48 results about "Batch Identifier" patented technology

The invention provides a food information tracing method and system based on Internet data, and relates to the technical field of industrial Internet, and the method comprises the following steps: generating an initial batch identifier of a to-be-produced product according to a product production plan, obtaining real-time external data of an associated region, and storing the real-time external data in a database; when it is judged that a batch adjustment event occurs in the production process of the to-be-produced product, based on real-time state data and real-time external data of a production line, dynamic batch decision is carried out to generate at least two sub-batch identifiers, and a tree-shaped association relationship is established between the sub-batch identifiers and the initial batch identifier; and receiving sub-batch detailed data of each sub-batch identifier, uploading the sub-batch detailed data to a block chain network, storing the batch topological relation in a block chain main chain, storing the sub-batch detailed data in a corresponding sub-chain node, responding to a tracing request of a user terminal, and according to the tree-shaped association relation, tracing the sub-batch data of each sub-batch identifier. And retrieving the full life cycle data of the corresponding target batch in the block chain network. The method overcomes the problem of tracing information splitting caused by batch splitting.

The embodiment of the invention discloses a multi-source data cleaning method, electronic equipment, a storage medium and a product, and relates to the technical field of industrial big data and semiconductor manufacturing. The method comprises the following steps: acquiring a device layer data stream and a system layer data stream in real time, generating a device-level space-time identifier for the device layer data stream, and generating a logic batch identifier for the system layer data stream; aligning a millisecond-level event of the equipment layer data stream with a second-level event of the system layer data stream, and generating a calibration timestamp chain; generating a three-dimensional fingerprint based on the calibration timestamp chain, and binding the three-dimensional fingerprint with the cleaning data to obtain first cleaning data; and performing time sequence compliance verification on the first cleaning data based on the dependency graph, generating a time-space conflict event chain for verified violation data, performing abnormity self-repairing to obtain repaired target cleaning data, and performing hierarchical storage on the target cleaning data based on the three-dimensional fingerprint, thereby realizing collaborative cleaning of data of different data sources.

The invention discloses a system and a method for producing a glue injection industrial brush by using an injection molding machine, and relates to the technical field of industrial brush processing, a multi-parameter coupling index is generated according to acquired working parameters, and the working parameters, the multi-parameter coupling index and a batch identifier in a corresponding time period are bound; the method comprises the following steps: performing multi-angle imaging on brushes, adding mark information sections, performing preprocessing, extracting candidate areas which may have defects, after detecting a plurality of defect areas, constructing a morphological synergy index to quantify the overall defect degree, and obtaining defect data of each brush; after a comprehensive index structure is constructed, an injection molding process feature vector is constructed, kernel mapping and an AWCCA method are utilized to calculate nonlinear correlation and local sensitivity between each working parameter and a form cooperation index, a working parameter interval is formulated, and a parameter modification suggestion is generated; in the production process, parameters are continuously corrected in a small-stride mode, the defect degree of the brush and the stability of the injection molding process are balanced, and more flexible regulation and control are achieved.

The invention relates to the technical field of quality management, in particular to a stamping hardware quality management method and system. Obtaining input production batch information and production event context information; acquiring quality data uploaded by the automatic detection equipment and associated detection context information, wherein the detection context information comprises detection time, a detection equipment identifier and a batch identifier; performing multi-dimensional consistency evaluation based on the production batch information, the production event context information and the detection context information; according to a result of the multi-dimensional consistency evaluation, performing association judgment and establishing an association relationship between the quality data and the production batch information; and marking the quality data which cannot establish the association relationship through association judgment, and prompting workers to check. The technical problem that in large-scale production of stamped hardware, due to the fact that batch information input is inconsistent or wrong, association between quality data and actual production batches is disordered is solved.

The invention provides a funnel data generation method, device and system and computer equipment, and relates to the technical field of data processing. The funnel data generation method comprises the steps that batch identification is added to content pushed in batches, the corresponding relation between the batch identification and content identification is recorded to content exposure data, the batch identification corresponds to the batch, and the content identification corresponds to the content; obtaining user behavior data, the user behavior data at least comprising a corresponding relationship between a behavior identifier and a content identifier, and the behavior identifier being used for identifying an interactive behavior of the content; based on the batch identification and the behavior identification, the content exposure data and the user behavior data are integrated to generate funnel data. Funnel data conforming to the actual user loss condition can be generated, and the analysis requirement of the funnel data is met.

The application relates to a factory data management method, system, terminal and storage medium, and relates to the field of data management.The method comprises the following steps: receiving an original data stream; performing timestamp alignment processing on the original data stream to obtain a standardized data sequence; extracting a production batch identifier and a process identifier in the standardized data sequence; generating a process sorting sequence corresponding to the production batch identifier based on the process identifier and a process sequence; grouping data items belonging to the same production batch identifier in the standardized data sequence according to the process sequence, and outputting process-level data records corresponding to the production batch identifier; extracting process parameters having a transmission relationship between adjacent processes, and calculating deviation values between the process parameters; when the deviation value exceeds a deviation threshold value, determining a parameter correction amount from a rule library according to a process position and a parameter deviation direction, and correcting the process parameters of an abnormal process node. The application has the effects of improving production operation efficiency and guaranteeing product quality.

The application discloses an Internet of Things multi-source data edge computing processing system and method in the technical field of Internet of Things data processing, and relates to the technical field of edge computing.The system comprises a centralized production scheduling unit that continuously monitors and analyzes production plans, identifies processing tasks to be started in a future preset time period, and extracts material batch identifiers corresponding to the processing tasks.Based on the material batch identifiers, simplified key background information matching the processing tasks is extracted from a global background information database.The simplified key background information is the minimum data set required for edge side anomaly detection.The application simultaneously solves the problem of misjudgment caused by the lack of global background information on the edge side and the problem of communication delay caused by real-time request information, significantly improves detection accuracy on the premise of ensuring millisecond-level response capability, and avoids redundant data transmission and storage overhead.

The invention provides a semiconductor manufacturing equipment scheduling method and device, electronic equipment and a storage medium, and relates to the technical field of semiconductors. The method comprises the following steps: acquiring a machine scheduling request; determining a next process and a plurality of available candidate machines meeting the requirements of the next process according to the wafer batch identifier and the current process identifier; according to the machine identifier of the current process, multi-dimensional scheduling information of each available candidate machine is obtained, and the multi-dimensional scheduling information at least comprises distance information, state information and load information; and according to the multi-dimensional scheduling information of each candidate machine, determining a target machine in the plurality of available candidate machines, and executing the next process through the target machine. According to the method, the scheduling score of each candidate machine is comprehensively evaluated by fusing three key dimensions including distance, state and load, accurate modeling and quick response to a complex dynamic environment in a semiconductor production line are realized, the production cycle is remarkably shortened, and the equipment utilization rate is improved.

The invention provides a data processing method and device for an AI application, equipment, a medium and a program product, and relates to the technical field of data processing.The method comprises the steps that a training data set is read from storage equipment according to a first data loading instruction sent by a CPU; performing data enhancement on data in the training data set, generating to-be-trained data corresponding to the batch identifier, and writing the to-be-trained data into a first data buffer area in the GPU; obtaining model intermediate state information written by the GPU from a second data buffer area of the DPU; and obtaining model data obtained by final training and written by the GPU from the second data buffer area. By introducing the DPU into the artificial intelligence server, necessary operations in artificial intelligence model training can be unloaded from the CPU of the artificial intelligence server to the DPU to be executed, so that processing resources of the CPU are released, processing time delay is reduced, execution efficiency of the CPU is improved, and data processing efficiency of an AI application is improved.

The invention discloses a data verification method and device based on a block chain, equipment and a medium. A leaf hash value is calculated for any batch of source data sub-files of the data storage node, a Merkel root hash value and a corresponding Merkel proof path are generated, and a batch identifier, a data storage address, the Merkel root hash value and a generation timestamp are synchronized to a data index node through a log of a block chain technology; a data index node can obtain a Merkel proof path corresponding to a corresponding batch identifier from a data storage node, based on this, a verification client side calculates a local hash value for any target source data sub-file of a target batch identifier needing to be verified, and verifies the target source data sub-file according to a target Merkel proof path obtained from the data index node. And reconstructing the to-be-confirmed root hash value, comparing the to-be-confirmed root hash value with the target Merkel root hash value, and verifying the target source data sub-file of the target batch. And the authenticity and credibility of the data are ensured.

The invention provides a flow cutting method, device and equipment for batch data, a medium and a program product, and can be applied to the technical field of big data and the technical field of financial science and technology. The method comprises the following steps: adjusting a host time zone corresponding to host batch data from a host into a current system time zone of the distributed system to obtain system batch data corresponding to the host batch data; under the condition that a historical batch identifier matched with the batch identifier exists in the historical batch identifiers of at least one piece of historical system batch data, performing validity analysis on the system batch data according to the target historical system batch data and the expected effective time period; and under the condition that the validity analysis result represents that the system batch data is not matched with the target historical system batch data and the batch moment is in the expected effective time period, updating the target historical system batch data based on the batch data included in the system batch data so as to realize the stream cutting operation of the batch data.

The invention provides a production process control method and device, electronic equipment and a readable storage medium, and can be applied to the technical field of computer technology and production and manufacturing. The production process control method comprises the steps of receiving a production batch identifier of a product, and obtaining site strategy information according to the production batch identifier, the site strategy information comprising at least one site identifier having an association relationship and a strategy identifier corresponding to the at least one site identifier; in response to the detection that a page jump button in the first-level page is triggered, a second-level page corresponding to a target strategy identifier in the at least one strategy identifier is displayed, and the page jump button is generated according to a target site identifier in the at least one site identifier; and in response to the received input information from the second level page, executing production process control of the product according to the input information.

This disclosure provides a method for optimizing temperature control during the baking process of seals in injection molding machines, including: generating batch identifiers based on batch information output by the injection molding machine and establishing a baking process archive; setting target material state and consistency judgment thresholds; collecting oven cavity temperature data, seal surface temperature data, and oven cavity gas characteristic data to form a process data stream after cleaning and calculating a set of state features; inputting the set of state features into a material state estimation model to obtain material state indicators and state compliance, and calculating consistency deviation to generate a set of control feedback quantities; under the constraints of maximum allowable temperature, maximum heating rate, zone temperature difference, and anti-over-baking, using a wolf pack algorithm to optimize and solve the temperature control execution quantities and perform closed-loop iterative updates; when the state compliance meets the target material state, the consistency deviation does not exceed the consistency judgment threshold, and the anti-over-baking constraint is not triggered, executing termination control and archiving the temperature control strategy sequence.

A system for monitoring distribution of takeaway boxes is provided. System includes a processor, and a memory in communication to the processor for storing instructions executable by the processor, such that the processor is configured to receive at least one batch identifier identifying at least one batch of takeaway boxes transmitted from a mobile device, retrieve a batch quantity of the at least one batch of takeaway boxes based on the at least one batch identifier, receive at least one outlet identifier identifying at least one food outlet that receives the takeaway boxes transmitted from the mobile device, receive location data transmitted from the mobile device or retrieving location data of the at least one food outlet based on the outlet identifier, generate a distributed quantity of the takeaway boxes based on the quantity of the at least one batch of takeaway boxes and the location data of the at least one food outlet, such that the processor is configured to monitor the distribution of the takeaway boxes to the at least one food outlet based on the distributed quantity. Further, a method for monitoring distribution of takeaway boxes is provided.

The present application relates to the technical field of fuel dispenser data management, in particular to a fuel dispenser remote data management method and system based on cloud communication. A site terminal generates batch data blocks by time threshold packet, performs reversible compression and anchor point insertion, and generates batch metadata with a hash tree root value as a batch identifier; a secure session is established with the cloud when online, and transactions are uploaded in priority; when offline, data blocks and metadata are spouted into optical frame columns, broadcasted by a price display screen, collected and decoded by a mobile terminal video, and uploaded. The cloud restores data blocks based on reconstruction fragments and metadata, verifies integrity with frame-level checking and batch identifier verification, and writes transaction records and running status into a transaction library and a timing library respectively with site identifier and batch identifier idempotent deduplication, generates a reply short code to synchronize the site terminal; after network recovery, the batch is supplemented according to the missing list to realize consistent reconciliation.

This invention belongs to the field of construction quality control and information technology for water conservancy and hydropower projects, specifically relating to a quality traceability system and method for asphalt panels in pumped storage power stations based on strip identification. It addresses the industry pain point of "data silos" formed by isolated storage of data at various construction stages, and the fragmentation of quality information leading to difficulties in fully tracing the overall quality history of the project. Key technical points: The system includes: a data acquisition module for collecting process data from each stage; an identification generation and binding module for dynamically and logically binding mixing batch identifiers, transport vehicle tags, and construction strip identifiers to the corresponding process data collected at each stage; a data integration and storage module for establishing a construction process-wide associated database with the construction strip identifiers as the core association key; a quality traceability analysis module for performing at least one traceability analysis based on the associated database; and a visualization query and output module for visually displaying the traceability analysis results.

The invention relates to the technical field of industrial internet and intelligent manufacturing, and discloses a rubber sealing ring batch tracking and process control optimization system based on space-time mapping, which comprises an internet-of-things sensing subsystem, an edge transmission subsystem and a central data processing subsystem. The sensing subsystem is used for acquiring a carrier identification code, weight and process parameters; the central subsystem generates a virtual batch identifier by using the carrier code and the timestamp through a space-time mapping module. A specific mass conservation verification module of the system calculates the weight deviation of fed and discharged materials in the circulation process, the data validity is judged by comparing the weight deviation with a preset threshold value, a process optimization module only extracts verified effective data, a correlation model of process parameters and quality data is established, and machining equipment parameters are automatically corrected through a closed-loop feedback unit. According to the method, the physical conservation law is utilized to constrain digital association, the problems of data link breakage and distortion caused by coexistence of discrete and mixed processes are solved, and accurate tracing and automatic process optimization of the whole production process are realized.

The application provides a MES system operation data flow asynchronous processing method, and belongs to the technical field of MES systems.The application collects material flow data by deploying multilevel sensors and distributes globally unique batch identifiers, calculates Lyapunov exponents, correlation dimensions and Kolmogorov entropy to identify and isolate abnormalities by using a phase space reconstruction anomaly detection module, calculates an entropy increase index for normal data and optimizes resource allocation by a double-layer game scheduling model, dynamically adjusts a back pressure threshold according to a resource allocation scheme and starts a fuse degradation strategy, performs cross-batch correlation analysis and corrects timing through a delay compensation mechanism, and adaptively adjusts a processing mode according to risk assessment parameters, thereby solving the technical problem of system stability decline caused by abnormal data propagation and pollution of downstream processing results in the asynchronous processing of batching operation data flow.

The application provides a data management method and a data management system for thin film ion recombination, comprising: storing production process data corresponding to a target batch to a structured database; in executing the production process data corresponding to the target batch, collecting corresponding control data in real time, and based on a multi-process parallel processing mechanism and a batch identifier corresponding to the target batch, storing the control data to an unstructured database in parallel; in response to an analysis request for the target batch, based on the multi-process parallel processing mechanism and the batch identifier, reading the production process data from the structured database and reading the control data from the unstructured database in parallel; and generating a visual result corresponding to the thin film ion recombination based on the production process data and the control data. In this way, data management can be converted into an automated and efficient operation, thereby improving data processing efficiency and providing accurate and real-time interactive visual data support for production managers.

This invention relates to the field of intelligent manufacturing data management technology and discloses a blockchain-based SMT production data traceability method. The method includes cleaning and standardizing heterogeneous data from the SMT production line, assigning identifiers to batches, and submitting the data for blockchain consensus verification. After verification, on-chain feature extraction is performed to generate data feature vectors containing key process parameters and quality indicators. These vectors are then bound to batch identifiers and stored in a blockchain Merkle tree. When traceability is required, the production health status is analyzed by retrieving the feature vectors of specific batches and performing pattern recognition. Once an anomaly is detected, the depth and scope of traceability are dynamically initiated and precisely customized based on the anomaly pattern characteristics. This method combines the tamper-proof characteristics of blockchain with intelligent data analysis, solving the problems of high data storage pressure and low traceability efficiency in traditional methods, and achieving efficient and accurate intelligent traceability.

The application relates to the technical field of data processing, in particular to a formation and component data processing method and device, electronic equipment and a storage medium, unconfirmed formation and component batch data is pulled from a cache queue to generate a globally unique batch identifier; data is formatted and written into an application buffer, and a pre-write log containing a batch identifier, a file path and a data offset range is synchronously recorded. The application actively performs a forced disk flushing operation to persist cache data to a physical disk, updates and solidifies a batch persistent state to a local confirmation state table after successful disk flushing, and finally sends a batch confirmation message to the cache queue to complete queue data marking or cleaning. The application discards the traditional early confirmation mechanism and delays the consumption confirmation node until the completion of data physical disk landing, solves the problem that data loss and poor integrity are easily caused by asynchronous disk flushing in the prior art, and effectively improves the reliability and safety of high-frequency massive data processing of lithium battery formation and component.

This invention provides a method and system for full-chain information traceability management of traditional Chinese medicine (TCM). Using TCM batch identifiers and quality control standard hashes as seeds, a shared RSA modulus N is securely generated among n nodes through an anti-collusion protocol. For the current block, based on the information from the previous block and node risk data, a dynamic delay parameter T is calculated via nonlinear mapping. The delay output of the previous block and the Merkle root hash of the current block are used as input x to generate the delay output y and proof π of the current block. π is divided using erasure coding and distributed to regulatory nodes along with y. After the nodes verify the validity of the shards, a certificateless threshold signature algorithm is used to generate a partial signature on the data digest containing y and the current Merkle root hash. When a preset threshold of t valid partial signatures are collected, they are aggregated to generate a complete block signature. The complete block signature, along with y and π, is recorded into the current block to complete the on-chain process.

The invention provides a data processing method and system for a virtual EAP in a semiconductor EAP system and electronic equipment, and the method comprises the steps: receiving a pull-in instruction for target equipment, and transmitting a pull-in request carrying a current batch identifier to an MES, so as to establish the operation association between the current batch and the target equipment in the MES; in response to a received data acquisition instruction, scanning a preset output directory of target equipment, and analyzing a target result file under the output directory according to an analysis rule matched with the target equipment to extract measurement data; sending the measurement data to the MES, and receiving a quality judgment result fed back by the MES based on a preset specification limit; according to the quality judgment result, configuring an executable state of the outbound operation control; wherein if the quality judgment result indicates that the quality judgment result does not pass, the outbound operation control is set to be in a locked state, the current batch is prevented from flowing to the next station, and therefore closed-loop quality control over the non-standard machine is achieved.

A system and method for monitoring the operating status and optimizing the production process of fuel cells based on multi-source data fusion is disclosed. By assigning a unique batch identifier to each fuel cell unit, production process data generated during the production phase is continuously correlated with operational data collected during the fuel cell operation phase, constructing a multi-source data set covering the entire fuel cell lifecycle. The operational data is preprocessed, and status indicators reflecting long-term performance changes in the fuel cell are extracted to generate operating status monitoring results. Based on these results, sensitivity analysis and optimization of production process parameters are performed, and the optimization results are fed back to the production system to adjust the production of subsequent batches of fuel cells, forming a closed loop of operating status monitoring and production process optimization. This system can improve the consistency, reliability, and safety of fuel cell products under complex operating conditions.

The present disclosure provides a message processing method, device and electronic equipment for internet medical treatment. The method is applied to a client and includes: obtaining an order event; generating an original business message according to the order event, and sending the original business message to a message platform, wherein the original business message includes a business scenario value, a message batch identifier, a message batch sequence number and a message display state; receiving a time sequence business message returned by the message platform, wherein the time sequence business message is generated by the message platform receiving original business messages of multiple clients and processing the original business messages according to the message batch identifier, the message batch sequence number and the message display state; and displaying the time sequence business message according to the message batch identifier, the message batch sequence number and the message display state of the time sequence business message. The message display of the instant messaging system of the multiple clients has orderliness.

This invention discloses a multi-node anomaly detection method for an intelligent centralized management and control platform, relating to the field of intelligent process automation centralized management and control technology. This invention generates stage tokens for business objects and binds these stage tokens to a business stage-execution node association graph, thus associating the processing states of multiple execution nodes and business stages within the same business batch under the same batch identifier. The platform generates anomaly observation sets based on stage token migration status, execution token distribution, stage state duration, and anomaly fingerprints. When multiple business stage nodes simultaneously have anomaly observation sets, it filters business stage nodes with token dependencies based on the directed flow relationship of the current process template version, thereby determining the anomaly source stage, affected stage, and anomaly attribution category. This allows the stage stagnation state in batch concurrent processes to be attributed to the corresponding business stage and corresponding execution node, forming a multi-node anomaly detection result associated with the business object.

The embodiment of the invention provides an equipment processing method, device and equipment, in the method, production equipment acquires a target batch identifier corresponding to equipment to be processed, and sends the target batch identifier to control equipment; the production equipment can also receive a target formula identifier corresponding to the to-be-processed equipment sent by the control equipment; determining target processing parameters of the to-be-processed equipment according to the target formula identifier; and processing the to-be-processed equipment based on the processing parameters to obtain target equipment. In the above process, the production equipment can interact with the control equipment to determine the target formula identifier of the to-be-processed equipment based on the obtained target batch identifier of the to-be-processed equipment, and accurately determine the target processing parameter of the to-be-processed equipment based on the target formula identifier; and the accuracy of configuring the target processing parameters for the to-be-processed equipment by the production equipment is improved.

The invention relates to the technical field of air conditioner upgrading, and provides an encrypted OTA upgrading method for an air conditioner controller, which comprises the following steps: generating model keys corresponding to different model identifiers, constructing a multi-level intermediate key corresponding to different area identifiers and upgrading batch identifiers, pre-encrypting an original firmware package layer by layer, and updating the firmware package layer by layer; generating batch-level encrypted packets stored in a classified manner; receiving an upgrade request carrying equipment identity information, matching a target batch-level encrypted packet, issuing a dynamic revocation list abstract, generating a unique session key of a single session based on joint negotiation of an equipment random number and a cloud random number, performing block encryption on the batch-level encrypted packet, and issuing the encrypted packet; decrypting the received encrypted blocks layer by layer by using the session key and a locally stored key of a corresponding level, and restoring to obtain original firmware blocks; and writing the restored firmware into a backup partition, and executing partition switching update after determining that the new firmware has a starting condition.

The invention relates to the field of data processing and intelligent manufacturing of the Internet of Things, and provides an intelligent production workshop data acquisition and processing method based on the Internet of Things. The platform registers an equipment baseline list of a station identifier, a gateway identifier, an event code field, a batch identifier field and a timestamp resolution field and gates an acquisition channel; a gateway interacts with a reference clock service according to a time synchronization period to form a time synchronization sample, a platform fits an affine correction model to generate a correction timestamp, and clock effective and non-anchor point marks are generated according to a residual threshold; the platform generates time uncertainty according to round-trip delay, queue waiting delay and timestamp resolution, and generates station internal order relation constraints based on event code mapping; the platform divides alignment windows according to correction timestamps and batch identifications, selects anchor points according to time uncertainty ranking, resolutions conflicts in combination with sequence relation constraints, and outputs cross-station alignment timelines and alignment reason codes; and through semantic dimension verification, drift rollback, quality gating and evidence chain packaging, the traceability chain can be rechecked.

The invention relates to a load balancing method and system for distributed deep learning training, and the method comprises the steps: analyzing the head information of a data packet in a switch, extracting a gradient index corresponding to the data packet, and dividing the data packets of the same gradient index from different training nodes into the same batch according to the gradient index; constructing a candidate path table based on the real-time queue length of the switch; distributing the same transmission path for the data packets in the same batch based on the Hash operation according to the candidate path table; the data packets in the same batch are continuously transmitted to the receiving end through the same transmission path; and carrying out instant aggregation on the received data packets through a receiving end according to the batch identifiers and the gradient indexes. The same batch of gradient data packets are ensured to continuously arrive at a receiving end through path selection based on gradient indexes and a transmission mechanism irrelevant to a sequence, so that instant aggregation and rapid synchronization of the gradient data packets are realized, and meanwhile, the utilization rate of network resources is improved.