45 results about "Process identification" patented technology

The invention provides an intelligent situation analysis and arrangement method and system, and the method comprises the steps: obtaining a user business analysis demand, extracting a core element, and constructing a four-layer neural network cooperation framework comprising a data layer, a processing layer, an analysis layer and a display layer agent; forward decomposing the service target to generate an initial task execution network, and adjusting through a reverse collaborative optimization algorithm to form an optimized service process; identifying risk interest points and generating risk situation data through a two-parameter risk model evaluation process containing a model and a data parameter vector; and generating an editing operation sequence based on the data, and performing non-autoregressive adjustment by using a continuous time Markov chain model to obtain a final scheme. According to the method, intelligent arrangement of the BI business process is realized, and the quality and efficiency of the process and the credibility of analysis results are improved.

The invention discloses an intelligent document approval method and system and terminal equipment, and belongs to the technical field of intelligent auditing, and the method comprises the steps: analyzing the layout structure of a to-be-approved document, and extracting a plurality of document information; according to the document information, performing process identification and approval classification on a to-be-approved document, and determining an approval classification result of the to-be-approved document; wherein the approval classification result comprises a business process and an approval decision; and transferring a to-be-approved document to an approver according to the business process, and monitoring an approval process of the approver based on the approval decision to obtain an approval result. Therefore, by implementing the method, the problem of low document approval efficiency in the prior art can be solved.

The embodiment of the invention provides a data management method and system for a product production process, equipment, a medium and a product, and the method comprises the steps: arranging a production process flow for a to-be-produced product model, the production process flow comprises at least one production process, and each production process corresponds to a unique process identification number; in the production process according to the production process flow, creating a product instance of the product model, the product instance corresponding to a unique product instance identification number; and obtaining production log data corresponding to each production process of the product instance, associating the production log data corresponding to the production process with the corresponding process identification number and the product instance identification number, and storing the production log data in the cloud. By using the method, the production log data generated by each process can be automatically stored in the cloud, and by establishing a complete product production process log recording and tracing system, the integration and sharing of the production data are realized, the information island phenomenon is avoided, and the production efficiency and the quality management level are improved.

The invention discloses an optimal heating process identification and control method for reducing surface decarburization of a wire rod, which comprises the following steps of: establishing a decarburization prediction model, acquiring actual production information from a production process control system, realizing decarburization prediction through the decarburization prediction model, and giving an optimal heating process interval of a current product. And finally, optimal heating process identification and control are realized. According to the method, the precision and stability of decarburization defect prediction are improved by taking field production big data and adopting a modeling thought of a local optimal interval and multi-model decision, and on the basis, the optimal control interval of key process parameters is determined, and field production management and control are carried out.

The invention provides a digital packaging and analyzing method for a structure-circuit-functional device integrated model, and relates to the field of additive manufacturing digital design. According to the method, basic semantic information including names, types and functions is added to an STL model of each component; the geometric data of each part under the local coordinate system is associated with structured JSON metadata containing semantics, pins and connection information; geometric data and metadata are packaged to generate a single. Scid format file, the file is analyzed, a geometry is rendered and reconstructed according to model type differentiation in a three-dimensional environment, metadata information is completely extracted and displayed in a log mode, and a two-dimensional connection relation matrix between devices is automatically constructed and visualized according to a pin connection relation. The method provides a complete and accurate data basis for subsequent integrated collaborative design, automatic process identification and path planning for multiple processes (such as an FDM printing structure, a silver paste jet printing circuit and a mechanical arm embedded device).

The application provides an Abaqus multivariable and multi-target fast optimization method and system based on a Bipod flexible support structure, and includes: five modules of Main_Optimizing.py, Sub_Parameter_Sensitivity_Analysis.py, Sub_Parameter_Range.py, Sub_Model_Bulider.py and Sub_Job_Submit.py work cooperatively, functions of coordinating a global process, identifying key design variables, determining an optimal search space, automatically generating a parameterized finite element model and realizing batch simulation calculation are realized, and therefore a finite element oriented multivariable and multi-target fast optimization is realized. The application can effectively coordinate conflicts among optimization targets, obtain a satisfactory parameter solution in a short time, solve problems of low efficiency of artificial experience parameter optimization and time-consuming and laborious large batch manual modeling, and is helpful to meet urgent needs of modern engineering fields for efficient and accurate structure optimization design.

This invention discloses a method and system for identifying process types based on neural networks and the introduction of domain terminology. It relates to the fields of construction engineering and natural language processing and machine learning, and includes: S1. Data acquisition, S2. Data processing, S3. Data quantification, S4. Model building, and S5. Process identification. This invention can accurately and quickly break down, reorganize, and classify scattered quality records, and achieve matching with corresponding specifications. This simplifies the steps for managers to find specifications during quality inspection, saves time, and thus assists in engineering quality management.

This invention discloses a real-time monitoring and optimization system for intelligent factory production processes, belonging to the field of intelligent factory production management. Specifically, it includes: acquiring process parameters and production action sequence data of the pipe fitting forming process using the Industrial Internet of Things (IIoT) and transmitting them to a data processing center; constructing a motion waste analysis model based on Markov chain theory and the motion sequence data; identifying motion waste by calculating state transition probabilities and steady-state distributions and comparing them with standard motion flows; and feeding the results back to the production management system; combining process parameters and motion waste identification results, the production management system uses queuing theory and operations research to construct a dynamic bottleneck station identification model, calculating the probability of each station becoming a bottleneck in real time, and determining the dynamic bottleneck station based on the amount of work-in-process inventory and production efficiency; and adjusting the production cycle based on the dynamic bottleneck station identification results to achieve real-time monitoring and optimization of the production process, thereby improving production efficiency and quality.

This invention discloses a method for detecting self-preferential ranking of platforms, including S1, market position determination; S2, ranking difference verification; S3, bias and defense verification; S4, manipulation behavior and damage evidence collection; and S5, comprehensive judgment and report issuance. This invention's method for detecting self-preferential ranking of platforms, through five core steps—market position determination, ranking difference verification, bias and defense verification, manipulation behavior and damage evidence collection, and comprehensive judgment and report issuance—forms a standardized and streamlined method for detecting self-preferential ranking. It solves the problem of the lack of a unified operating system in existing detection methods, providing directly applicable operational basis for anti-monopoly enforcement and platform supervision. It fills the gap in methods for pre-detection and in-process identification of algorithmic ranking behavior, and avoids subjectivity and bias in detection through process design, significantly improving the accuracy and rigor of identifying and judging self-preferential ranking behavior.

This application discloses a process identification method, apparatus, and storage medium, relating to the field of communication technology, for achieving accurate identification of processes to be detected. The method includes: acquiring behavioral data of the process to be detected; and based on the behavioral data, acquiring an identification result of the process to be detected; wherein the identification result of the process to be detected includes abnormal processes and non-abnormal processes.

The application discloses a process identification method and device, and relates to the computer application field.The method comprises the following steps: receiving a process identification instruction input by a user, wherein the process identification instruction comprises the identification of a program where the process is located; acquiring a plurality of process UIDs belonging to the program according to the identification of the program where the process is located; identifying each process UID of the plurality of process UIDs to obtain the running state of each process; and displaying the identification result to the user, wherein the identification result comprises the running state of each process.The application realizes simple, fast and accurate identification of the process and determination of the program where the process is located after the operating system is started.

The embodiment of the invention provides a flotation whole process bottleneck technology identification method and system, and the method can extract feasibility features, robustness features and sensitivity features from operation data after the operation data is obtained. And evaluation indexes such as a matching degree index and an adjustability index are calculated based on the extracted operation characteristics. A PI-PNN index qualitative prediction model based on prior model assistance is constructed according to operation data and domain knowledge, and index marginal contribution value calculation is performed on the flotation process based on the established prediction model, so that bottleneck process variables or bottleneck process combinations are identified. The method can effectively predict the qualitative influence of the process parameters on the flow quality indexes, reduces the cost generated in the actual field invalid debugging process, enables the transformation process to have a basis, and relieves the blindness of decision making.

The invention provides a process guarding method and device for a credential system and a readable storage medium. The method comprises the steps of obtaining current process information of a process in the credential system; based on the current process information, performing process identification by adopting a process identification method based on a dynamic health portrait, and determining whether the daemon process is abnormal or not; and if it is determined that the daemon process is abnormal, performing process recovery processing on the daemon process. According to the portrait-based process identification method, high-precision process identification can be realized, and the problem of invalid restarting caused by process misjudgment in the prior art is solved.

The invention discloses a memory access management method and device for data processing and a medium, and relates to the technical field of data processing, and the method comprises the steps: receiving a read-write access request sent by a processor, writing the read-write access request into an access request queue, carrying out the message disassembly and field analysis of the read-write access request, obtaining a request feature field, and storing the request feature field in a database; generating an access stream window parameter according to the process identifier in the request feature field; and acquiring congestion characterization data around the access request queue, executing congestion judgment to obtain a flow limiting control signal, updating access flow window parameters according to the flow limiting control signal, and setting an uncompleted request admission threshold, so that the access request queue forms a controlled request queue constrained by the uncompleted request admission threshold. According to the method, the scale of the access request queue is controlled through linkage of congestion judgment and the uncompleted request admission threshold, the access rhythm under concurrent reading and writing is controllable, uncompleted request accumulation and queuing delay fluctuation are reduced, and the overall throughput stability and operation reliability are improved.

The invention discloses a precast beam process identification method based on edge calculation, which comprises the following steps: acquiring a video stream of a precast beam production scene in real time through a multi-modal data acquisition unit, and preprocessing the video stream; inputting the preprocessed video stream into a deep learning model, and dynamically identifying a current process type through multi-scale feature extraction and multi-modal data fusion in combination with sensor data; and an identification result is converted into structured data of the production progress, wherein the structured data comprises a process state, progress analysis and quality early warning. The invention further discloses a precast beam process recognition device based on edge calculation, corresponding equipment and a storage medium. According to the precast beam process identification method based on edge calculation provided by the embodiment of the invention, through combination of edge calculation and a lightweight deep learning model, identification delay can be reduced, the robustness of process identification is enhanced, and high bandwidth occupation of cloud transmission and potential data safety hazards are avoided.

The invention discloses a business process risk dynamic identification system based on multi-modal data analysis, relates to the technical field of business process risk identification, and aims to solve the technical problem that full-process identification monitoring cannot be performed according to a business process front end, a business process middle end and a business process rear end in the prior art. Specifically, on the basis of multi-modal data acquisition, full-process dynamic identification is taken as a core, closed-loop management and control is taken as a target, information islands of capacity planning, production execution and supply chain delivery are broken through according to business process characteristics of the manufacturing industry, and a full-link risk monitoring system is constructed; and through quantitative index and trend analysis, the accuracy and foresight of risk identification are improved, a closed loop of identification-early warning-disposal is realized, landing measures such as equipment upgrading, process optimization and supply chain scheduling can be directly linked after a risk signal is triggered, and core risks such as capacity overload, equipment failure and supply chain delay in the manufacturing industry are effectively reduced.

To ensure product traceability in an environment where product manufacturing-related information is managed in a distributed manner across individual factories.SOLUTION: A production management system 10 comprises process management servers 1A to 1D, which share and manage a series of manufacturing processes 6, and a production control server 2, which sends an identification number issuance request signal 21 to the process management servers 1. When receiving the signal 21, the process management server 1C, which manages an intermediate stage in the manufacturing processes 6, receives a post-process identification number for a product from the process management server 1D that manages a subsequent process, issues a pre-process identification number for the product in association with the received post-process identification number, sends the issued pre-process identification number to the process management server 1B that manages the preceding process, and sends an identification number issuance completion signal 22 to the production control server 2. Based on the signal 22 received from the process management servers 1, the production control server 2 determines whether or not to send a production start signal 23 to the process management servers 1.SELECTED DRAWING: Figure 2

The invention provides a large model feedback optimization system and method based on process identification and tool calling. The method comprises the following steps: S1, converting received input data into semantic vectors and inputting the semantic vectors into a large model; s2, enabling the large model to output an intention label, and carrying out semantic matching on the intention label and a preset business flow chart; s3, according to a semantic matching result and a task node sequence of the flow chart, gradually scheduling and executing a corresponding tool function; s4, recording the behavior of each time of tool calling, and forming a tool calling track chain; s5, evaluating the rationality and correctness of the tool calling track chain based on a reward model, and outputting an evaluation result; and S6, finishing fine adjustment of the main model according to the evaluation result. According to the method, the user intention can be automatically identified, the tool call chain can be called, the model is optimized in combination with the execution result and the evaluation result of the reward model, and illusion problems such as false call and assume execution of a large model are effectively reduced.

The embodiment of the application belongs to the field of development assistance, is applied to the field of automatic generation of process documents, and relates to a process document automatic generation method and device, computer equipment and a storage medium, which comprises the following steps: obtaining page basic data based on a vue analysis tool; determining data to be converted into an abstract syntax tree structure in js script content by using a babel parser; generating an abstract syntax tree; outputting a markdown format file based on the abstract syntax tree and a markdown file generator, obtaining comment information in the js script content, and outputting the comment information into the markdown format file; converting the markdown format file into an HTML format file based on CSS style content, and completing automatic generation of the process document. The logic of the corresponding front-end page can be quickly and accurately viewed through the finally generated process document, process identification can be facilitated, and the learning cost of new employees for page processes can be greatly reduced through the comment information.

The present application relates to the technical field of process monitoring in program dynamic analysis, and provides a software and hardware cooperative process identification and monitoring method, device, equipment and medium, which comprises the following steps: adding extended instructions adaptive to process creation, process covering and process exit in the execution functions of process creation, process covering and process exit; modifying the execution mechanism of the CPU according to the extended instructions, adding an extended register for process identification and monitoring in the CPU, and adding the analysis and execution functions of the extended instructions, so that the CPU analyzes and executes the extended instructions, and stores process record information in the buffer area allocated by the operating system in advance for interaction; when the operating system executes the instruction of storing the process record information of the buffer area into the special register of the CPU, the CPU completes various transfer buffer area information and register information operations based on the extended execution mechanism. The present application improves the transparency of the analysis system.

The invention discloses an operation instruction book automatic generation method based on a database and an automatic script, and relates to the technical field of manufacturing industry informatization. The method comprises the following steps: a logic processing engine module receives a product code and associatively queries product information, a template and a logic rule in a database; the process steps and the specific operation data are matched, and a structured local data object is assembled; calling the document template, and automatically filling operation flows and contents according to logic rules to generate a workbook; automatically retrieving and loading a bill of material, an operation picture and a special process identification picture according to the process name, and generating a final file after the format is adjusted. According to the method, through full-automatic closed-loop operation, the problems that manual compiling is low in efficiency, errors are prone to occurring and formats are not uniform are solved, and granulated storage of process data and standardized output of documents are achieved.

This invention discloses a superheat control method and system for suppressing superheat oscillations. The method includes: a process identification module identifying the dynamic characteristics of the superheat control system through a transfer function; a predictive control module constructing a state-space model based on the dynamic characteristics, and performing state prediction based on state estimates and disturbance estimates, outputting a state prediction vector and calculating a first objective function; a parameter identification and self-tuning module self-tuning the PID control parameters based on the dynamic characteristics and a second objective function; a PID control module outputting an electronic expansion valve drive signal based on the deviation between the predicted superheat value in the state prediction vector and the preset superheat target value, and the tuned PID control parameters; and an electronic expansion valve driver driving the electronic expansion valve to adjust the refrigerant flow rate according to the electronic expansion valve drive signal. The technical solution provided by this invention significantly suppresses superheat oscillations caused by the refrigeration system operating at low superheat, improving system stability.

The invention discloses an automatic fracturing operation method based on flow identification driving. The method comprises the following steps that the fracturing construction process is divided into standardized processes, and corresponding identification symbols are set for the standardized processes to obtain process identifiers; constructing a pump injection plan table of fracturing construction operation; calling a control algorithm module bound with the flow identification according to the flow identification sequence to automatically start a corresponding fracturing construction flow, collecting real-time fracturing construction data to calculate a stage parameter accumulated value, and setting a jump condition according to the stage parameter accumulated value and a stage parameter threshold value of each flow identification, and automatically switching to a flow corresponding to the next flow identifier until the pump injection schedule of the fracturing construction operation is executed. According to the method, the whole process is driven to be automatically executed through digital identification, the problems of response lag and low standardization degree caused by the fact that traditional fracturing operation depends on artificial experience are solved, and then the construction efficiency and safety are remarkably improved.

The invention provides an anti-fraud processing method and device, a computer program product and an anti-fraud system.The method comprises the steps that under the condition that a transaction request is obtained, service identification and in-event fraud identification are conducted on the transaction request, and a service identification result and an in-event identification result are obtained; refusing the transaction request under the condition that at least one of the business identification result characterizing that the verification is not passed and the in-event identification result characterizing that the verification is not passed is satisfied; executing the transaction request under the condition that the business identification result characterization passes the verification and the in-event identification result characterization passes the verification; and storing the transaction request obtained this time, the business identification result corresponding to the transaction request and the in-event identification result corresponding to the transaction request, and performing post-event fraud identification on all the first historical transaction requests in the first historical time period to obtain a post-event identification result. According to the method, the problem that anti-fraud identification is static and poor in flexibility in the prior art is solved.

A processor of the quality monitoring device is capable of executing a monitoring process for monitoring quality using a plurality of state prediction models for predicting the state of the liquid related to the quality using a spectral spectrum acquired online from the liquid generated in the manufacturing process as input data, and a model switching process for switching the state of the liquid related to the quality using a plurality of state prediction models for predicting the state of the liquid related to the quality as input data. According to process management information for managing at least one process included in the manufacturing process, the state prediction models are switched midway in the monitoring process, and the plurality of state prediction models are learned before the monitoring process starts. The process management information includes at least one of process identification information, use equipment information, and process quality information.

The invention discloses an identification method and system for a carrier tape packaging machine, and the method comprises the steps: obtaining a working principle of the carrier tape packaging machine, determining a plurality of working process identification elements according to the working principle, and obtaining an identification mechanism of each working process identification element; determining identification logic and an identification analysis mechanism of each working process identification element according to the identification mechanism, and selecting identification equipment specifications and identification equipment mode configuration parameters based on the identification logic and the identification analysis mechanism; target identification equipment is selected based on the identification equipment specification and the identification equipment mode configuration parameters, and the working process of the carrier tape packaging machine is identified and identified; and determining working defect parameters according to an identification result, and performing feedback control based on the working defect parameters through a preset defect judgment and linkage control model. High-matching-degree identification work can be carried out according to the working characteristics of different working components through multiple types of identification devices, and the identification and detection stability and reliability and the working efficiency are improved.

The invention provides a tunnel engineering construction process data automatic extraction method and system and electronic equipment. The method comprises the following steps: acquiring a digital audio signal of a construction site acquired by an audio sensor; preprocessing the digital audio signal to extract audio features of the digital audio signal; inputting the audio features into a pre-trained construction scene classification model to obtain a construction scene classification result, and recording the starting and ending time of recognition of each construction scene; wherein each construction scene corresponds to one working procedure; and based on the construction scene classification result and the starting and ending time of each construction scene recognition, determining a process type and the starting and ending time of each process. According to the method, the accuracy of process identification is greatly improved, the process is automatically identified through the pre-trained construction scene classification model, and the efficiency of process identification is improved.

The application provides a kind of multi-modal industrial process identification and monitoring method based on hierarchical fluctuation window, it is related to process monitoring technical field.Firstly, Gaussian mixture model is used to roughly identify modal information;Then, hierarchical fluctuation window strategy is used to accurately distinguish stable mode and transition mode, in hierarchical fluctuation window, it contains initial level and terminal level two parts;In initial level, sample field characteristics are divided by sliding window, and key window is selected and transported to terminal level, next, variable fluctuation concept is introduced and divided by sliding window to accurately divide the starting point and end point of transition mode.Then, stable mode is monitored by GMM model based on Mahalanobis distance;Finally, a new neighborhood shift model is established to monitor transition mode.This method can accurately divide the mode from the sample and variable angles, respectively, for stable mode and transition mode, to establish appropriate monitoring model, reduce monitoring complexity and improve the process fault detection rate.

A steelmaking process monitoring system, characterized in that it comprises an adaptive module, a process monitoring module, a composite recording module, an adjustment module, a feedback module and a process optimization module, wherein the process monitoring module comprises a first sensing module group and a second sensing module group; the adaptive module is used to record the steelmaking process flow; the first sensing module group comprises various gas sensors; the second sensing module group comprises various environmental sensors; the composite recording module is used to record and analyze the data recorded by the first sensing module group; the process optimization module is used to optimize the process according to the concentration change curve; the adjustment module is used to adjust the sensitivity of the second sensing module group; the feedback module is used to generate improvement strategies for the production process flow; by setting the adaptive module and integrating the process identification database, the flexibility and application range of the system are significantly improved, and the system is suitable for rapid switching and deployment of various steelmaking process flows.

The application discloses an AI-driven construction process recommendation and mixed reasoning optimization method and system. The method comprises the following steps: receiving project characteristic input, loading expert rule template; taking the project characteristics and the expert rules as the context, calling a large language model to output structured process network data, including process identification, construction period, preposition relationship type, resource demand reference and construction process label; cross-verification checking the integrity of the LLM generation result and the expert rules; supplementing auxiliary processes from the process template library to form a candidate scheme pool; taking the candidate process network as the input, constructing a four-dimensional target model for simultaneously optimizing the minimization of the total construction period, the minimization of the artificial load balance degree, the minimization of the material daily supply pressure and the maximization of the mechanical utilization rate, and solving a Pareto optimal scheme set. The application integrates the LLM structured process network generation, the expert rule mixed reasoning and the multi-dimensional resource optimization into an integrated process, so that the AI-assisted scheme preparation is upgraded from a text suggestion to a deducible process network.