A data warehouse-based data management method, system, medium and device

Abstract

The application relates to the technical field of numerical control and discloses a data management method, system, medium and equipment based on a data warehouse, which adopts an architecture design of 'running memory + data warehouse', that is, current parameter values are loaded into a software memory, and the rest of the parameter values are stored in the data warehouse; when function switching is performed, corresponding parameters are read from the data warehouse and loaded into the running memory to directly replace original parameters; compared with a traditional method, the application uses the data warehouse to manage parameters, can greatly reduce software memory consumption, significantly improves the memory resource utilization efficiency of numerical control system software, and improves the flexibility and expandability of data management; in addition, the data warehouse has universality and can be applied to multiple fields including but not limited to laser processing data management, spatial error compensation data management and the like, and has wide application prospects.

Description

Technical Field

[0001] This invention relates to the field of numerical control technology, and in particular to a data management method, system, medium and device based on a data warehouse. Background Technology

[0002] In the CNC machining industry, when manufacturers produce the same type of products but different models and specifications, they typically use parametric programming. This means the machining process is fixed, and process elements such as machining dimensions, depth, and feed are represented by parameters. When switching to a different specification, only the parameter values ​​need to be modified; the main program corresponding to the machining process does not need to be changed, allowing for rapid switching between different machining processes as needed. In high-end CNC systems, a complete set of process parameters is usually packaged into an independent data block called a process parameter set. Switching between machining processes simply keeps the original machining program unchanged and loads the different process parameter sets.

[0003] CNC system software is generally based on embedded systems, where available memory resources are often limited. In traditional solutions, CNC system software typically pre-loads and stores all process parameter sets in memory, such as... Figure 1a As shown, when the software needs to support a large number of process types or the set of process parameters is large, using traditional solutions will lead to a significant increase in memory usage.

[0004] To address the aforementioned issues, a data management method with higher memory resource utilization is needed to improve the efficiency of tasks in CNC machining, including laser processing and spatial error compensation. Summary of the Invention

[0005] This invention provides a data management method, system, medium, and device based on a data warehouse, which solves the technical problem of excessive memory consumption in the prior art.

[0006] The first aspect of this invention provides a data management method based on a data warehouse, applied to a numerical control system, comprising the following steps: Step 1: Construct a data warehouse, which includes a functional relationship diagram for describing the logical relationships between multiple related functions, a data model for defining parameter characteristics, and a numerical storage area for storing the parameter values ​​corresponding to each function. Step 2: Based on the target task, filter and combine the functions in the functional relationship diagram to construct a target functional map, and configure the parameters of the target functional map based on the data model; Step 3: Load the initial parameter values ​​of the target task into the running memory of the CNC system, and store the other parameter values ​​into the corresponding numerical storage area of ​​the data warehouse; Step 4: Execute the current function based on the parameter values ​​in the running memory. When a function switching instruction is triggered, query the target function map and read the corresponding target parameter values ​​from the data warehouse to replace the original parameter values ​​in the running memory. Repeat this step until the target task is completed.

[0007] Preferably, the data model is used to provide a unified data template. The data features of the data model include at least one of index, mapping address, data type, data precision, default value, maximum value, minimum value, unit, name, and comments, thereby effectively solving the problem of inconsistent data formats in the data warehouse.

[0008] Preferably, the functional relationship diagram adopts a multi-branch tree topology structure, where each internal node of the multi-branch tree topology structure represents a functional dimension, and the leaf nodes represent specific functions and are associated with corresponding data models and parameter values; wherein, the content of each node includes at least one of the following: level, total level, index, parent node index, level name, node name, and annotation information, thereby clearly describing the hierarchical relationship between functions and providing a framework for parameterized programming.

[0009] Preferably, the function switching instruction is triggered by manually switching on the system interface or by switching via a G command, thereby reading a set of parameters from the data warehouse to replace the original parameters in the running memory.

[0010] Preferably, it also includes extended management steps, specifically: constructing multiple data warehouses corresponding to different functions, and establishing a relationship table for the multiple data warehouses; When executing the target task, the target task is split into different task nodes, and the corresponding target data warehouse is queried for each task node to establish the target function map corresponding to the task node and configure the parameters. The association table is queried to obtain the target associations of multiple target data warehouses. Steps 3 and 4 are executed sequentially for each task node according to the target associations until the target task is completed.

[0011] A second aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the data management method based on a data warehouse as described above.

[0012] A third aspect of the present invention provides a data management device based on a data warehouse, including a computer-readable storage medium and a processor, wherein the processor executes a computer program on the computer-readable storage medium to implement the steps of the data management method based on the data warehouse described above.

[0013] A fourth aspect of this invention provides a data management system based on a data warehouse, including a first construction module, a second construction module, a preloading module, and an execution module. The first construction module is used to build a data warehouse, which includes a functional relationship diagram for describing the logical relationship between multiple related functions, a data model for defining parameter characteristics, and a numerical storage area for storing the parameter values ​​corresponding to each function. The second construction module is used to perform functional filtering and combination on the functional relationship diagram according to the target task to construct a target functional map, and to configure the parameters of the target functional map based on the data model; The preloading module is used to load the initial parameter values ​​of the target task into the running memory of the CNC system, and store other parameter values ​​into the corresponding numerical storage area of ​​the data warehouse. The execution module is used to execute the current function based on the parameter values ​​in the running memory. When a function switching instruction is triggered, it queries the target function map and reads the corresponding target parameter values ​​from the data warehouse to replace the original parameter values ​​in the running memory. This step is repeated until the target task is completed.

[0014] Preferably, the system interface includes a map management interface, a data model management interface, and a file management interface. The functional relationship graph can be filtered, combined, and its parameters configured through the graph management interface to construct the target functional graph and parameter values ​​corresponding to the target task. The parameter characteristics of the data warehouse are defined through the data model management interface; The task file is generated through the file management interface, and the task file can be saved, loaded, edited, imported, or exported. The task file includes the target function map and the corresponding parameter values.

[0015] Preferably, in one embodiment, the system further includes an extended management module, specifically: The associated building unit is used to build multiple data warehouses corresponding to different functions and to establish a relationship table between the multiple data warehouses; The configuration unit is used to split the target task into different task nodes when executing the target task, and query the corresponding target data warehouse for each task node to establish the target function map corresponding to the task node and configure the parameters. The cross-warehouse management unit is used to query the relationship table to obtain the target relationship of multiple target data warehouses, and to execute the preloading module and the execution module sequentially for each task node according to the target relationship until the target task is completed.

[0016] The beneficial effects of this invention are: This invention provides a data management method, system, medium, and device based on a data warehouse, adopting an architecture design of "running memory + data warehouse," such as... Figure 1b As shown, the current parameter value or set of current parameter values ​​is loaded into the software memory, while the remaining parameter values ​​or sets of parameter values ​​are stored in the data warehouse. When switching functions, such as switching between different process parameters, the corresponding parameters are read from the data warehouse and loaded into the running memory to directly replace the original parameters. Compared with traditional methods, this invention uses a data warehouse to manage parameters, which can significantly reduce software memory consumption, significantly improve the memory resource utilization efficiency of CNC system software, and improve the flexibility and scalability of data management. In addition, the data warehouse has universality and can be applied to multiple fields, including but not limited to laser processing data management and spatial error compensation data management, and has broad application prospects.

[0017] To make the above-mentioned objects, features and advantages of the invention more apparent and understandable, preferred embodiments of the invention are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1a This is a diagram illustrating data loading in existing data management methods. Figure 1b This is a schematic diagram of data loading in the data management method of the present invention; Figure 2 This is a flowchart illustrating the data management method provided in Example 1; Figure 3 This is an overall architecture diagram of the data management method in Example 1 applied to laser processing; Figure 4 This is a schematic diagram of a cross tree of laser processing technology; Figure 5a This is a schematic diagram of the spectrum management interface for laser processing applications; Figure 5b This is a schematic diagram of the data model management interface for laser processing applications; Figure 6 This is a schematic diagram of the structure of the data management system based on a data warehouse provided in Example 2; Figure 7 This is a schematic diagram of the structure of the data management device based on a data warehouse provided in Embodiment 3. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0021] It should be noted that, unless otherwise specified, the various features in the embodiments of this invention can be combined with each other, all of which are within the protection scope of this invention. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than the module division in the device or the order in the flowchart. Moreover, the terms "first," "second," and "third" used in this invention do not limit the data or execution order, but only distinguish identical or similar items with essentially the same function and effect.

[0022] Figure 2 This is a flowchart illustrating a data management method based on a data warehouse, as provided in Example 1. Figure 2 As shown, it includes the following steps: Step 1: Construct a data warehouse, which includes a functional relationship diagram for describing the logical relationships between multiple related functions, a data model for defining parameter characteristics, and a numerical storage area for storing the parameter values ​​corresponding to each function. Step 2: Based on the target task, filter and combine the functions in the functional relationship diagram to construct a target functional map, and configure the parameters of the target functional map based on the data model; Step 3: Load the initial parameter values ​​of the target task into the running memory of the CNC system, and store the other parameter values ​​into the corresponding numerical storage area of ​​the data warehouse; Step 4: Execute the current function based on the parameter values ​​in the running memory. When a function switching instruction is triggered, query the target function map and read the corresponding target parameter values ​​from the data warehouse to replace the original parameter values ​​in the running memory. Repeat this step until the target task is completed.

[0023] The above embodiments provide a data management method based on a data warehouse. It adopts an architecture design of "running memory + data warehouse," loading the current parameter value or set of parameter values ​​into the software memory, while storing the remaining parameter values ​​or sets of parameter values ​​in the data warehouse. When switching functions, the corresponding parameter value is read from the data warehouse and loaded into the running memory to directly replace the original parameter value. Compared with traditional methods, this method uses a data warehouse to manage parameters, which can significantly reduce software memory consumption, significantly improve the memory resource utilization efficiency of the CNC system software, and enhance the flexibility and scalability of data management. Furthermore, the data warehouse has versatility and can be applied to multiple fields, including but not limited to laser processing data management and spatial error compensation data management.

[0024] The present invention will now be described in detail through the data management process in laser processing. Figure 3 This is an overall architecture diagram of the data management method of the present invention in laser processing applications, as shown below. Figure 3 As shown, it includes a laser process management interface layer, a laser process data warehouse, and a laser processing execution layer containing running memory. The running memory pre-stores the main program that characterizes the laser processing process, which can be the main program corresponding to a certain processing step of a product or multiple main programs corresponding to the complete processing steps.

[0025] For example, the laser process management interface in this embodiment includes a spectrum management interface, a data model management interface, and a file management interface. Based on laser processing requirements, the spectrum management interface filters and combines functions from the data warehouse's functional relationship diagram to construct a target functional spectrum (i.e., a laser process spectrum) corresponding to the target task, and configures parameters, specifically configuring control parameters and corresponding parameter values ​​for specific functions (specific laser processing processes). Figure 5a As shown.

[0026] As those skilled in the art will know, the parameter characteristics of the data warehouse, such as processing speed and processing frequency, can also be defined through the data model management interface, maintaining a consistent format, such as... Figure 5b As shown. Simultaneously, task files can be generated through the file management interface, such as historical functional diagrams and parameter value sets during the configuration process of a certain type of workpiece, etc., and batch saving, loading, editing, importing, or exporting of these task files can be performed, thereby improving processing efficiency.

[0027] In a preferred embodiment, the aforementioned laser process data warehouse is implemented using an SQLite3 database, which includes structure tables, model tables, and numerical tables. The structure tables are relatively complex, with each row representing a node. Nodes are connected using hierarchical structures, indexes, and other data connections, forming a complete multi-branch tree structure representing the logical relationships between multiple processing types, processing materials, and / or processing techniques. Then, based on the actual needs of laser processing, multiple target nodes in the structure tables are selected and connected to construct a multi-branch tree structure describing the target laser process map. The leaf nodes of the multi-branch tree structure are then configured with parameters, assigning control parameter values. Figure 4 The image shown is a laser process diagram of a specific embodiment, which can be obtained through, as follows: Figure 5a The graph management interface is used to construct and assign values. The first level represents the processing type, including cutting and drilling; the second level represents the processing material, including steel and aluminum. The leaf nodes represent the specific laser processing technology and are associated with specific data models and parameter values. For example, cutting steel, cutting aluminum, drilling steel, and drilling aluminum can be bound to a set of control parameters and corresponding parameter values, such as cutting speed, cutting frequency, drilling speed, drilling frequency, etc.

[0028] The model table stores the data model for laser processing. A specific implementation example is... Figure 5b As shown, a laser processing data model includes speed and frequency as its laser processing parameters. The speed data model's characteristics and data format are configured as follows: Mapping type - macro variable; Mapping address - #50001; Data type - integer; Default value - 3000; Maximum value - 4000; Minimum value - 2000; Precision - None; Unit - None. The frequency data model's characteristics and data format are configured as follows: Mapping type - macro variable; Mapping address - #50002; Data type - floating point; Default value - 20.0; Maximum value - 50.0; Minimum value - 10.0; Precision - 1; Unit - Hz. This allows for unified configuration of speed, frequency, and other parameters according to these data models, improving system control efficiency.

[0029] As those skilled in the art will know, data tables are used to store process values, i.e., specific control parameter values, which are obtained through methods such as... Figure 5a The interface can be configured to set, for example, the cutting speed of aluminum parts is 4000 and the cutting frequency is 23.3, and the cutting speed of steel parts is 2000 and the cutting frequency is 17.3, all of which meet the format requirements of the above data model.

[0030] Then, the corresponding G-code in the CNC system's running memory is executed to perform the specific laser processing technology. When it is necessary to switch process parameters, a function switching instruction is triggered. At this time, there is no need to modify the G-code; simply running the same G-code will execute the switched process. Internally, the system finds the corresponding multi-branched leaf node in the target laser process map based on the processing technology selected by the user. Then, it reads the data model and data values ​​associated with the leaf node and replaces the original parameter values, thereby greatly reducing software memory consumption and significantly improving the memory resource utilization efficiency of the CNC system software.

[0031] Taking a specific application as an example, we need to process two product models, A1 and A2. Both processes include step a (cutting) and step b (drilling). A1 involves cutting 3mm steel and drilling holes in the cut steel, while A2 involves cutting 3mm aluminum and drilling holes in the cut aluminum. In a specific embodiment, the G-code representing "cutting" and / or "drilling" can be loaded into the runtime memory. A cross-tree structure, i.e., the corresponding target process diagram, can be established based on the product model, etc. Specific control parameters and their corresponding values ​​can be configured for the leaf nodes. For example, the cutting speed and frequency of steel can be configured for the cutting step of A1, and the drilling speed and frequency of steel can be configured for the drilling step of A1. Similarly, the cutting speed and frequency of aluminum can be configured for the cutting step of A2, and the drilling speed and frequency of aluminum can be configured for the drilling step of A2, and so on. Then, based on the processing requirements, the G-codes for "cutting" and "drilling" as well as the complete set of parameter values ​​for model A1 are loaded into the running memory, and the complete set of parameter values ​​for model A2 is saved into the data warehouse. After completing the processing flow of product A1, the process is switched, that is, the parameter values ​​in the data warehouse are called to replace the current parameter values ​​in the running memory, and the processing of product A2 is continued.

[0032] Of course, in other embodiments, only step a of model A1 and the parameter values ​​involved can be placed in the running memory, and the parameter values ​​of model A2 can be placed in the data warehouse. Only this one process step can be processed. The laser process map can be flexibly established and used according to the task requirements and the product processing flow.

[0033] In a preferred embodiment, to further improve processing efficiency, the data management method further includes an extended management step, constructing multiple data warehouses and enabling parameter calls across data warehouses. Specifically, this includes the following steps: Build multiple data warehouses corresponding to different functions, and establish a relationship table between the multiple data warehouses.

[0034] When executing the target task, the target task is split into different task nodes, and the corresponding target data warehouse is queried for each task node to establish a target function map for each task node and configure the parameters. The association table is queried to obtain the target associations of multiple target data warehouses. Steps 3 and 4 are executed sequentially for each task node according to the target associations until the target task is completed.

[0035] Specifically, if the target task includes performing the cutting process for product A and the drilling process for product B, then corresponding data warehouses can be established for product A and product B respectively, and target function maps can be constructed and parameters configured. Then, based on the relationship between the two data warehouses, such as mapping and dependency rules, the control parameter values ​​can be automatically invoked, which not only improves efficiency but also facilitates the management of the data warehouses.

[0036] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0037] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the data management method based on a data warehouse as described above.

[0038] Figure 6 This is a schematic diagram of the structure of the data management system based on a data warehouse provided in Example 2, as shown below. Figure 6 As shown, it includes a first building module 100, a second building module 200, a preloading module 300, and an execution module 400. The first construction module 100 is used to construct a data warehouse, which includes a functional relationship diagram for describing the logical relationship between multiple related functions, a data model for defining parameter characteristics, and a numerical storage area for storing the parameter values ​​corresponding to each function. The second construction module 200 is used to perform functional filtering and combination on the functional relationship diagram according to the target task to construct a target functional map, and to configure the parameters of the target functional map based on the data model; The preloading module 300 is used to load the initial parameter values ​​of the target task into the running memory of the CNC system, and store other parameter values ​​into the corresponding numerical storage area of ​​the data warehouse. The execution module 400 is used to execute the current function based on the parameter values ​​in the running memory. When a function switching instruction is triggered, it queries the target function map and reads the corresponding target parameter values ​​from the data warehouse to replace the original parameter values ​​in the running memory until the target task is completed.

[0039] The above embodiments provide a data management system based on a data warehouse. It adopts an architecture of "running memory + data warehouse," loading the current parameter value or set of parameter values ​​into the software memory, while storing parameters for other functions in the data warehouse. When switching functions, the corresponding parameters are read from the data warehouse and loaded into the running memory to directly replace the original parameters. Compared to traditional methods, this system uses a data warehouse to manage parameters, which can significantly reduce software memory consumption, significantly improve the memory resource utilization efficiency of the CNC system software, and enhance the flexibility and scalability of data management. Furthermore, the data warehouse is versatile and can be applied to multiple fields, including but not limited to laser processing data management and spatial error compensation data management.

[0040] In a preferred embodiment, the CNC system interface includes a map management interface, a data model management interface, and a file management interface. The functional relationship graph can be filtered, combined, and its parameters configured through the graph management interface to construct the target functional graph and parameter values ​​corresponding to the target task. The parameter characteristics of the data warehouse are defined through the data model management interface; The task file is generated through the file management interface, and the task file can be saved, loaded, edited, imported, or exported. The task file includes the target function map and the corresponding parameter values.

[0041] In a preferred embodiment, the data management system further includes an extended management module, specifically: The associated building unit is used to build multiple data warehouses corresponding to different functions and to establish a relationship table between the multiple data warehouses; The configuration unit is used to split the target task into different task nodes when executing the target task, and query the corresponding target data warehouse for each task node to establish a target function map for each task node and configure the parameters. The cross-warehouse management unit is used to query the relationship table to obtain the target relationship of multiple target data warehouses, and to execute the preloading module and the execution module sequentially for each task node according to the target relationship until the target task is completed.

[0042] It should be noted that the foregoing explanation of the data management method embodiments based on data warehouses also applies to the data management system based on data warehouses described above, and will not be repeated here.

[0043] This invention also provides a data warehouse-based data management device, including a computer-readable storage medium and a processor, wherein the processor executes a computer program on the computer-readable storage medium to implement the steps of the data warehouse-based data management method described above.

[0044] Figure 7 This is a schematic diagram of the structure of the data management device based on a data warehouse provided in Embodiment 3 of the present invention, as shown below. Figure 7 As shown, the data management device 8 based on a data warehouse in this embodiment includes: a processor 80, a readable storage medium 81, and a computer program 82 stored in the readable storage medium 81 and executable on the processor 80. When the processor 80 executes the computer program 82, it implements the steps in the various method embodiments described above, for example... Figure 2 The steps shown. Alternatively, when the processor 80 executes the computer program 82, it implements the functions of each module in the above-described device embodiments, for example... Figure 6 The functions of the module shown.

[0045] For example, the computer program 82 may be divided into one or more modules, which are stored in the readable storage medium 81 and executed by the processor 80 to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 82 in the data warehouse-based data management device 8.

[0046] The data warehouse-based data management device 8 may include, but is not limited to, a processor 80 and a readable storage medium 81. Those skilled in the art will understand that... Figure 7 This is merely an example of a data warehouse-based data management device 8 and does not constitute a limitation on the data warehouse-based data management device 8. It may include more or fewer components than shown, or combine certain components, or different components. For example, the data warehouse-based data management device may also include a power management module, a computing processing module, input / output devices, network access devices, buses, etc.

[0047] The processor 80 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0048] The readable storage medium 81 can be an internal storage unit of the data warehouse-based data management device 8, such as a hard drive or memory of the data warehouse-based data management device 8. The readable storage medium 81 can also be an external storage device of the data warehouse-based data management device 8, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the data warehouse-based data management device 8. Furthermore, the readable storage medium 81 can include both internal storage units and external storage devices of the data warehouse-based data management device 8. The readable storage medium 81 is used to store the computer program and other programs and data required by the data warehouse-based data management device. The readable storage medium 81 can also be used to temporarily store data that has been output or will be output.

[0049] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0050] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0051] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0052] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0053] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0054] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0055] The present invention is not limited to the description in the specification and embodiments, and other advantages and modifications can be readily realized by those skilled in the art. Therefore, without departing from the spirit and scope of the general concept as defined by the claims and their equivalents, the present invention is not limited to the specific details, representative devices and illustrated examples shown and described herein.

Claims

1. A data management method based on a data warehouse, applied to a numerical control system, characterized in that, Includes the following steps: Step 1: Construct a data warehouse, which includes a functional relationship diagram for describing the logical relationships between multiple related functions, a data model for defining parameter characteristics, and a numerical storage area for storing the parameter values ​​corresponding to each function. Step 2: Based on the target task, filter and combine the functions in the functional relationship diagram to construct a target functional map, and configure the parameters of the target functional map based on the data model; Step 3: Load the initial parameter values ​​of the target task into the running memory of the CNC system, and store the other parameter values ​​into the corresponding numerical storage area of ​​the data warehouse; Step 4: Execute the current function based on the parameter values ​​in the running memory. When a function switching instruction is triggered, query the target function map and read the corresponding target parameter values ​​from the data warehouse to replace the original parameter values ​​in the running memory. Repeat this step until the target task is completed.

2. The method according to claim 1, characterized in that, It also includes extended management steps, specifically: Build multiple data warehouses for different product types and establish relationship tables between the multiple data warehouses; When executing the target task, the target task is split into different task nodes, and the corresponding target data warehouse is queried for each task node to establish a target function map for each task node and configure the parameters. The association table is queried to obtain the target associations of multiple target data warehouses. Steps 3 and 4 are executed sequentially for each task node according to the target associations until the target task is completed.

3. The method according to claim 1, characterized in that, The data model is used to provide a unified data template, and the data features of the data model include at least one of the following: index, mapping address, data type, data precision, default value, maximum value, minimum value, unit, name, and comments.

4. The method according to claim 1, characterized in that, The functional relationship diagram adopts a multi-branch tree topology structure. Each internal node of the multi-branch tree topology structure represents a functional dimension, and the leaf nodes represent specific functions and are associated with corresponding data models and parameter values. The content of each node includes at least one of the following: level, total level, index, parent node index, level name, node name, and annotation information.

5. The method according to claim 1, characterized in that, The function switching command can be triggered by manually switching through the system interface or by switching via the G command.

6. A data management system based on a data warehouse, based on the method of any one of claims 1-5, characterized in that, It includes a first building module, a second building module, a preloading module, and an execution module. The first construction module is used to build a data warehouse, which includes a functional relationship diagram for describing the logical relationship between multiple related functions, a data model for defining parameter characteristics, and a numerical storage area for storing the parameter values ​​corresponding to each function. The second construction module is used to perform functional filtering and combination on the functional relationship diagram according to the target task to construct a target functional map, and to configure the parameters of the target functional map based on the data model; The preloading module is used to load the initial parameter values ​​of the target task into the running memory of the CNC system, and store other parameter values ​​into the corresponding numerical storage area of ​​the data warehouse. The execution module is used to execute the current function based on the parameter values ​​in the running memory. When a function switching instruction is triggered, it queries the target function map and reads the corresponding target parameter values ​​from the data warehouse to replace the original parameter values ​​in the running memory until the target task is completed.

7. The system according to claim 6, characterized in that, The system interface includes a map management interface, a data model management interface, and a file management interface. The functional relationship graph can be filtered, combined, and its parameters configured through the graph management interface to construct the target functional graph and parameter values ​​corresponding to the target task. The parameter characteristics of the data warehouse are defined through the data model management interface; The task file is generated through the file management interface, and the task file can be saved, loaded, edited, imported, or exported. The task file includes the target function map and the corresponding parameter values.

8. The system according to claim 6, characterized in that, It also includes an extended management module, specifically: The associated building unit is used to build multiple data warehouses corresponding to different functions and to establish a relationship table between the multiple data warehouses; The configuration unit is used to split the target task into different task nodes when executing the target task, and query the corresponding target data warehouse for each task node to establish a target function map for each task node and configure the parameters. The cross-warehouse management unit is used to query the relationship table to obtain the target relationship of multiple target data warehouses, and to execute the preloading module and the execution module sequentially for each task node according to the target relationship until the target task is completed.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the data management method based on a data warehouse as described in any one of claims 1-5.

10. A data management device based on a data warehouse, comprising a computer-readable storage medium and a processor, characterized in that, When the processor executes the computer program on the computer-readable storage medium, it implements the steps of the data management method based on a data warehouse as described in any one of claims 1-5.

Images