-year Plan"period,and the stability of the system has been well tested.In recent years,with the continuous improvement of monitoring technology and increase of public demands,the original real-time monitoring system needs to be upgraded and improved in terms of timeliness,stability,accuracy and ease of operation.Therefore,by accessing a total of more than 1,000 seismic stations,reducing the seismic trigger threshold of the monitoring system,eliminating the false trigger stations and optimizing the seismic waveform display interface,the current earthquake monitoring demands can be satisfied on the basis of ensuring the stable operation of the system.

A real-time AI-assisted seismic monitoring system based on new nodal stations with 4G telemetry and its application in the Yangbi M_(S) 6.4 aftershock monitoring in southwest China

A rapidly deployable dense seismic monitoring system which is capable of transmitting acquired data in real time and analyzing data automatically is crucial in seismic hazard mitigation after a major earthquake.However,it is rather difficult for current seismic nodal stations to transmit data in real time for an extended period of time,and it usually takes a great amount of time to process the acquired data manually.To monitor earthquakes in real time flexibly,we develop a mobile integrated seismic monitoring system consisting of newly developed nodal units with 4G telemetry and a real-time AI-assisted automatic data processing workflow.The integrated system is convenient for deployment and has been successfully applied in monitoring the aftershocks of the Yangbi M_(S) 6.4 earthquake occurred on May 21,2021 in Yangbi County,Dali,Yunnan in southwest China.The acquired seismic data are transmitted almost in real time through the 4G cellular network,and then processed automat-ically for event detection,positioning,magnitude calculation and source mechanism inversion.From tens of seconds to a couple of minutes at most,the final seismic attributes can be presented remotely to the end users through the integrated system.From May 27 to June 17,the real-time system has detected and located 7905 aftershocks in the Yangbi area before the internal batteries exhausted,far more than the catalog provided by China Earthquake Networks Center using the regional permanent stations.The initial application of this inte-grated real-time monitoring system is promising,and we anticipate the advent of a new era for Real-time Intelligent Array Seismology(RIAS),for better monitoring and understanding the subsurface dynamic pro-cesses caused by Earth's internal forces as well as anthropogenic activities.

Comparison of Two Real-time PCR Technigues for Quantification of GMO Contents in Highly Processed Products of Soybean

The RR soybean was quantitatively detected by ABI Prism 7300 sequence detector with PCR primers and fluorescence probes were designed according to the sequences of endogenous Lectin gene and exogenous CP4-EPSPS gene, and the PCR systems were based on SYBR Green I and TaqMan. The standard curve of ACt between CP4-EPSPS gene and Lectin gene of the RR soybean in standard materials was generated and a linear regression equation was obtained. Quantification methods were optimized through two different real-time PCR chemistries, i.e. SYBR Green I and TaqMan, and the RR soybean contents were quantified in five standard samples and seven highly processed products by the two assays. Both methods are proved to be specific, highly sensitive and reliable for both identification and quantification of soybean DNA. The results indicate that the two optimized PCR system can be used for the practical quantitative detection of RR soybean in highly processed products.

Label-Free and Real-Time Monitor of Binding and Dissociation Processes between Protein A and Swine IgG by Oblique-Incidence Reflectivity Difference Method

Life science has a need for detection methods that are label-free and real-time. In this paper, we have selected staphylococcal protein A （SPA） and swine immunoglobulin G （IgG）, and monitor the bindings between SPA and swine IgG with different concentrations, as well as the dissociations of SPA-swine IgG complex in different pH values of phosphate buffer by oblique-incidence reflectivity difference （OIRD） in a label-free and real-time fashion. We obtain the ON and OFF reaction dynamic curves corresponding to the bindings and dissociations of SPA and swine IgG. Through our analysis of the experimental results, we have been able to obtain the damping coefficients and the dissociation time of SPA and swine IgG for different pH values of the phosphate buffer. The results prove that the OIRD technique is a competing method for monitoring the dynamic processes of biomolecule interaction and achieving the quantitative information of reaction kinetics.

Patient Centered Real-Time Mobile Health Monitoring System

In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.

Monitoring and diagnosis of complex production process based on free energy of Gaussian–Bernoulli restricted Boltzmann machine

Online monitoring and diagnosis of production processes face great challenges due to the nonlinearity and multivariate of complex industrial processes.Traditional process monitoring methods employ kernel function or multilayer neural networks to solve the nonlinear mapping problem of data.However,the above methods increase the model complexity and are not interpretable,leading to difficulties in subsequent fault recognition/diagnosis/location.A process monitoring and diagnosis method based on the free energy of Gaussian-Bernoulli restricted Boltzmann machine(GBRBM-FE)was proposed.Firstly,a GBRBM network was established to make the probability distribution of the reconstructed data as close as possible to the probability distribution of the raw data.On this basis,the weights and biases in GBRBM network were used to construct F statistics,which represents the free energy of the sample.The smaller the energy of the sample is,the more normal the sample is.Therefore,F statistics can be used to monitor the production process.To diagnose fault variables,the F statistic for each sample was decomposed to obtain the Fv statistic for each variable.By analyzing the deviation degree between the corresponding variables of abnormal samples and normal samples,the cause of process abnormalities can be accurately located.The application of converter steelmaking process demonstrates that the proposed method outperforms the traditional methods,in terms of fault monitoring and diagnosis performance.

Quality monitoring of real-time GNSS precise positioning service system

The Real-Time Global Navigation Satellite System(GNSS)Precise Positioning Service(RTPPS)is recognized as the most promising system by providing precise satellite orbit and clock correc-tions for users to achieve centimeter-level positioning with a stand-alone receiver in real-time.Although the products are available with high accuracy almost all the time,they may occasionally suffer from unexpected significant biases,which consequently degrades the positioning perfor-mance.Therefore,quality monitoring at the system-level has become more and more crucial for providing a reliable GNSS service.In this paper,we propose a method for the monitoring of realtime satellite orbit and clock products using a monitoring station network based on the Quality Control(QC)theory.The satellites with possible biases are first detected based on the outliers identified by Precise Point Positioning(PPP)in the monitoring station network.Then,the corresponding orbit and clock parameters with temporal constraints are introduced and esti-mated through the sequential Least Square(LS)estimator and the corresponding Instantaneous User Range Errors(IUREs)can be determined.A quality indicator is calculated based on the IUREs in the monitoring network and compared with a pre-defined threshold.The quality monitoring method is experimentally evaluated by monitoring the real-time orbit and clock products generated by GeoForschungsZentrum(GFZ),Potsdam.The results confirm that the problematic satellites can be detected accurately and effectively with missed detection rate 4×10^(-6) and false alarm rate 1:2×10^(-5).Considering the quality alarms,the PPP results in terms of RMS of positioning differences with respect to the International GNSS Service(IGS)weekly solution in the north,east and up directions can be improved by 12%,10%and 27%,respectively.

面向流程制造的数字孪生车间可视化监控系统研究

为了解决流程制造过程监控实时性差、准确性不足的问题,在数字孪生技术迅速发展且与制造业结合愈发紧密的背景下,提出基于生产要素数据、生产工艺流程与多工序设备实体对象的数字孪生车间可视化监控系统架构。从要素、状态、运行逻辑等多个维度对多工序耦合生产线实体对象进行孪生建模,基于OPC UA统一架构的数据采集传输,虚拟车间的实时驱动等关键技术,实现了流程制造设备状态、生产要素工艺全流程三维可视化监控。基于某流程制造企业制丝生产试验线开展了应用验证,为提升流程制造过程中全生产要素的预见性、联动性、自治性提供了方法和实现途径。

气象应用业务融入气象大数据云平台的设计

针对现有气象应用不能直接融入气象大数据平台的现状,该文采用“云+端”融入的方法,对气象应用的数据存储、产品加工、业务监控、应用接口以及应用端进行改造设计,使现有气象应用业务能够快速接入气象大数据云平台。该设计在湖北气象业务进行应用,避免了因气象业务智能化建设而造成的重复建设和资源浪费,有利于推动气象业务智能化发展,为今后气象应用改造提供了参考。

Real-time monitoring system for quality monitoring of jujube slice during drying process

The real-time monitoring and prediction system for quality attributes of jujube slices during the drying process was designed to solve the problem of destructive and inconvenient of the traditional quality detection method and realize quality online monitoring.Firstly,machine vision and automatic weighing were employed to monitor the color and moisture content changes of jujube slices in real-time.Secondly,correlation models between color parameter(a^(*)value)and nutritional quality attributes(vitamin C,reducing sugar)were established to predict vitamin C and reducing sugar content of jujube slices during the drying process.Finally,the upper computer monitoring software was integrated and designed based on LABVIEW virtual instrument,and the real-time monitoring system was tested and validated.Results showed that:the changing trends of color(L^(*),a^(*),and b^(*)values)monitored by the system were basically the same as the results detected by color difference meter,and the average errors of L^(*),a^(*),and b^(*)values were 0.93,0.52,and 0.73,respectively.The average relative error of moisture content between the system monitoring and manual static detection was 0.18%.The average error of vitamin C and reducing sugar content between the system prediction and manual detection were 50 mg/100 g in dry basis and 0.71g/100 g in dry basis,respectively.The current work can provide a useful reference for real-time monitoring of quality attributes of fruits and vegetables during the drying process.

基于预测模糊FLC控制下农业生产过程监控与优化

该文以温室监控系统为案例,针对传统PLC控制在复杂环境下的局限性,提出了一种基于预测模糊FLC控制的优化技术,该技术利用模糊逻辑对环境变量进行模糊化处理,并结合预测算法对未来状态进行预测,从而实现对温室环境的精准控制和优化调节。通过试验验证,该优化技术能够提高温室生产效率、降低能耗成本,并保障农作物的生长质量和产量稳定性。研究结果可以为农业生产过程的智能化监控与优化提供新的思路和方法。

烧结工况智能识别与质量在线预估系统的应用

由于烧结过程恶劣的生产环境、高昂的在线检测成本及较低的在线检测精度等原因,导致目前公司生产过程中的工况和质量只能通过离线检测和人工观察等方式获取,不能及时、有效地监控生产、调整工艺,难以提高质量。基于此,针对厂内烧结工况识别困难、识别结果主观性大以及烧结质量离线化验滞后等问题,通过数据及图像处理、深度学习及多信息融合等技术,实现了烧结生产过程多级工况在线判断,系统工况判断准确率达90%以上,质量在线预估误差在95±5%以内。

电石法聚氯乙烯信息化智能化实践

聚焦电石法聚氯乙烯信息化的主要问题,通过定制化开发与工业应用MES信息化管理系统、生产工艺自动控制系统、设备智能监测系统以及能源管理信息化平台,提高了生产运行和能源管理效率,取得了良好的实践效果。

卷烟生产质量全过程信息化管控系统设计

现有的卷烟生产管控模式未能将监测与报警功能有效融合,对烟叶切丝等生产过程的控制效果不佳,导致卷烟包装存在褶皱和断裂现象,对此,设计一个全新的卷烟生产质量全过程信息化管控系统。设计电压输出电路控制电平,处理滤波电路处理前端干扰;根据固定的卷烟生产批次,建立数据在线采集与显示模块,实时获取生产全过程信息,设计与优化生产质量动态评估过程,采用有序加权算子评价卷烟生产质量,构建自动监测与报警融合模型,以信息化方式管控整个卷烟生产过程。实例测试结果表明,该系统管控下的烟丝质量更好,成丝细腻,烟丝压实,没有出现褶皱问题,对卷烟包装质量的影响最小。

深度学习行为识别的挖掘机生产效率监控系统

为了对大型施工设备的生产率进行准确的实时监测和分析,提出了一种基于计算机视觉的设备识别和生产率监控系统。该方法利用Faster-RNN对视频序列中的挖掘机和卡车进行识别检测,然后通过ASFormer对挖掘机动作进行动作分割。基于行为分割的结果对挖掘机的工作效率进行计算。比较了人工计算与行为分割网络的准确度的差距,对2台不同的挖掘机进行动作的识别和分析,结果显示平均准确度分别为91.7%和94.8%。因此,当多个场景并行处理时,可以大大节省人力成本,验证了本方法的有效性和实用性。本研究为慧工地的数字化管理提供了有效的技术基础,开辟了计算机视觉应用的新场景,提出了一种新的计算挖掘机生产效率的计算因子,更适合于通过统计挖掘机动作来进行效率计算。

轨道车体涂装线生产过程监控系统设计

针对高铁、地铁车体涂装车间加工流程的分析与目前涂装车间存在的问题,结合软件系统的运行特点,分析涂装车间的系统需求以及实际生产的工艺流程和业务流程,从而设计生产过程监控系统的体系架构、功能模块和软硬件的集成。通过此系统监控生产过程中的物料、质量数据及车体位置等信息,解决了生产数据不透明、设备保养不及时、车体信息追溯难等问题。提高了设备利用率及工时统计精准度,实现了车体信息的精准追溯,达到了信息化管控的目的,从而推动了企业的信息化进程。

基于网络数据的预先包装熟肉制品生产过程致病菌监控指标选择分析研究进展

主要研究预先包装熟肉制品生产过程中主要食源性致病菌污染风险来源和变化趋势。以GB 29921—2021《食品安全国家标准预包装食品中致病菌限量》中肉制品涉及的沙门氏菌、单核细胞增生李斯特菌、金黄色葡萄球菌和致泻大肠埃希氏菌为研究对象,基于网络数据统计畜禽肉原料、生产过程环境和过程产品以及市售产品中这4种致病菌污染监测结果,通过对比在产销过程不同阶段的检出情况,分析污染风险的变化趋势,为企业在生产过程中合理选择和开展致病菌监控提供建议。结果表明:畜禽肉原料中4种致病菌污染较普遍,总检出率达到10.63%;生产过程中,单核细胞增生李斯特菌在生产环境中检出率为7.25%,沙门氏菌检出率为2.65%,金黄色葡萄球菌检出率为1.07%,通过熟制工艺和生产过程卫生控制措施,金黄色葡萄球菌和沙门氏菌检出率下降趋势明显大于单核细胞增生李斯特菌,结合单核细胞增生李斯特菌嗜冷、易形成生物膜且具有较高致病风险的生物学特性,建议企业重点关注该致病菌在生产过程中的污染风险,并将其列为致病菌监控指标。需要说明的是,因为致泻大肠埃希氏菌为新版GB 29921新增加的限量指标,所以污染监测数据少于其他3种致病菌,后续应针对该致病菌加强监测力度。

欧盟新版无菌药品附录主要变化点及对我国无菌药品生产管理的启示

目的:通过比对分析提出工作建议,为国内无菌生产企业进一步提升无菌管理水平提供借鉴。方法:介绍最新版欧盟无菌药品附录的一些主要变化点,对比分析欧盟和我国在无菌药品方面的异同。结果与结论:欧盟无菌附录在适用范围、污染控制体系(CCS)、环境监测和工艺监测、部分生产和生产具体技术等方面较原有版本和我国要求有较大提升。国内无菌生产企业应重视欧盟无菌附录的变化,逐步加强无菌管理,大力推进无菌生产企业的国际化进程。