65 results about "Process identification" patented technology

The invention discloses a digital twinning-based drilling machine lifting system digitization method, device and equipment and a medium, and relates to the technical field of engineering machinery, and the digital twinning-based drilling machine lifting system digitization method comprises the steps of constructing a digital twinning model of a target drilling machine lifting system; virtual-real mapping of the digital twin model is carried out according to the equipment information data; determining simulation data of the digital twinborn model based on the real-time monitoring data, and simulating the digital twinborn model to quantify the model precision of the digital twinborn model so as to obtain an initial digital twinborn model; and performing joint simulation by using a preset technological process identification model and the initial digital twinborn model, and making corresponding automatic updating strategies for different technological processes so as to perform dynamic updating according to the automatic updating strategies, and performing evaluation verification on the initial digital twinborn model to obtain a target digital twinborn model. Therefore, digital twinborn modeling can be carried out on the drilling machine lifting system so as to realize optimization management on the drilling machine lifting system based on the digital twinborn model.

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 a superheat degree control method and system for suppressing superheat degree oscillation. The method comprises the steps that the process identification module identifies dynamic characteristics of the superheat degree control system through a transfer function; the prediction control module constructs a state space model according to the dynamic characteristics, performs state prediction based on a state estimation value and a disturbance estimation value, outputs a state prediction vector and calculates a first objective function; the parameter identification self-tuning module carries out self-tuning on the PID control parameters according to the dynamic characteristics and a second objective function; the PID control module outputs an electronic expansion valve driving signal according to the deviation between a superheat degree predicted value in the state prediction vector and a superheat degree preset target value and the set PID control parameter; and the electronic expansion valve driver drives the electronic expansion valve to act according to the electronic expansion valve driving signal so as to adjust the flow of the refrigerant. According to the technical scheme provided by the embodiment of the invention, superheat degree oscillation caused by low superheat degree operation of the refrigerating system is remarkably inhibited, and the system stability is improved.

The invention is suitable for the technical field of enterprise decision management, and provides an intelligent decision method and system based on a large model, and the method comprises the steps: obtaining a decision target, and obtaining historical data according to the decision target; if the decision target is judged to improve the efficiency, obtaining a production demand, obtaining resource data and obtaining an existing process, analyzing the existing process according to the production demand and the resource data, and identifying unreasonable allocation of low-efficiency links and tasks in the existing process; if the decision target is determined to increase the income, obtaining an existing marketing strategy, analyzing the existing marketing strategy according to the existing product and the existing product price, and identifying whether the conversion rate of the existing marketing strategy reaches the standard; the method comprises the steps of obtaining an optimization result, obtaining an optimization qualified threshold value, comparing the optimization result with the threshold value, and judging whether optimization is qualified or not. The problems that a decision method cannot be provided according to a specific decision direction, improvement suggestions cannot be provided for an existing method, and meanwhile the effect of the decision method cannot be evaluated are solved.

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 relates to the technical field of data processing, in particular to a business data processing method and device, equipment and a medium, which are applied to a financial scene or a medical scene, and are used for obtaining business data of a plurality of business management systems corresponding to preset businesses and business process relationships and business process types corresponding to the preset businesses. And then classifying the business data based on the business process relationship and the business process type to obtain target business data, monitoring whether the target business data has production and consumption abnormity or not based on a preset monitoring rule, and if the target business data has the production and consumption abnormity, obtaining consumer information related to the abnormal target business data, the consumer information comprises the consumer configuration information and the consumer process identification information, so that the normal circulation of the business process is ensured, the influence of abnormity on the business is reduced, and the data processing efficiency is improved.

Artificial intelligence (AI)-based process identification, extraction, and automation for robotic process automation (RPA) is disclosed. Listeners may be deployed to user computing systems to collect data pertaining to user actions. The data collected by the listeners may then be sent to one or more servers and be stored in a database. This data may be analyzed by AI layers to recognize patterns of user behavioral processes therein. These recognized processes may then be distilled into respective RPA workflows and deployed to automate the processes.

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 invention relates to the technical field of process identification, and discloses an automatic identification method, system and device for a railroad bed filling process and a storage medium, and the method comprises the steps: constructing a process structure according to the construction data of railroad bed filling; according to the construction progress and the procedure structure, the front procedure is initialized; collecting a construction image, and performing target identification on the construction image to obtain an identification target; acquiring equipment sensor data of the construction equipment, and performing data analysis on the equipment sensor data according to the auto-encoder model to obtain an equipment state; and determining the current three-stage process according to the identification target and the equipment state, and updating the previous process according to the three-stage process. Through image recognition, data analysis and process reasoning, accurate recognition and automatic updating of the construction process can be realized, so that the management efficiency and the project progress of railway roadbed filling construction are improved, and the unmanned management degree of construction is further improved.

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.

The invention discloses a key process digital management and control system and method.The system comprises a process planning system, a product data management system and a manufacturing execution system, the process planning system completes compiling of a structured process file and carries out key process identification, and the structured process file comprises structured process data; the process data are recorded in a key process operation content table, a key process quality control table and a key process inspection record table; the product data management system archives the structured process data in an XML text form and issues the structured process data to the manufacturing execution system; and the manufacturing execution system receives and analyzes the process data in the XML text form, creates a structured three-fixed table, fills a production process operation result and a detection result in a key process inspection record table, and generates a visual record table. According to the invention, structured storage, electronic recording and visual query of key process data can be realized, so that digital management and control of the key process production process can be realized.

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 invention discloses a simulator and debugger linkage-based full-system-state process identification method, which comprises the following steps of: S1, constructing a new command get-process of qemu for performing a process identification function according to values of an IP (Internet Protocol) register and an SP (Service Provider) register; s2, setting an interaction module for completing information collection of the process; s3, constructing a full-system-state virtual machine mirror image of a simulated environment, wherein the full-system-state virtual machine mirror image comprises a gdbserver user state program; and S4, calling an interaction module to complete process identification. The method has the advantages of being simple in principle, simpler and more convenient to operate, easier to implement, better in compatibility and the like.

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.