72 results about "Intelligent manufacturing system" patented technology

The invention relates to the technical field of intelligent manufacturing and reinforcement learning, and discloses a causal-driven intelligent manufacturing strategy optimization method and system, and a medium. Comprising the following steps: collecting state information of an intelligent agent and a production environment, and constructing an environment data set and a zero action data set; constructing a causal decoupling dynamic model comprising a causal intervention module and an environmental evolution module; mixing the environment data set and the zero action data set, and training a causal decoupling dynamic model by using a first mixed data set; performing intervention simulation on the environment data set by using the trained causal decoupling dynamic model to obtain a model data set; mixing the environment data set and the model data set, and training an unbiased strategy optimizer by using a second mixed data set; and deploying the trained unbiased strategy optimizer into a production system, and driving an intelligent agent to execute production operation according to the state information. Through causal-driven modeling and unbiased strategy optimization, high-precision and high-reliability decision control of the intelligent manufacturing system in a complex dynamic environment is realized.

A Comprehensive Energy-Saving and Carbon-Negative system (CESCN) includes a Carbon-negative Emissions Device (CED) based on Carbon Capture, Utilization and Storage (CCUS). CED can obtain a carbon emission reduction target from a current Industry 4.2-Green intelligent Manufacturing (14.2-GiM) system, and then collect the material consumption and equipment operation energy consumption data of the CCUS process of CED through a Cyber Physical Agent (CPA) which is the Internet of Things (IoT) component. An intelligent method is used to identify the execution progress of CCUS and improve the automation and intelligent operation of CCUS. The CESCN can effectively reduce the manpower requirements of implementing CCUS and the overall energy consumption of the production line. In addition, it can calculate the amount of the carbon reduction, the amount of the carbon offset and amount of feasible carbon trading according to the amount of carbon fixation.

The invention relates to the technical field of intelligent manufacturing control, and discloses a dynamic continuous polymerization and online block regulation and control method and system for a polyurethane hot melt adhesive. The invention aims to solve the technical problems of long formula development period, black box in the process and poor batch stability in the traditional polyurethane reaction extrusion process. According to the method, a closed-loop adaptive control system of virtual simulation guidance and physical data correction is constructed, so that intelligent optimization from target performance to an optimal process is realized. The core of the method is that space-time alignment of full-cycle data is realized by utilizing a material retention time distribution function, and internal parameters of an aggregation kinetic model are reversely corrected and a formula knowledge base is updated based on actually measured evolution data and theoretical prediction deviation characteristics. By constructing an intelligent manufacturing system with transparent perception and accurate regulation and control, the formula development efficiency and the product quality consistency are remarkably improved, and the self-adaptive regulation capability in the production process is greatly enhanced.

The invention discloses a multi-algorithm collaborative optimization water turbine key component additive manufacturing method, and relates to the technical field of additive manufacturing. According to the method disclosed by the invention, a complete intelligent manufacturing system is constructed: in a data acquisition stage, a high-precision laser scanning technology (the precision reaches 0.01 mm) is adopted to realize accurate three-dimensional reconstruction of a damaged area; in the modeling analysis link, thermodynamic behaviors in the restoration process are accurately predicted through finite element simulation of multi-physics field coupling; in the aspect of process optimization, an adaptive differential evolution algorithm, an improved NSGA-II multi-objective optimization algorithm and deep reinforcement learning (DRL) are innovatively and organically combined to form a full-process intelligent decision-making system for coverage deviation analysis, path planning and parameter optimization. Particularly, due to introduction of a deep reinforcement learning algorithm, millisecond-level dynamic adjustment of key process parameters such as wire and powder feeding speed and laser power is achieved, and the stability and the material utilization rate of the manufacturing process are greatly improved.

The invention relates to the technical field of intelligent manufacturing system design, and discloses an intelligent manufacturing production line automatic design method and system based on a large model, and the method comprises the steps: building a semantic understanding engine through a pre-training large language model, and converting user unstructured text input into a standardized design element set; based on the process knowledge graph and the product manufacturing feature library, performing process path reasoning and task level disassembly by using a graph neural network, and disassembling the standardized design element set into a plurality of sub-tasks; constructing an equipment capability database, realizing automatic matching of a plurality of subtasks and equipment through vectorization similarity retrieval and a multi-objective optimization model, and outputting a recommended equipment combination list; based on the recommended equipment combination list and the spatial constraint condition, a digital workshop sketch is established by adopting a 3D spatial modeling module, the equipment layout and the logistics path are optimized by applying a genetic algorithm, and an optimal layout scheme set is output; according to the invention, intelligent design and optimization support of the whole life cycle are provided.

The utility model discloses an industrial control board based on STM32F103 singlechip, it includes main control chip, RS 485 level conversion circuit, digital I / O circuit and LED pilot lamp, and RS 485 circuit realizes efficient level conversion through three stage anti -interference framework magnetic pearl filtering, photo -coupler isolation, TVS protection, and digital I / O circuit adopts photo -coupler and isolation amplifier to enhance signal stability, and is equipped with LED pilot lamp feedback state, and the application effectively reduces the code rate less than 10 ‑7 , reduces signal dithering, and the temperature drift attenuation is only 2.1%, is suitable for industrial automation and intelligent manufacturing system, has simple structure, low in cost, stable performance and the advantages such as.

1. The name of the design product: programmable controller (VG700). 2. The use of the design product: used for switch logic control, analog control, motion control, process control, data processing and networking communication of intelligent manufacturing system equipment in automated production. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The invention relates to the technical field of collision sensing devices, and discloses a collision sensing device for an industrial robot capable of being conveniently installed, the collision sensing device comprises at least one collision sensing module, at least one data processing module, a shell and a quick release type adhesion fixing mechanism, the collision sensing module is used for detecting collision and outputting capacitance information; the data processing module is used for collecting and processing capacitance information output by the collision sensing module; the shell comprises a sensing end shell, a sensing and processing end shell and a processing end shell, collision sensing modules are installed in the sensing end shell and the sensing and processing end shell, and data processing modules are installed in the sensing and processing end shell and the processing end shell; the quick-release adhesion fixing mechanism is used for fixing the sensing end shell, the sensing and processing end shell and the processing end shell to the surface of the industrial robot, real-time and accurate sensing of the collision behavior of the industrial robot is achieved, and the safety and reliability of the intelligent manufacturing system are improved.

This invention discloses a method for processing multimodal data acquisition in intelligent manufacturing systems, relating to the field of industrial data governance technology. The method includes: acquiring multimodal data streams; segmenting raw data blocks and standardizing and encapsulating them to generate metadata records; establishing time indexes, batch indexes, and process indexes; hashing the metadata records to generate content addresses and writing them to object storage; recalculating the hash and comparing the content addresses to complete consistency verification and writing the results to a commit log table; generating operator records for successfully committed data blocks and hashing them to obtain operator addresses; registering lineage edges to form directed acyclic lineage relationships; generating leaf lists according to a preset range and aggregating them to obtain root digests; verifying content addresses object by object based on the leaf lists and root digests; replaying and recalculating to generate replay-derived data blocks; comparing them to generate audit records and storing them. This invention ensures continuous monitoring of data quality during big data acquisition through the replay and recalculation processing of directed acyclic lineage relationships.

Intelligent manufacturing systems for the optimized curing of adhesives for battery assemblies, methods for manufacturing / using such systems, and stored instructions for automating the operation of such systems are presented. One method for assembling a battery module involves aligning a stack of battery cells on a workpiece carrier and compressing the cell stack to a predefined length. Module housing side plates are positioned on opposite lateral sides of the compressed cell stack, and a thermosetting structural adhesive (SAM) is injected between the side plates and the lateral sides of the cell stack. A conductive heating system heats the module housing side plates to cure the SAM. While the side plates are heating, a cell thermal conditioning system cools a selected surface of the compressed cell stack.After determining that a monitored curing time has reached a predefined curing time, the system interrupts the heating of the side plates and the cooling of the battery cells.

The application discloses an industrial full-link through implementation method based on unique identification and double contract anchoring, and belongs to the technical field of intelligent manufacturing informatization. The method takes a plan number as a unique and unchangeable identification throughout the full life cycle from raw material warehousing to finished product warehousing, takes a contract hash value as a cross-table association anchor point, adopts a step-by-step anchoring mechanism of sales contract priority binding and procurement contract subsequent binding, and combines batch processing, transaction safety control and full-chain backtracking synchronization technology to realize bidirectional synchronous updating of procurement and sales contract information in full life cycle data tables such as raw material inventory tables and production process quality inspection record tables. The application breaks the strong coupling of the procurement and production processes, supports cost information post-recording, solves the pain points of data disconnection, untraceability, low efficiency and maintenance difficulty of traditional industrial software, has a simple architecture, high performance and stable operation, and is suitable for various intelligent manufacturing systems such as MES and ERP. The application has been applied in actual industrial scenes and has industrialization popularization capability.

The invention relates to the technical field of additive and subtractive integrated intelligent manufacturing, and discloses an intelligent manufacturing system and method for coaxial feeding, stirring and friction solid-phase additive-subtractive machining integrated equipment. And efficient additive deposition and precise subtractive machining can be achieved on one equipment platform. A metal material is fed into a friction stir head through a coaxial feeding device, surface finishing and surface layer removal are carried out through a material reducing module while additive manufacturing is carried out through solid heating and plastic deformation technologies, surface, interlayer and internal structure performance regulation and control are carried out in combination with needle-free friction stir treatment and synchronous induction heating rolling, and the mechanical property of the metal material is improved. The problems of precision, surface quality and production efficiency which cannot be independently achieved through single additive manufacturing or subtractive machining are solved, efficient utilization of materials can be achieved in manufacturing of high-performance and large complex structural parts through the additive and subtractive composite technology, and meanwhile the forming precision and the mechanical property are guaranteed.

The invention relates to the field of intelligent manufacturing systems of aluminum alloy casting workshops, in particular to a molten aluminum supply production line for aluminum alloy low-pressure casting and a control method of the molten aluminum supply production line for aluminum alloy low-pressure casting. And the optimization model is solved in combination with the operation process and logic of the molten aluminum supply production line, so that the optimal solution of the operation parameters of the multiple devices is obtained. Besides, when the optimization model is solved, a pause probability model is introduced, and the optimal solution of the operation parameters under the target of achieving the minimum cost in the operation process of the molten aluminum supply process is obtained under the condition that pause is introduced. According to the aluminum liquid supply process proactive optimization method provided by the invention, the optimization model and the solving method based on the multi-agent simulation and meta-heuristic algorithm are constructed, so that the efficient optimization of the operation parameters is realized, and the purposes of reducing cost and improving efficiency are remarkably achieved.

The invention relates to the technical field of intelligent manufacturing and dynamic personnel scheduling optimization, and discloses a dynamic personnel scheduling optimization method based on DDQN. According to the method, a DDQN intelligent decision framework oriented to the industrial scheduling problem is constructed, and the problem of Q value over-estimation in a traditional DQN algorithm is effectively solved by designing a multi-dimensional state space containing a process state, a team and a parallel mark and directly mapping an action space of an actual scheduling decision; and process states and worker behaviors are modeled through a deep reinforcement learning framework, and a task allocation and resource scheduling scheme is dynamically adjusted in combination with a real-time worker behavior prediction module, so that the total completion time is minimized and the resource utilization rate is maximized in a multi-process and multi-constraint scene, and the working efficiency is improved. The method is suitable for complex industrial environments such as chemical workshops and flexible manufacturing systems, scheduling challenges caused by process dependence, worker heterogeneity and dynamic task requirements can be effectively handled, and the production efficiency and the response capability of the intelligent manufacturing system are improved.

The invention belongs to the technical field of intelligent manufacturing data protection, and discloses an intelligent manufacturing system and method for an explosion-proof motor. The system comprises a formulating module which is used for determining a flow link of intelligent manufacturing of the explosion-proof motor, and uploading and storing the flow link to a cloud server; wherein the process links correspond to equipment and equipment control parameters; the setting module is used for configuring a sub-data bin corresponding to one process link, the sub-data bin is correspondingly configured with a data port, and the data port is externally deployed with a docking information area; according to the method and the device, an illegal user can be effectively prevented from being connected with the sub-data bins through imaginary data, the cracking time of requests which do not conform to a preset condition can be prolonged, request behaviors which do not conform to the preset condition can be confused, and a time window corresponding to copy and destroy skin replacement is won for the sub-data bins; the method and the device can avoid request positioning sub-data bins which do not accord with preset conditions so as to facilitate safety protection of data of each flow link in an intelligent manufacturing system of the explosion-proof motor.

The invention provides an automatic ultrasonic detection device and method for a differential mechanism welding assembly, and the device comprises an ultrathin sheet-shaped ultrasonic probe which is installed below a welding seam of the differential mechanism welding assembly in a height-adjustable manner; the active jet flow coupling system forms a stable sound wave conduction medium layer between the ultrathin sheet-shaped ultrasonic probe and the welding seam; the ultrasonic analyzer and control unit is connected with the ultrathin sheet-shaped ultrasonic probe and is used for collecting ultrasonic echo signals and analyzing, identifying and quantifying weld defects in real time by adopting a perimeter algorithm, and when defect echoes exceeding a preset threshold value are found, perimeter equivalent calculation is immediately started and all the defects are automatically graded; outputting a final judgment conclusion of qualification or not; and the mechanical scanning mechanism is used for clamping and driving the differential mechanism welding assembly to rotate. According to the invention, efficient, accurate and reliable automatic detection which is deeply fused with an intelligent manufacturing system can be realized.

The invention discloses a flexible intelligent manufacturing system based on AI intelligent control, and belongs to the technical field of industrial intelligent manufacturing. The system realizes sensing, transmission, analysis and control closed loop of the whole manufacturing process through an industrial Internet of Things architecture, and breaks through the bottleneck of traditional flexible manufacturing in combination with an AI algorithm; the system covers an industrial Internet of Things sensing layer, an edge transmission layer, an AI intelligent control layer, a flexible execution layer and a cloud collaboration layer. The sensing layer uses multi-type sensors to collect data precisely in a global manner. The edge transmission layer realizes low-delay and high-reliability transmission and preprocessing based on a specific protocol and a 5G gateway; the AI intelligent control layer analyzes data in real time and performs fault prediction and the like; the flexible execution layer responds to the instruction to realize flexible cooperation; and the cloud collaboration layer gathers data and iterates the model. According to the method, multiple technical problems of traditional flexible manufacturing are solved, the system flexibility, the intelligent level and the production efficiency are improved, the cost is reduced, and the method is suitable for multi-field flexible production scenes.

This invention discloses a flexible control method for digital twin manufacturing systems addressing uncertainties in the production process, belonging to the field of intelligent manufacturing system technology. The method includes: statistically analyzing the time of the four stages of perception, analysis, decision-making, and execution; determining the advance prediction time by combining system complexity and simulation speed; constructing an scalable system state transition matrix; performing accelerated simulation based on real-time states and injecting uncertain events according to trigger probabilities to obtain multiple future states; selecting the top n high-probability states in descending order of occurrence probability for parallel decision-making; binding decision instructions with corresponding states and issuing them in advance; and executing the corresponding instructions through state matching when the physical system reaches the predicted time, achieving flexible control. This invention can improve the consistency and robustness of "virtual control of the real" in digital twin systems under uncertain conditions and is applicable to multi-level digital twin systems such as machine tools, robots, production lines, and workshops.

The application relates to the technical field of industrial communication and intelligent manufacturing system integration, and discloses an enterprise-based intelligent manufacturing digital integration analysis method, which comprises the following steps: collecting real-time operation data of production equipment, forming an integrated data pool after cleaning, standardization and space-time alignment, constructing an enterprise intelligent manufacturing knowledge graph based on the business logic of the intelligent manufacturing field and calculating key performance indicators, combining the integrated data pool and the knowledge graph, applying the integrated data pool and the knowledge graph to constructing a digital twin model, predicting production bottlenecks and evaluating different production scheduling plans, and finally generating a comprehensive analysis report based on the index calculation results and the simulation results. The application realizes full-process automation from data perception, integration and application to decision output, and significantly improves the transparency, analysis accuracy and decision intelligence level of the manufacturing management process.

1. The name of the design product: programmable controller. 2. The use of the design product: used in the centralized control of frequency converters, servo drives and other equipment in automatic production and intelligent manufacturing systems. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: design 1 perspective view. 5. Design 1 is designated as the basic design.

The invention relates to an intelligent manufacturing system maintenance method considering machine-raw material-environment coupling degradation in the technical field of intelligent manufacturing, and the method comprises the steps: constructing an intelligent manufacturing system with a multi-process route flexible manufacturing network as a core into an intelligent manufacturing system architecture under the background of the Internet of Things, abstracting an intelligent manufacturing system architecture into a complex manufacturing dynamic network composed of heterogeneous machines and heterogeneous product process routes; constructing a machine-raw material-environment coupled intelligent manufacturing system degradation model, and determining the real-time reliability of the machine according to the intelligent manufacturing system degradation model; according to the average fault time interval of the machine and the performance efficiency loss caused by machine shutdown, the maintenance importance degree of the machine in the intelligent manufacturing system is determined; and when the real-time reliability and the maintenance importance of the machine are determined, a comprehensive maintenance strategy is adopted to maintain the machine in the intelligent manufacturing system. According to the method, the system maintenance cost is reduced, and reliable, efficient and low-cost system operation is finally realized.

The application discloses a BEVFusion industrial inspection robot positioning method based on multi-scale wavelet transform, multi-frequency domain decomposition is carried out on the input characteristics of a multi-modal sensor through discrete wavelet transform, low-low frequency, high-low frequency, low-high frequency and high-high frequency information are separated out, and the enhanced features are realized by combining a convolutional neural network and a full convolutional neural network. Specifically, the improved CNN decomposes and enhances the camera features, and then reconstructs the camera features through inverse wavelet transform, and meanwhile, the extracted laser radar point cloud features are combined, so that the camera and point cloud features are fused, and the improved RCNN deeply fuses the camera and laser radar features. The application of the method significantly improves the target detection range and semantic segmentation capability in a dynamic environment, and provides important technical support for the application and development of the fields of autonomous navigation of an industrial robot, monitoring of a high-risk environment device and multi-sensor collaborative perception of an intelligent manufacturing system.

This invention discloses an intelligent manufacturing system based on the entire lifecycle of special bolts, belonging to the field of intelligent manufacturing technology. It includes a digital twin construction module for receiving orders, calling a pre-set working condition-performance mapping model, generating initial performance targets based on the target working condition information in the order, and creating an initial digital twin with a unique digital identifier for each order. The initial digital twin integrates design specifications, material properties, and the initial performance targets. This invention significantly improves product performance consistency, manufacturing efficiency, and resource utilization by constructing a digital twin and intelligent closed-loop system covering the entire lifecycle of special bolts. It achieves transparency in the manufacturing process and precise control over product quality. This effectively avoids early failures caused by improper processes, reduces rework and scrap, and thus lowers production costs and delivery cycles while meeting high-performance indicators.

The invention belongs to the field of intelligent mechanical equipment, and particularly relates to a multi-station pose controllable device. The multi-station material storage device is widely applied to all walks of life and is a core unit of a flexible production and intelligent manufacturing system. The invention provides a multi-station pose controllable device which comprises a rack, a bearing platform, a lifting device and a connecting device, and the lifting device comprises a linear transmission mechanism and two guide rails. The posture of the bearing platform can be adjusted and controlled through cooperation of the single-rotating-pair connecting module and the double-rotating-pair module. According to the connecting module based on the roller path and roller structures, the technology that the double bearing platforms share the guide rail is formed, mutual supporting is achieved, reliability is high, high universality is achieved, the number of the bearing platforms can be conveniently increased, and space and cost are saved. The performance of the intelligent manufacturing equipment is improved, wide application of the intelligent manufacturing equipment in the fields of manufacturing, logistics, medical instruments, automobiles and parts, low-altitude economy and the like is promoted, and important practical value is achieved.

The invention relates to the technical field of semiconductor intelligent manufacturing, and discloses a method and a system for realizing efficient data storage of a semiconductor MES system by expanding MyBatis Plus, the system comprises an optimistic lock module, a logic deletion module, an expansion module and a loading module, and according to the method, optimistic lock concurrency control processing is performed by introducing a data version mechanism and a logic deletion mechanism; based on a data version check result and a logic deletion mechanism, a MyBaatisPlus framework is expanded to define a template method of full-amount update data, and a default injector is inherited and expanded through a self-defined SQL (Structured Query Language) injector; in the system starting stage, a user-defined SQL injector is replaced, a data access factory is created, and data version control, logic deletion and total data updating are comprehensively applied to execute data operation. The data reliability of the manufacturing execution system and the decision-making accuracy and stability of the intelligent manufacturing system are improved, and the flexibility of system data management is improved.

The application relates to the technical field of intelligent manufacturing systems, in particular to an intelligent manufacturing system and method based on digital twinning, a data acquisition module is used for acquiring manufacturing data in a manufacturing system, a data analysis module is connected with the data acquisition module to receive the manufacturing data and process the manufacturing data into feature data, a visualization module is connected with the data analysis module to receive the feature data and construct a virtual manufacturing scene, a real-time control module performs dynamic task control based on the feature data, and an early warning module is connected with the real-time control module to receive abnormal information and send adjustment instructions to the real-time control module; the modules in the intelligent manufacturing system form a closed loop of synergistic effect through data flow and information flow, the data analysis module performs multilevel decomposition and high-dimensional feature fusion, extracts key features in a manufacturing process, and finally displays the key features in the virtual manufacturing scene through the visualization module, so that accurate mapping of a manufacturing state is realized, and adjustment can be made in time when an abnormal state is found.

The invention discloses an elastic control method for a digital twin manufacturing system aiming at uncertainty of a production process, and belongs to the technical field of intelligent manufacturing systems. The method comprises the following steps: counting time of four links of perception, analysis, decision making and execution, and determining advanced prediction time by combining system complexity and simulation speed; constructing an expandable system state transition matrix; performing accelerated simulation based on the real-time state, and injecting an uncertain event according to a triggering probability to obtain a plurality of future states; ranking in a descending order according to the probability of occurrence, and selecting the first n high-probability states for parallel decision making; binding the decision instruction with the corresponding state and issuing the decision instruction in advance; and when the physical system reaches a predicted moment, executing a corresponding instruction through state matching to realize elastic control. According to the method, the consistency and robustness of the digital twin system under the uncertain condition can be improved, and the method is suitable for multi-level digital twin systems of machine tools, robots, production lines, workshops and the like.

This application provides a method for improving the processing efficiency of an intelligent manufacturing system based on tags. The intelligent manufacturing system includes a data resource pool, a production scheduling center, an AGV robot scheduling center, AGV robots, processing stations, a waiting-to-process area, and a finished product area. The data resource pool is equipped with tags, including processing tags and handling tags. The processing tags include the processing sequence, processing steps, processing station, and processing time for each part to be processed, used to track the processing of each step of the part at different processing stations. After the part is labeled with a processing tag, it enters the waiting-to-process area. The handling tags are used by the AGV robots to transport parts between different locations in the raw material area, processing area, and finished product area according to planned paths and self-finding paths. This method solves the problem in existing intelligent manufacturing processes where the handling time of each step in the AGV workpiece transportation process is long, resulting in a long overall processing time and reduced production efficiency.

The present application relates to a kind of flexible intelligent manufacturing systems, including rack and multiple work modules.The mounting surface of rack is equipped with first standard interface group.The bottom of each work module is equipped with first module interface group, first standard interface group and first module interface group cooperate to make work module detachably installed on rack, first module interface group and first standard interface group all have the same cooperation size and connection form, to realize the interchange of same type or different type work module.Working module includes feeding module and multiple pickup special components, feeding module includes second standard interface group, each pickup special component is provided with second module interface group, second standard interface group and second module interface group cooperate to make pickup special component detachably installed on feeding module.The flexible intelligent manufacturing system of the present application is by being set as whole replacement cooperation pickup special component partial replacement, it is advantageous to further improve the versatility of equipment.

1. The name of the design product: display screen panel for chart drilling configuration and operation of graphical user interface. 2. The use of the design product: for information display and interaction; the design product can be used for tablet computers, notebook computers and desktop computers. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best shows the design points: design 1 change state diagram 1. 5. Other views except the front view containing the graphical user interface have no design points, and other views are omitted. 6. Design 1 is designated as the basic design. 7. The use of the graphical user interface: for multi-field drilling function configuration and operation interface of data multi-dimensional visualization analysis of intelligent manufacturing BI system, which can display data content in chart form, and meet the use requirements of BI system multi-level drilling function through the combination of tree structure and drilling list and the drilling relationship configuration between charts. 8. The human-computer interaction mode of the graphical user interface: the main view of design 1 is the chart drilling configuration function entrance interface, clicking the "new drilling structure" on the right upper side of the interface can pop up the corresponding new window, as shown in design 1 change state diagram 1, the required content can be selected and filled in the pop-up window (as shown in design 1 change state diagram 2), and then the "OK" button in the lower right corner is clicked to enter the interface of design 1 change state diagram 3, and the newly created single-level drilling structure is displayed on the right upper side. The main difference between design 2 and design 1 is that in design 2 change state diagram 1, after filling in the drilling content, the "plus" in the middle of the pop-up window is clicked to add the next level of drilling, enter the interface of design 2 change state diagram 2, click the "OK" button after configuration to enter the interface of design 2 change state diagram 3, and the newly created two-level drilling structure is displayed on the right upper side. The difference between design 3 and design 2 is that in design 3 change state diagram 2, after filling in the 2-level drilling content, the "plus" in the pop-up window is clicked again to add the 3rd level of drilling, as shown in design 3 change state diagram 3, and then the 4th and 5th levels of drilling are added in the same way, as shown in design 3 change state diagram 4 and 5, and then the "OK" button is clicked after configuration to enter the interface of design 3 change state diagram 6, and the newly created multi-level drilling structure is displayed on the right upper side. The main view of design 4 is a page with a multi-level drill-down structure configured. Clicking "Edit Drill-down Structure" below the drill-down structure tree on the right side of the interface can pop up the corresponding edit window, as shown in design 4 change state diagram 1. In the "Drill-down Content" area, if there are multiple siblings at a certain level, a thumbnail display will be presented. At this time, you can click a node in the drill-down structure tree on the left side of the pop-up window to view the drill-down content corresponding to the node and edit it. For example, clicking the third-level drill-down "Grouped Summary Table 1-Yield" will enter the interface of design 4 change state diagram 2. For nodes displayed in thumbnail form, you can view the drill-down content corresponding to the node by clicking a certain block in the "Drill-down Content" area with "" or by clicking the corresponding node in the tree on the left side of the pop-up window. Both are displayed in linkage, such as clicking the fourth-level drill-down "Section Grouped Summary Table" on the right side of the pop-up window of design 4 change state diagram 2 or clicking the fourth-level drill-down "Grouped Summary Table 2-Section" in the tree on the left side of the pop-up window. You can enter design 4 change state diagram 3. The corresponding part of the node drill-down content and the corresponding content of the tree are both highlighted due to linkage. Design 5 is the interface display for adding sibling drill-down at a single level. The change state and human-computer interaction mode of design 5 main view to design 5 change state diagram 2 are the same as those of design 1 main view to design 1 change state diagram 2. Clicking the "Add Sibling Drill-down" button on the right side of the pop-up window of design 5 change state diagram 2 enters the interface of design 5 change state diagram 3. You can select and fill in the required content in this pop-up window, as shown in design 5 change state diagram 4. Clicking the "OK" button at the bottom right of this interface pop-up window completes the addition of sibling drill-down at a single level. Design 6 is the interface display for adding sibling drill-down at multiple levels. The change state and human-computer interaction mode of design 6 main view to design 6 change state diagram 2 are the same as those of design 4 main view to design 4 change state diagram 2. Clicking the "Add Sibling Drill-down" button on the right side of the selected third-level drill-down on the right side of the pop-up window of design 6 change state diagram 2 enters the interface of design 6 change state diagram 3. Select and fill in the required content in this pop-up window, as shown in design 6 change state diagram 4. Clicking the "OK" button at the bottom right of this interface pop-up window completes the addition of sibling drill-down at multiple levels. 9. Other situations that need to be explained. The "X" in the view represents replaceable or changeable text, numbers, or punctuation marks.