340 results about "Instrument Data" patented technology

The invention belongs to the field of pollution prevention and control data processing, and particularly relates to a laboratory instrument data automatic acquisition and quality tracing system and a laboratory instrument data automatic acquisition and quality tracing method. A distributed search traceability mapping space is constructed through the storage mapping module to realize space-time-semantic composite indexing and classification detection of data, and a causal reasoning model of the quality judgment module is used for performing anomaly judgment; a reverse Hash traceability mechanism is innovatively designed, an index-traceability node graph and a segmented Hash heterogeneous node anchoring strategy are combined, Hash head, body and tail fragments are stored in three types of nodes of acquisition, storage and detection, and tamper-proof traceability of abnormal data and dynamic optimization of acquisition parameters are realized through cross-node aggregation verification; according to the invention, the problems of low data credibility and poor abnormal traceability efficiency in ecological environment monitoring are solved, and full-chain closed-loop management of data acquisition-analysis-traceability-acquisition configuration adjustment is supported.

The invention provides an intelligent laboratory full-process collaborative management system and method based on multi-dimensional data fusion, and the method comprises the steps: obtaining an original data stream from laboratory instrument equipment, carrying out the format recognition and conversion of heterogeneous instrument data through a standard protocol adapter, and generating a first data set in a unified format; aiming at the first data set, executing automatic analysis by adopting data middleware, and integrating into a structured second data set based on a metadata rule of field mapping and unit conversion; according to the second data set, a multi-source data fusion model is constructed, data are classified according to instrument types through batch processing and aggregated according to timestamps, and a fused third data set is generated; aiming at the initial process configuration, adjusting a node sequence and a parameter threshold value by using a visual design tool, and generating an optimized process configuration adaptive to the diversified scene; according to the optimized process configuration, process scheduling is executed through a dynamic process engine, execution time and resource occupation are monitored in real time, and a process execution log is generated.

The invention discloses a thunder early warning model optimization method based on a deep learning algorithm. The thunder early warning model optimization method comprises the steps that five risk early warning levels are divided according to the distance between a thunder occurrence position and a target point, namely six corresponding early warning levels are obtained; selecting lightning positioning, radar extrapolation results and atmospheric electric field instrument data around the target point from the sample data set; and risk grading preprocessing is carried out on the thunder early warning probabilities and critical thresholds of the three early warning factors. The method has the following advantages that the precision is high, analysis and modeling are carried out through comprehensive multi-source data, information related to thunder and lightning occurrence and development can be captured more comprehensively, and therefore the precision of early warning is improved. Data can be obtained and processed in real time, early warning information can be updated in time, and the requirement of a user for the real-time performance is met. Personalized customization: personalized risk grading and early warning setting can be carried out according to characteristics of different target point locations and user requirements.

The invention provides a personalized health monitoring method based on multi-instrument data fusion, and aims to solve the problems of non-uniform data standards and poor interoperability of existing multiple health monitoring devices. According to the method, a data standardization protocol is automatically selected and optimized according to data characteristics (such as sampling frequency, data precision and the like) of different devices (such as oximeters, sleep monitors and the like) through a machine learning algorithm, and seamless fusion of data between the devices is ensured. Different from a traditional static data processing mode, the scheme not only improves the collaboration efficiency among the devices, but also effectively solves the problems of inconsistent data formats and information loss. For different types of data generated by multiple devices, a data fusion strategy is automatically identified and optimized by adopting an intelligent fusion algorithm based on deep learning, and information conflict or delay caused by mismatching of data features is avoided.

The invention relates to the field of water treatment and video treatment, and discloses a water plant coagulating sedimentation effect estimation method, system, medium and equipment based on alumen ustum target tracking, and the method comprises the following steps: collecting alumen ustum video images, and aligning online flow water quality instrument data with the alumen ustum video images; inputting the aligned online flow water quality instrument data and alumen ustum video images into a video target tracking large model, tracking each alumen ustum target, and constructing the form and motion characteristics of alumen ustum to obtain alumen ustum characteristic data; based on alumen ustum characteristic data and online flow water quality instrument data, a sedimentation tank effluent turbidity estimation model is trained; reading alumen ustum video streams and online instrument data on line, inputting the alumen ustum video streams and the online instrument data into the large video target tracking model, and estimating the effluent turbidity of the sedimentation tank through the effluent turbidity estimation model of the sedimentation tank. The risk that the adding amount of the coagulant is not adjusted in time can be reduced, the water quality stability of effluent of the sedimentation tank is improved, and the backwashing frequency and economic cost of the filter tank are reduced.

The invention discloses a well station instrument fault intelligent diagnosis system and diagnosis method based on big data and artificial intelligence, and the system comprises a well station instrument data management and acquisition module which collects and stores well station instrument data in real time, and provides a visual data display interface at the same time, so that an operator can know the operation state of a well station in time; the well station instrument data analyzing and processing module is used for analyzing the collected well station instrument data and extracting key index data; according to the well station instrument fault early warning model system, a multi-level early warning model is created, and potential faults of well station instruments can be effectively recognized and predicted; and the well station instrument fault diagnosis and analysis module is used for quickly identifying abnormal conditions in the well station instrument data, making fault early warning and analysis reports and providing the fault early warning and analysis reports to management personnel. According to the intelligent diagnosis system and the diagnosis method, problems possibly occurring in production are analyzed and predicted through an intelligent algorithm and a model, and corresponding measures are taken in advance so as to ensure safety and stability of oil well production.

The invention relates to the technical field of sports competition real-time penalty, and discloses a sports competition real-time penalty system and method based on multi-modal sensing and AI algorithms, and the system comprises a monitoring identification module, an instrument tracking module, a rule parameter construction module, a fusion penalty module, an output and snapshot module, and a wireless linkage and energy management module. The method comprises the following steps: acquiring image and instrument data, and constructing a multi-modal sensing structure; calling a rule base to generate a penalty vector and loading a structure constraint; fusing the image and the instrument features to generate a feature tensor; inputting an AI model to judge the legality of the action and outputting a penalty result; driving the snapshot logic to generate an image record and synchronizing the image record to the terminal; and adjusting the energy consumption strategy according to the task load and the equipment state. According to the invention, a multi-modal perception input structure fusing instrument motion data and a visual image flow is introduced, so that the system can synchronously acquire displacement, acceleration and attitude images in the action recognition process, and a penalty basis is checked in a cross manner from multiple dimensions.

The invention provides a TFT instrument data interaction and cloud collaborative management system, and relates to the technical field of TFT display, and the system comprises a communication collection module which is used for deploying a plurality of integrated thin film transistor display screen units in a transport vehicle, and each unit collects the operation parameter data of the vehicle in real time through a vehicle-mounted sensor network; and the cloud receiving module is used for receiving and analyzing the operation parameters of the transport vehicle, determining the vehicle position, the operation state, the real-time parameter information content and the display mode position which need to be displayed on the monitoring interface, and mapping related data into interface elements to generate a motorcade monitoring interface. According to the invention, real-time data intelligent analysis and cloud cooperation are realized, full-link visualization and remote optimization of the vehicle state are realized, the operation efficiency is improved, and the maintenance cost is reduced.

The invention discloses a laboratory instrument data automatic acquisition and analysis method and system, and relates to the technical field of laboratory instrument acquisition and analysis, and the method comprises the steps: obtaining sample identification information, automatically sorting and distributing the sample identification information to different analyzers, uploading detection data to a central database in real time, and associating a sample number, a detection timestamp and an instrument number; the method comprises the following steps: constructing a multi-dimensional feature space based on historical detection data, performing sub-cluster division by adopting a density clustering algorithm, establishing a cross-index logic constraint relationship in combination with an expert rule base, mapping real-time detection data to the multi-dimensional feature space, judging a data exception type according to sub-cluster affiliation and the logic constraint relationship, and when judging that the data is exceptional, judging that the data is abnormal. And triggering a secondary detection process, automatically dispatching a mechanical arm to send the sample back to a specified instrument to execute differentiated reinspection operation, and generating a comprehensive report containing detection process tracing. According to the invention, the automation level of detection is improved, the labor cost is reduced, the detection quality and efficiency are improved, and the laboratory detection work efficiency is improved.

The invention discloses a lightning early warning method based on atmospheric electric field networking data, which relates to the technical field of lightning early warning, and comprises the following steps: dividing electric field instrument data into time windows with a certain length, integrating and splicing effective data, and dividing the data into a crossed and overlapped region and a non-crossed and overlapped region; determining whether lightning activity occurs in each region or not to obtain a data set; a graph convolutional network model with an adjacent matrix is constructed, whether different electric fields are crossed or not is obtained according to the adjacent matrix, and if not, independent training is directly carried out; if the similarity m of the data of the electric field instruments is greater than or equal to 0.6, determining that the area is a crossed overlapping area, and performing fusion training on the data of the electric field instruments participating in the crossing; and if m is less than 0.6, determining that the area is a non-cross overlapping area, and at the moment, independently training data of different electric field instruments to obtain a thunder and lightning early warning model. According to the method, the capability of judging and fusing the signal confidence in the overlapping region is improved, and the accuracy and reliability of lightning early warning are improved.

The utility model relates to an existing cavern compressed air energy storage physical model test system, which comprises a physical model, a hydraulic loading system, an air circulation charging and discharging system and a monitoring system, and is characterized in that the physical model comprises a surrounding rock and an air storage part, the surrounding rock comprises an external rock block and an internal rock block, the internal rock block is provided with a hole, and the hole penetrates through the internal rock block along the axial direction of the internal rock block; one or more prefabricated cracks are formed in the wall of the hole, the outer rock block is arranged on the outer side of the inner rock block in a surrounding mode, at least part of the air storage part is arranged on the two sides of the inner rock block in the axial direction, and the air storage part can seal the hole; the hydraulic loading system can apply a load from an external rock block to an internal rock block to the physical model, and the air circulation inflation and deflation system can inflate and deflate the hole; the monitoring system comprises a monitoring instrument and a data acquisition system, the monitoring instrument can perform data monitoring on the physical model, and the data acquisition system is in data connection with the monitoring instrument and can perform data transmission and processing.

The invention discloses an intelligent developing active packaging film thickness uniformity detection method and system, and relates to the field of film thickness uniformity detection.The intelligent developing active packaging film thickness uniformity detection method comprises the steps that a terahertz time-domain spectrum detection device is arranged, and area scanning is conducted on a to-be-detected developing active packaging film; network edge nodes are set, and data acquisition, data preprocessing and film thickness uniformity judgment are processed step by step; based on a convolutional neural network algorithm, preliminarily confirming the film thickness of the developing active packaging film; environment data are collected, and the film thickness of the color developing active packaging film is corrected through an environment error compensation algorithm; analyzing the influence of different film thicknesses of the developing active packaging films on the data of the terahertz time-domain spectroscopy instrument, and performing feedback correction on the film thicknesses of the developing active packaging films; and carrying out uniformity evaluation on the to-be-detected developing active packaging film area. The method has the advantages that non-contact detection is adopted, the efficiency of detecting the film thickness uniformity of the developing active packaging film is effectively improved, and the error influence caused by the film thickness and the environment is avoided.

The invention particularly relates to an instrument intelligent analysis method and system based on a video image, and the method comprises the steps: carrying out the preprocessing based on the instrument video image, and carrying out the illumination contrast parameter adjustment, thereby improving the visual effect and definition of the image. Then, image segmentation operation is carried out based on the preprocessed instrument image, the image segmentation operation is used for accurately positioning a feature region needing to be identified, and the accuracy of the key features of the instrument is improved; and further, instrument feature extraction is carried out on the instrument region segmentation map by utilizing an instrument feature recognition model, when the instrument type is a pointer type instrument, pointer positioning refinement and scale calibration are carried out based on the key features of the instrument to obtain calibration data, and then instrument reading calculation is carried out based on the calibration data to determine the instrument reading. And an instrument data identification mode combining a classic image processing technology and convolutional neural network model features is integrated, so that the accuracy of instrument data reading is improved.

The application provides a weather load-based air conditioner energy control method and device and a storage medium, wherein the method comprises the following steps: saving PLC bottom layer instrument data, indoor temperature sensor data and public data to a cloud platform server; processing the above data, calculating first index data in units of predetermined working time, calculating an optimized load amount according to the first index data; selecting a date closest to a predicted load day comprehensive temperature T in historical operation data as a reference day, taking the load of the reference day as a reference day load; and adjusting air conditioner energy according to the optimized load amount through two preset optimization conditions and the reference day load. The application predicts future load change rules by mining the corresponding relationship among climate, indoor temperature and load change rules through historical data, so as to realize the purpose of precise energy supply and effectively avoid energy waste.

The invention provides instrument detection equipment and method, a gateway and an instrument inspection system, and relates to the technical field of Internet of Things. The instrument detection equipment comprises a detection element, a controller and a communication module, wherein the detection element and the communication module are both in communication connection with the controller; the controller is used for controlling the communication module to send a network access request to the gateway; the controller is used for responding to the received network access success information sent by the gateway and controlling the detection element to measure the instrument based on a preset measurement frequency so as to obtain target instrument data; and the control module is used for controlling the communication module to send the target instrument data to the instrument inspection terminal through the gateway based on the preset sending frequency. According to the instrument detection equipment, remote, efficient and accurate monitoring of the instrument is realized, the efficiency and the accuracy of instrument inspection can be improved, the cost and the risk of manual inspection are reduced, and in addition, the energy consumption of the instrument detection equipment is low.

The application discloses a kind of transformer substation in-station instrument automatic meter reading method and system, it is related to power station inspection technical field, including: meter reading work is completed by transformer substation in-station instrument meter reading software, including automatic meter reading module and meter reading photo analysis module;Automatic meter reading module is used to use mobile terminal and main control room computer to establish network communication, obtains meter reading photo by photographing field instrument to mobile terminal, and is returned to main control room computer, is downloaded and is transmitted to meter reading photo analysis module calculation;Meter reading photo analysis module is used to initialize meter reading photo, position target and calculate the instrument contained in meter reading photo, extract the outline of target, identify the edge coordinates of instrument pointer by contour matching mode, calculate the instrument reading pointed by instrument pointer by looking up black and white boundary in binary image.The application can replace artificial field meter reading, automatically obtain the reading of transformer substation instrument by field photo identification, generates instrument data report.

The embodiment of the invention provides a process industry real-time monitoring and early warning system and method and a storage medium, and the system comprises a data collection module which is used for receiving the safety instrument data of each device and the sensing data of each sensor in the process industry in real time; the verification module is used for calling an SIL level verification model to perform online verification on the data according to the safety instrument data, and determining risk information of each piece of equipment; the anomaly detection module is used for determining anomaly data based on an anomaly detection model according to the sensing type data and the risk information; and the early warning module is used for generating early warning information according to the abnormal data and the risk information. Through cooperative work of all the modules, safety and reliability in the industrial production process can be remarkably improved, potential problems can be warned in advance, non-planned downtime and maintenance cost are reduced, meanwhile, the service life of equipment is prolonged, and therefore production efficiency is improved.

The invention discloses an instrument data reading and collecting system and method based on image recognition, relates to the field of instrument image recognition, introduces a specially trained Meter-CornerNet deep learning model, and can accurately position four key angular points of an instrument panel from an original image containing perspective distortion and uneven illumination. After the angular points are obtained, an inclined instrument area is corrected into a standard front rectangular view through perspective transformation, and illumination normalization is carried out, so that the interference of the angle and illumination problems on the image quality is improved. And then, digital segmentation and OCR identification are carried out on the processed high-quality image, a result is verified, and finally, an accurate reading is sent to an Internet of Things platform. According to the method, high robustness and high accuracy of the whole system in a complex real scene are ensured through a strategy of first accurate correction and then identification verification.

The invention discloses a remote cooperative verification and communication management system and method for a metering instrument, and relates to the technical field of instrument intellectualization. The method comprises the following steps: acquiring environment data and original instrument data of a plurality of metering stations through an Internet of Things communication protocol, and transmitting the original data to a remote communication management center for protocol adaptation processing to generate standard data frames in a unified format; further performing semantic standardization on the standard data frame to obtain a data set for verification; and constructing a cross-site mutual parameter verification model based on the data set and the environmental data, calculating a deviation value of each metering instrument, determining a cooperative verification priority index of the metering instruments in combination with a historical error change trend, generating a remote cooperative verification list containing verification task distribution information, and sending the remote cooperative verification list to a server. And the information is issued to each site for execution through an Internet of Things communication protocol. The remote automatic verification scheduling and data consistency guarantee of heterogeneous instruments is realized, and the efficiency and reliability of multi-site collaborative verification are improved.

The invention relates to the technical field of laboratory information management systems, and discloses an LIMS (Laboratory Information Management System)-based full-category detection data integration and quality tracing management system, which comprises a data modeling module for automatically generating domain ontology extension by adopting a method of combining ontology modeling and natural language processing; the data processing module is used for acquiring original data of the multi-source heterogeneous instrument data, and performing format recognition and semantic mapping on the original data by adopting a full-category detection data unified model; the block chain traceability module is used for storing traceability standardization detection data in a classified manner and establishing a layered framework; the quality prediction and early warning module is used for constructing a quality influence factor library and generating a prediction result based on the quality influence factor library; the cross-system integration module is used for obtaining a cross-system integrated data management platform by adopting a containerization technology and an event traceability architecture; according to the invention, the quality tracing query response time is shortened, and the credibility and timeliness of quality management are improved.

The invention relates to the technical field of data synchronization, and particularly discloses a multichannel instrument data synchronization method and system. The method comprises the following steps: acquiring time sequence data and timestamps of each channel of a multi-channel instrument, calculating a data sampling period and a clock drift rate according to the time sequence data and the timestamps so as to quantify time synchronization deviation, extracting a transient energy ratio and a spectrum distortion degree of a time sequence signal, measuring signal stability, performing normalization processing, and combining dynamic drift risk indexes of the time sequence signal, the spectrum distortion degree and the signal stability so as to obtain a time sequence signal. The low-risk data form a synchronization group, the high-risk data are isolated and corrected and then merged into the synchronization group to form synchronization data, and through dynamic risk assessment and grading processing, the influence of single-channel abnormity on overall synchronization is avoided, the clock and signal stability is considered, and the synchronization reliability is improved.

The invention relates to the technical field of industrial production process control, in particular to an alumina production plant distributed control method and system.The method comprises the steps that alumina production instrument data monitored in real time is collected and stored, and first alumina production data and second alumina production data are obtained; combining the two kinds of data with alumina production reference standard data, a dynamic path algorithm and an Euclidean distance to calculate the similarity of the data, and judging whether abnormal feature analysis needs to be carried out or not through an alumina abnormal threshold value; when anomaly analysis is needed, first aluminum oxide anomaly feature data are obtained by combining a time series data anomaly recognition algorithm and time series data prediction, second aluminum oxide anomaly feature data are obtained through clustering attribution analysis, and corresponding self-adjusting measures are taken; according to the method, the DCS system can carry out more specific and more accurate alumina fault monitoring under the condition that the production process is not influenced, and the self-diagnosis range of the alumina DCS system is improved.

In an example, a system includes a processor that may receive instrument data from a surgical instrument related to operation of the surgical instrument during a surgical procedure; (ii) receiving, from the surgical navigation system, position data indicative of a position of the surgical instrument relative to the patient's anatomy during the surgical procedure; (iii) determining kinematic data based on the position data; (iv) determining a plurality of surgical performance indicators indicative of characteristics of the surgical performance using the instrument data and the kinematic data; (v) analyzing (a) kinematic data and instrument data with respect to (b) a plurality of surgical performance metrics; and (vi) causing the user interface to output information based on the analysis to provide feedback to a surgeon and / or stakeholder (e.g., a hospital administrator) relating to the performance of the surgical procedure.

The present invention provides a method and system for monitoring the status and predictive maintenance of a liquid chromatography instrument, which relates to the technical field of data processing. By jointly modeling the sensor data of the liquid chromatography instrument (such as continuous parameters like flow rate, temperature, pressure, etc.) and the log data (such as discrete information like alarm status, equipment events, etc.), and performing data dimensionality reduction based on the geometric similarity of data points, the local structure and global information of the liquid chromatography instrument data are maintained, while reducing the information loss during the dimensionality reduction process. Further, a status monitoring model is used to process the data, enhancing the context awareness ability of the model. When anomalies or faults occur, it can comprehensively consider various factors for accurate diagnosis, showing higher accuracy and reliability in monitoring the complex status changes of the liquid chromatography instrument.

The invention discloses an intelligent data management system and method for an experimental instrument based on an RFID electronic tag, and relates to the technical field of data management, and the method comprises the steps: carrying out the RFID electronic tag initialization of a pre-managed experimental instrument; performing instrument management deployment based on radio frequency management and application programming, and constructing an instrument intelligent management module, performing continuous radio frequency identification on the initialized RFID electronic tag, and determining an instrument data stream; receiving and storing instrument data streams in real time, and constructing an instrument database; and performing experimental instrument management and data flow sharing management based on the instrument database. The technical problems of low experimental instrument management efficiency and poorer instrument data instantaneity and accuracy caused by difficulty in quickly and accurately identifying the experimental instrument and tracking the use condition of the experimental instrument in real time in the existing experimental instrument data management are solved. The technical effect of improving the management efficiency of the experimental instrument data and the real-time performance and accuracy of the instrument data is achieved.

The invention relates to the technical field of vehicle tracing, and discloses a vehicle tracing analysis method and system in a hot event and electronic equipment. The method comprises the following steps: acquiring a hot event, identifying a target vehicle in an image or video associated with the hot event, and determining a target feature; searching based on the published information of the hotspot event, and determining the occurrence time period and the location area of the hotspot event; inputting the target feature, the time period and the position area into a cloud for joint retrieval, and determining a vehicle identification code to which the target vehicle belongs; vehicle information corresponding to the target vehicle in front and back time periods is called according to the vehicle identification code, data analysis is carried out on the vehicle information, an analysis report is determined, and the vehicle information including instrument data, central control screen display data and appearance data is stored in the cloud. Through cloud joint retrieval, accurate identification of the target vehicle is realized, rapid and accurate public gateway response and forward maintenance of the vehicle brand value are facilitated, and the event processing efficiency is improved at the same time.

The invention discloses an analysis instrument data management system and method based on cloud computing, and belongs to the technical field of data management. According to the method, gas inspection task requirements are collected and converted into feature data, background interference, response time and application occasions are comprehensively analyzed through a trained artificial intelligence model, and adaptive analysis instruments are screened to form a set; qualified parts are screened in combination with task requirements to form a candidate set, a graph structure is constructed through physical, technical and functional compatibility analysis, and an instrument and part matching set is generated. Establishing a mechanism-driven virtual simulation environment, restoring a dynamic working condition, embedding a three-class coupling effect mathematical model, and performing dynamic linkage simulation on a matching pair; setting a multi-dimensional quantitative index score, superposing the coupling effect correction value to obtain an execution score, and selecting an optimal matching pair through descending sorting. Physical integration and debugging are completed according to configuration, an actual gas inspection task is put into use, and accurate adaptation and efficient execution of the whole process are achieved.

The invention relates to the technical field of purified water systems, and discloses an intelligent control and cleaning method of a two-stage reverse osmosis purified water system, which comprises the following steps: step 1, acquiring water temperature, pH value, conductivity and instrument data of a water inlet and a water outlet in the purified water preparation process; 2, comparing the obtained data of each instrument with a set data reference and fluctuation range, and judging whether the data of each instrument is abnormal or not; and step 3, when the instrument data is abnormal, obtaining estimated correct data of the abnormal instrument according to the normal and abnormal instrument data and the historical data. According to the method, whether part of instrument data is abnormal or not is judged through various data in the pure water preparation process, when the instrument data is abnormal, estimated correct data is obtained according to association characteristics of an abnormal instrument and other normal data and the obtained various data, and then the obtained estimated correct data is verified.

The invention discloses a health detection instrument data processing system and method based on the Internet of Things, and relates to the technical field of data analys.The method comprises the following steps that sampling intervals are determined, a CGM sensor is controlled to collect data according to the corresponding sampling intervals, and a frequency mixing blood glucose data set is formed; constructing an input data set, and introducing a sampling interval weight to construct a basic trend model; based on the input data set, introducing a correction item to construct a scene correction model, and outputting a final prediction value at the current next moment; setting a future prediction time interval and a prediction moment number N, generating N prediction values based on the basic trend model and the scene correction model, and integrating the N prediction values to form a prediction sequence; setting a threshold value to analyze whether the prediction sequence is abnormal or not and the abnormal type; when an anomaly is detected, a feedback mechanism is triggered. The method can effectively improve the situation that in the prior art, dynamic blood glucose monitoring sampling frequency lacks scene adaptability and is insufficient in frequency mixing data processing capacity.

A method for processing data of an analytical instrument for analyzing biological samples is presented. The method comprises receiving instrument data from the analytical instrument at a data processing module communicatively connected with the analytical instrument, generating metadata from the received instrument data at the data processing module, applying a first encryption to the instrument data at the data processing module, applying a second encryption to the generated metadata at the data processing module, and transmitting the encrypted metadata and encrypted instrument data to a remote server. The remote server and the data processing module are communicatively connected. The method also comprises removing the second encryption from the metadata at the remote server and forwarding the instrument data encrypted by the first encryption from the remote server to a management system of the analytical instrument.