30 results about "Process identification" patented technology

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 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 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 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.

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 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.

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.

The embodiment of the application provides a flotation full-process bottleneck process identification method and system. After operation data is acquired, the method can extract feasibility features, robustness features and sensitivity features from the operation data. Then, evaluation indexes such as a matching degree index and an adjustability index are calculated based on the extracted operation features. Then, a PI-PNN index qualitative prediction model based on a prior model is constructed according to the operation data and domain knowledge, and a bottleneck process variable or a bottleneck process combination is identified by calculating an index marginal contribution value of the flotation process based on the established prediction model. The method can effectively predict the qualitative influence of the process parameters on the process quality index, reduce the cost generated in the actual field invalid debugging process, make the improvement process have a basis, and reduce the blindness of the solution.

The application provides a kind of intelligent situation analysis arrangement method and system, the method includes: obtaining user business analysis demand and extracting core elements, constructs four-layer neural network cooperation framework containing data layer, processing layer, analysis layer and display layer agent;Forward decomposition business target generates initial task execution network, and forms optimized business process by adjusting through reverse cooperation optimization algorithm;Through the double-parameter risk model evaluation process containing model and data parameter vector, identify risk interest points and generate risk situation data;Based on the data, generate editing operation sequence, use continuous time Markov chain model for non-autoregressive adjustment, get the final scheme.This application realizes BI business process intelligent arrangement, improves process quality, efficiency and analysis result reliability.

The invention discloses a statistical method, system and device for service node time consumption and a storage medium, and relates to the technical field of computers, and the method comprises the steps: receiving a node event message reported by a data collector; the data collector is deployed in each business system spanned by the business process; the node event message comprises a global process identifier, a timestamp triggered by a service node and the like; the data collectors use the same time reference source; dividing the node event messages with the same global process identifier into the same group; sorting the node event messages in the groups according to the timestamps; calculating the time difference of timestamps in the sequenced continuous node event messages to obtain service node time consumption; writing the global process identifier, the timestamp, the service node time consumption and the like into a time sequence database; and querying data from the time sequence database, and drawing and displaying a target view according to the data. According to the method, the problems of non-uniform data acquisition standards, asynchronous cross-system clocks, high system coupling degree and poor real-time performance can be solved.

The invention provides a part graph process identification processing method, which comprises the following steps: acquiring a drawing data set which comprises a plurality of graph objects and text marks; splitting the drawing data set into a first processing subset and a second processing subset; performing grouping processing on the graphic objects and the text marks in the first processing subset to obtain a plurality of grouping sets; performing attribute extraction on the grouping set to obtain a plurality of attribute feature sets; performing grouping processing on the graphic objects and the text marks in the second processing subset to obtain a plurality of different pieces of grouping information; inputting the grouping information into the attribute feature set to obtain an extraction result; performing process judgment according to the extraction result, and generating process description content; and classifying the graphic object and the text mark according to the process description content to generate a final process text.

The application discloses a kind of multilayer circuit board hole position offset detection device, belong to multilayer circuit board detection technical field.A kind of multilayer circuit board hole position offset detection device, including rack, detection module is arranged on rack, further include: slidingly arranged in rack detection frame, detection module is arranged in detection frame, wherein, mark cylinder is slidably arranged in detection frame, chamber one for storing marking liquid is opened in mark cylinder, detection rod is slidably connected in chamber one, and spraying pipe is opened in mark cylinder and is communicated with chamber one;Detection table is slidably arranged on rack, and blowing hole is opened in detection table;The application is by setting mark cylinder in detection frame, detection rod is slidably arranged in mark cylinder, can be convenient for post-process identification, reduce downtime screening time, help to improve production detection efficiency, can be rechecked to hole position, prevent burr or scrap in hole from influencing judgment, lead to detection module misjudgment.

A method includes receiving a request to generate a process orchestration and a log associated with the process orchestration, wherein the log includes data associated with one or more objects and associated processes, identifying the one or more objects and associated processes from the log, identifying correlations among the one or more objects and associated processes, generating the process orchestration based on the one or more objects, associated processes, and their correlations, wherein the process orchestration includes one or more process hierarchies including workflows for the one or more objects, and one or more process groups linked to the one or more process hierarchies.

This invention relates to the field of data processing technology, specifically to a metal pulverizing process optimization system based on data analysis. The system includes a pressure abrupt change identification module, a multi-source data sequence registration module, a particle size trend highlighting module, a velocity-pressure offset decomposition module, and a trend time period classification module. In this invention, by extracting pressure abrupt change points to construct a unified time index, precise alignment of multi-source data such as energy consumption, temperature, and particle size is achieved in the time dimension. Combined with directional consistency analysis to screen linkage time periods, stable trend segments are further identified based on changes in the average particle size. The relationship between velocity and pressure difference is decomposed and directionally matched to quantify, constructing a trend response index system. Finally, trend levels are classified according to the degree of matching, and time and directional information are integrated to achieve aggregated expression of multi-variable linkage features and hierarchical presentation of particle size change trends, improving the accuracy of process identification and control response capabilities under complex operating conditions.

Disclosed herein are system, method, and computer program product embodiments for efficient operation relinquishment of a process in a system. An embodiment involves creating a control record based on the process and stores it in a lock-capable storage. The control record contains unique process identification information and an instruction for closing an operation associated with the process. If the embodiment then determines that the process has terminated abnormally based on the control record, it can execute the instruction stored in the control record to close the operation. After the embodiment determines that the operation has closed successfully, it can modify or delete the control record in the lock-capable storage.

This invention discloses a business process identification method, apparatus, device, and storage medium. The business process identification method includes: acquiring an audio file to be identified; performing speech recognition on the audio file to obtain an audio recognition result; executing a preset business process chain to call a process state matcher to match the audio recognition result, thereby obtaining a business process identification result. This invention can improve the accuracy of business process identification results.

The invention discloses a DFMEA system supporting cross fusion of forward design and reverse engineering, and the system comprises a linkage interface module which is used for providing a three-region linkage user interface and supporting two starting modes of a forward design mode and a reverse engineering mode; the structure management module is used for constructing or adjusting a product structure tree; the function association module is used for establishing a bidirectional mapping relation between functions and structures and generating a function chain; and the analysis engine module is used for executing a DFMEA analysis process, identifying a failure mode existing in each target function, analyzing failure reasons, failure influences, current control measures and action priorities, and generating a DFMEA analysis report after analysis is completed. According to the method, through organic fusion of forward and reverse analysis paths, the flexibility and accuracy of the DFMEA analysis process are improved, and the function and structure collaborative modeling and failure recognition capability in the complex product design process is remarkably enhanced.

The present application relates to the technical field of data analysis, and particularly relates to an AI-based unattended weighing-in and weighing-out factory full-process identification method, which comprises the following steps: acquiring the positions of pressure sensors in a weighbridge and the pressure amplitudes of the pressure sensors at each time, and acquiring the point cloud data of the weighbridge and the vehicle at each time, so as to acquire a plurality of support points at each time; acquiring the support force of each support point at each time in combination with the positions of the pressure sensors in the weighbridge and the pressure amplitudes of the pressure sensors at each time; acquiring the gravity center coordinates of the vehicle at the last time in combination with the positions of each support point and the support force at the last time and the pressure amplitudes of the pressure sensors; and acquiring and recording the real weight of the vehicle in combination with the position of the center of the weighbridge and the pressure amplitudes of all the pressure sensors at the last time. The present application acquires the real weight of the vehicle by analyzing the point cloud data and the pressure data in the process of the vehicle driving into the weighbridge.

The invention discloses a running-in process identification method based on friction electric signals, which comprises the following steps: acquiring friction time sequence electric signals in the friction process of different metal friction pairs under a running-in working condition based on a friction electrification system coupling equation set; obtaining running-in data corresponding to the friction time sequence electric signal based on a probability density function; taking the friction time sequence electric signals as characteristic data, and taking the corresponding friction time sequence electric signal running-in data as data labels to obtain a data set so as to obtain a running-in process identification model; and on the basis of a running-in process identification model, performing running-in process identification on the preprocessed time sequence electric signal, and further determining a wear transformation moment between a running-in wear stage and a stable wear stage according to an identification result. The problem that the existing running-in process identification method has factors such as high system complexity, insufficient signal reliability or difficulty in continuous monitoring in engineering scenes such as ship power devices which are limited in space, complex in working condition and need for long-term online operation, so that the running-in process identification precision and efficiency are low is solved.