19results about How to "Effective traceability" patented technology

The invention discloses a heat preservation and thermal insulation project whole industrial chain quality tracing method and system, and the method comprises the steps: obtaining industrial chain data and monitoring data of a heat preservation and thermal insulation project, carrying out the tracing coding based on the industrial chain data and the monitoring data, and constructing a heat preservation and thermal insulation project whole industrial chain quality tracing network, determining industrial chain data corresponding to the to-be-traced monitoring data through the traceability code, and performing correction and anomaly screening on the to-be-traced monitoring data to obtain to-be-traced abnormal monitoring data, and according to the to-be-traced abnormal monitoring data and the thermal insulation project whole industry chain quality traceability network, determining a thermal insulation project whole industry chain quality hidden danger source. The method not only can improve the efficiency and accuracy of quality traceability of the whole industrial chain of the thermal insulation project, but also has good interpretability, and can be directly applied to a quality traceability system of the whole industrial chain of the thermal insulation project.

The invention discloses a small-scale greenhouse gas tracing method based on numerical simulation and data assimilation, and belongs to the technical field of greenhouse gas tracing. The method comprises the following steps: acquiring real-time meteorological monitoring data in a park, and performing cyclic assimilation; establishing a high-precision backward trajectory tracking system to obtain refined backward trajectory data of a monitoring station; establishing a high-precision greenhouse gas traceability calculation model to obtain greenhouse gas potential emission source distribution data; averaging the emission source distribution data to finally obtain potential emission source comprehensive probability distribution data; according to the invention, through a high-resolution numerical simulation and data assimilation technology, diffusion, transmission and chemical reaction processes of greenhouse gas in a small-scale range can be accurately simulated; meanwhile, the grid empirical weighting function of the traceability algorithm is optimized, the adverse effect of the low-track end point number on the final traceability result is effectively reduced, the robustness of the model is enhanced, and it is ensured that the traceability result is more accurate, reasonable and stable.

The invention discloses a traceability management method and system for an industrial product, and relates to the technical field of industrial product production management, a comprehensive product traceability database is established, and detailed records of product production, accessory production links and assembly procedures are covered; performing deep analysis on historical product abnormal conditions by using a semantic recognition model, recognizing abnormal state types and parameters, constructing a historical abnormal state parameter group, and establishing association with abnormal accessories to form an abnormal state-abnormal accessory association group; performing classification collection and preferential selection on the association groups, and constructing an abnormal state-abnormal accessory corresponding library; for the current abnormal condition of the product, the semantic recognition model is used again for recognition and analysis, and the abnormal part and the production link thereof are quickly locked by matching the abnormal state-abnormal part corresponding library, so that efficient tracing is realized; according to the method, the tracing efficiency and accuracy are remarkably improved, the tracing cost is reduced, and powerful support is provided for quality control of industrial products.

The invention relates to the technical field of pet medical information, and discloses a pet cloud medical record management method and system, and the method comprises the steps: collecting pet health data through a user terminal, and the pet health data comprises video and text description; uploading the video to a video processing module, automatically editing the video through the video processing module, and extracting key segments; the text description and the key segments are integrated, and a structured pet electronic medical record is generated through a medical record generation module; storing the pet electronic medical record to a cloud storage server, and storing the hash value of the pet electronic medical record to a block chain network; and according to a user instruction, safe sharing and multi-terminal synchronization of the pet electronic medical record are realized through the safety control module. According to the method, the problem that the pet cannot know the pain point of the illness state by himself / herself is solved, the behavior performance of the pet can be effectively recorded and traced, the burden of a pet owner for sorting data is relieved, and meanwhile, a more intuitive and complete diagnosis and treatment basis is provided for veterinarians.

The present invention relates to the technical field of material detection, and discloses an energy-saving material density detection method and system. The method comprises: obtaining basic data of a material to be detected and calculating real-time density, inputting a real-time image set into a pre-built material classification model to obtain a material category, determining a standard density interval according to the category and a mapping relationship, and judging whether the real-time density is in the standard density interval, if not, marking the material, generating an initial detection process video, segmenting the initial video based on the real-time image to generate an intermediate detection process video, and processing the intermediate video according to the real-time density to generate a target detection process video; obtaining the density of the material by calculation, being able to judge whether there is a material that does not meet the standard according to the density of the material, and being able to efficiently trace the material that does not meet the standard after determining the material that does not meet the standard.

This invention provides a method for monitoring volatile pollutants, belonging to the field of pollutant monitoring. The method includes: dividing a monitoring area into grid cells; acquiring the pollutant concentration, wind speed, and wind direction within each grid cell; determining the pollutant concentration residence time based on the wind speed and wind direction; determining the grid weight concentration based on the pollutant concentration and the concentration residence time; dividing the grid into concentration intervals according to the magnitude of the grid weight concentration, with each interval corresponding to a different identifier; and displaying each grid cell with the identifier corresponding to its respective concentration interval. This method couples the monitored pollutant concentration with wind speed and wind direction to determine the grid weight concentration of each grid cell, divides the grid weight concentration into concentration intervals, and displays each grid cell with the identifier corresponding to its respective concentration interval. Through these identifiers, the dynamic change process of the grid cell concentration can be observed, and effective source tracing can be performed.

The invention provides an intelligent manufacturing data exception identification method and system based on artificial intelligence, and relates to the field of data processing. According to the method, multi-source signals of production, control and detection equipment are collected through an energy manifold sensing architecture, and a global signal flow is constructed; performing event triggering type observation through a space-time perception model to generate a local state sequence; identifying a cross-process coupling relationship through the coupling analysis model to form an interference spectrum set; the virtual manufacturing cognitive body is used for executing mimicry copying and generating an abnormal perception event flow; an abnormal path is backtracked by an energy propagation model to identify an energy leakage set; and finally, generating an energy narrative set containing causes, propagation and attenuation fragments through a semantic narrative model, and realizing intelligent identification and interpretation of manufacturing data anomalies. By implementing the technical scheme provided by the invention, the root cause and the propagation path of the abnormity can be effectively traced, and the intelligent manufacturing data abnormity identification effect is improved.

The invention relates to an air conditioner outdoor unit electrical security check method, system and device and computer equipment. The method comprises the steps of obtaining production order information of the air conditioner outdoor unit under the condition that it is determined that the air conditioner outdoor unit moves to a preset detection point, conducting electrical security check on the air conditioner outdoor unit under the condition that it is determined that an external power source is mounted to a connection point position of the air conditioner outdoor unit, obtaining an electrical security check result, and if the electrical security check result shows that the air conditioner outdoor unit passes electrical security check, determining that the air conditioner outdoor unit passes electrical security check. And if so, associating the electrical security check result with the production order information, storing the result in a preset database, and releasing the air conditioner outdoor unit. By adopting the method, the electrical security check data tracing efficiency can be improved.

The application discloses a railway freight car configuration history management method, device, medium and equipment, and the method comprises the following steps: acquiring history basic information, including the attributes and attribute fields of each configuration item. An association matrix is constructed according to the correlation intensity of each attribute field, the penetration probability between each attribute field is determined based on the association matrix, the attribute grouping and the history type identification of the attribute field are determined according to the penetration probability. Each classified attribute field is taken as a node to construct a causal reasoning graph, and the causal strength and the causal path between each classified attribute field are determined according to the causal reasoning graph. The classified attribute fields are established into a hierarchical data structure according to a preset configuration level, the causal strength and the causal path between the classified attribute fields are marked in the hierarchical data structure, and the attribute configuration result of each configuration item is obtained. The application is helpful for the effective management and tracing of the attribute information of the railway freight car configuration item.

This invention presents a multi-scale subway disaster risk level prediction method based on multi-source data fusion. It obtains data collected during the construction, operation, and maintenance of subway projects, stores it in a database, and assigns it specific timestamps and spatial coordinates. After preprocessing, the data is divided into three categories: text, images, and sensor data, each containing multiple modalities. A multi-modal data analysis and fusion unit is constructed, including a multi-head cross-modal attention module and three parallel feature extraction branches, generating the final multi-modal fusion feature vector. Using the operating conditions as input and the physical variables as inputs, and the predicted physical quantities and risk level judgment results as outputs, a subway project risk level prediction model based on a physical information neural network is constructed. This method effectively integrates multi-source data related to subway projects, contributing to improved accuracy and practicality in disaster risk level prediction.

This invention discloses a laser welding method for ultra-narrow steel-shell batteries, comprising S1. incoming material preparation, S2. positioning and transfer, S3. laser welding parameter setting, S4. laser welding, S5. post-weld inspection, S6. sorting and traceability, and S7. fixture return. This method solves the technical problems of low welding precision, unstable weld quality, poor compatibility, and difficult traceability in ultra-narrow steel-shell battery casings. The method deeply integrates a high-precision magnetic levitation fixture return system, CCD vision-based precise positioning, differentiated laser welding modes, and a full-process MES traceability system. Specifically adapted to the structural characteristics of ultra-narrow steel shells, it achieves efficient and high-precision welding of the steel shell and casing through precise control of welding parameters and process coordination. This invention ensures that weld dimensional accuracy, penetration depth, and sealing meet requirements, enables full-process information traceability in welding, improves production efficiency and product qualification rate, and is suitable for automated mass production of ultra-narrow steel-shell batteries.

The invention discloses a traceability management method and system for industrial products, relates to the technical field of industrial product production management, establishes a comprehensive product traceability database, covers detailed records of product production, accessories production links and assembly processes; uses a semantic recognition model to deeply analyze historical product abnormal conditions, identifies abnormal state types and parameters, constructs a historical abnormal state parameter group, and establishes associations with abnormal accessories to form abnormal state-abnormal accessories association groups; classifies and aggregates these association groups and selects the best to construct an abnormal state-abnormal accessories corresponding library; uses the semantic recognition model again to identify and analyze the current product abnormal conditions, and quickly locks the abnormal accessories and their production links by matching the abnormal state-abnormal accessories corresponding library to achieve efficient traceability; the invention significantly improves the traceability efficiency and accuracy, reduces the traceability cost, and provides strong support for industrial product quality control.

The invention discloses a data sharing method and device based on a block chain, relates to the technical field of block chains, and mainly aims to solve the problem that existing data sharing cannot meet the sharing requirements of multi-party collaborative complex scenes such as combination of an industrial block chain, the Internet of Things and the industrial Internet. Comprising the steps that a multi-chain shared network structure matched with a target service scene is created, and the multi-chain shared network structure comprises a service chain, a service system and a cross-chain route; the method comprises the following steps: after a service system acquires original data, creating shared data and a cross-chain association structure of the original data, and executing uplink operation of a service chain on the shared data and the cross-chain association structure through cross-chain routing; and when the business system sends a query transaction of a target business chain through the cross-chain route, querying the target shared data and the cross-chain association structure corresponding to the target business chain, and querying the association shared data and the cross-chain association structure of the association business chain according to the target shared data and the cross-chain association structure.

The application discloses an intelligent perception-based Internet of Things traceability hierarchical architecture, and proposes a 'four horizontal and one vertical' architecture; the 'four horizontal' are a perception layer, a transmission layer, a traceability layer and an application layer from bottom to top; the perception layer is responsible for acquiring data required by traceability; the transmission layer is responsible for uploading the perceived traceability data to a trusted traceability platform; the traceability layer is responsible for storing traceability data and providing trusted traceability verification; and the application layer realizes traceability applications such as logistics tracking and quality control based on the trusted traceability result; the 'one vertical' runs through the perception, transmission and traceability layers and provides reliable guarantee mechanisms for the layers. Through analyzing existing Internet of Things, block chain and traceability systems and combining the characteristics of intelligent perception and traceability of the Internet of Things, the architecture realizes accurate and controllable intelligent perception, economical and reliable networking transmission and efficient and trusted traceability process.

The present disclosure provides a product digital passport updating method, device, equipment, medium and product. When an updating request is received, the target user and the change data are subjected to reliability verification, when the target user and the change data both pass the reliability verification, based on a passport version chain, a current version product digital passport is determined, based on the current version product digital passport and the change data, difference data of a new passport version node is determined, based on the last passport version node of the passport version chain and the difference data, a verification version digest and a current version digest of the new passport version node are determined, based on the current version digest, the verification version digest and the difference data, the new passport version node is generated, and the new passport version node is added to the end of the passport version chain. The incremental change between adjacent versions of the product digital passport is recorded in the form of difference data, avoiding repeated storage of full data, and significantly reducing data redundancy and network transmission burden.

The invention discloses a traceable privacy protection method and system for crowd sensing. The method comprises the following steps: step 1, initializing a system; 2, a data demander registers; step 3, establishing a task group; 4, submitting the collected data; and 5, tracing the user. According to the invention, the block chain technology and the group signature technology are combined, so that the problem of user privacy leakage in the traditional block chain traceability process is solved. The group signature allows the user to participate anonymously in the transaction, and the unforgeability and traceability of the signature are ensured at the same time. While the privacy of the user is protected, the malicious behavior can be effectively traced. When dispute occurs or auditing is needed, an auditing party can track the signature under the condition that privacy of other users is not leaked, and accuracy and fairness of source tracing are ensured.

The present invention belongs to the field of test reagent management technology, and specifically relates to the application of artificial intelligence technology in the conversion of test reagent benefits. The invention can monitor the use status of test reagents in real time by assigning consumable codes to test reagents, thereby realizing effective management and tracing of reagent information. During the use of test reagents, the fluctuation of reagents can be monitored and analyzed in real time, thereby tracing back to the cause of abnormal fluctuation of test reagents, providing corresponding data support for subsequent correction and optimization. At the same time, for abnormal states that occur, corresponding traceability processing can be performed, and refined to upper and lower limit abnormalities, so that the direction of abnormalities can be clarified, which is convenient for management personnel to investigate. It can also count the abnormal causes during abnormal periods and generate abnormal reports, which are helpful for management personnel to promptly discover and solve problems that arise during the inspection process and improve the inspection quality of subsequent test reagents.

The present invention relates to an angle and angular velocity calibration device and calibration method of a steering robot, which comprises: a base, which is equipped with an angle sensor; an adapter, which is used to connect with the steering robot, the adapter is connected with the angle sensor through a shaft, the adapter, the shaft and the angle sensor are coaxially arranged, and when the steering robot rotates, the steering robot drives the adapter and the shaft to rotate synchronously. Using the steering robot angle calibration device, the steering robot is fixed on the adapter, and the adapter is connected with the angle sensor through the shaft. When the steering robot rotates, the angle and angular velocity of the steering robot are calibrated through the angle sensor. The structure is simple, the calibration process is easy to operate and implement, and the effective traceability of the angle and angular velocity parameters of the steering robot is realized.

This invention discloses a method for marking tobacco material information, including controlling the tobacco material to be conveyed on the discharge bottom conveyor at a certain stacking height I; controlling the next batch of tobacco material to be conveyed on the discharge bottom conveyor at another stacking height II; and so on, until all batches of tobacco material are discharged; the stacking height I, stacking height II, etc., change according to a certain pattern as the batch quantity of tobacco material is updated; by knowing the current stacking height of the tobacco material on the discharge bottom conveyor and the pattern of stacking height change, the batch data of this tobacco material can be obtained. This method aims to improve the efficiency of tobacco material tracking, management and traceability characterized by assembly line operations. By adopting dynamic stacking marking technology, this method can monitor and update tobacco material information in real time, providing more accurate and reliable data for production and supply chain management.