Data analysis system of numerical control machining equipment cluster

Abstract

The invention relates to a data analysis system of a numerical control machining equipment cluster. The data analysis system comprises a data acquisition module, a data storage module, a machining program management module, a data analysis module and a fault management module. The data analysis system of the numerical control machining equipment cluster belongs to depth data acquisition, can acquire information such as cutting speed, feeding multiplying power, main shaft power, cutter number, program name, fault code and energy consumption, can truly reflect operation normalization problems such as the cutting speed and the feeding multiplying power through an equipment operation historical slice map, and is high in accuracy and high in reliability. Equipment operation state data and man-hour data are more transparent and accurate. And data support is provided for technical man-hour accounting, capacity verification, production plan scheduling and the like.

Description

Technical Field

[0001] The present invention relates to the field of digital control technology, and in particular to a data analysis system for a cluster of digital control processing equipment. Background Art

[0002] Under the wave of Industry 4.0, the Industrial Internet of Things (IIoT) has become the key to the transformation and upgrading of the manufacturing industry. The manufacturing process of rail passenger car bodies and bogie parts involves the use of CNC machine tools for material removal processing. Especially when facing CNC systems of different brands, models and ages, the wide adaptability of data collection methods, storage modes, and analysis methods will become the core competitiveness.

[0003] As a bridge connecting traditional industrial equipment and intelligent networks, the importance of data acquisition modules is self-evident. The communication protocols used by different CNC systems may vary greatly, from old serial communication protocols to modern Ethernet protocols, requiring acquisition modules to be seamlessly connected to ensure accurate data collection and transmission. Currently, data acquisition in CNC machine tool clusters mostly collects signals based on current changes, and can only collect three data: equipment power-on time, start-up time, and processing time. Due to the limited amount of collected data, it is impossible to conduct in-depth mining of data value. The other type of acquisition based on independent unit mode can meet basic monitoring needs, but there is an obvious problem of data isolation, and the information interaction between different devices is limited, which is not conducive to large-scale data analysis and comprehensive resource scheduling.

[0004] In terms of data storage, existing systems mostly use local storage as the main method and lack an effective cloud collaboration platform. This means that long-term data storage and cross-device query become complicated and inefficient. In addition, at the data management level, there is a lack of unified standards and interfaces, making it difficult to achieve efficient data exchange and sharing.

[0005] The configuration and management of machining programs also face difficulties under the traditional system. The correspondence between machining programs and materials still relies on technicians to fill out forms offline, and configuration management is difficult to solidify; program version management lacks information tracing methods; and program transmission still uses manual information transfer from computers or service areas to machine tools via USB flash drives. Ultimately, it is difficult to form clear quality traceability.

[0006] The data analysis tools in traditional CNC systems are relatively simple, mainly focusing on the analysis of indicators of machine operation rate and operation rate and the generation of basic statistical reports. Faced with increasingly complex manufacturing needs, such tools seem to be unable to cope with the situation, especially in terms of predictive maintenance and performance optimization. Summary of the invention

[0007] The present invention aims to solve the technical problems in the prior art and provides a data analysis system for a CNC machining equipment cluster.

[0008] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0009] A data analysis system for a CNC machining equipment cluster, comprising: a data acquisition module, a data storage module, a machining program management module, a data analysis module and a fault management module;

[0010] The data acquisition module is connected to the CNC machining equipment end and is used to collect the operation data of the equipment end, including: program name, speed, feed, ratio, spindle power, equipment status, fault code, etc. The acquisition module has a temporary storage function, which is used to provide historical data supplement function when the network is temporarily interrupted or the transmission fails;

[0011] The data storage module is used to store collected data. It is located on the cloud server side and is used to build the original database for equipment operation. The database table fields are uniformly stored in VARCHAR type. When performing business operations, they are converted and supplemented into the required data type according to business requirements.

[0012] The processing program management module is used for program list management, program version management and program transmission management. It uses a file receiving device with network function to connect to the CNC processing equipment, and has the functions of remotely receiving programs issued by the server, and supporting remote viewing, deletion and return services. It is also used for program list management corresponding to the CNC program folder name and material number, and version management for each program folder. The reasons for program folder upgrades are selected or combined according to the two reasons of drawing change and process optimization. Drawing changes require filling in the change notification number, and process optimization requires description of the optimization content.

[0013] The data analysis module is used for real-time data display, historical data analysis, and threshold indicator data alarms;

[0014] The fault management module is used for fault statistical analysis and predictive maintenance, and provides analysis methods for sorting by fault code frequency, fault code duration, and fault code downtime.

[0015] In the above technical solution, a data dictionary is provided in the data storage module;

[0016] The data dictionary is used to build structured storage tags for data with different variable names and different usage meanings, and to set key fields such as parameter identification, parameter description, device identification, parameter channel category, etc., in preparation for new parameter expansion for different devices.

[0017] In the above technical solution, the data storage module has a downsampling configuration function for the acquisition parameters. For unnecessary variables, the system will only extract the parameters configured by the application after configuring the device acquisition parameters. Other unnecessary variables will still be retained in the time series database. The storage frequency of each parameter can be configured in the system. The system will use the configured storage frequency for downsampling processing during the original data cleaning process, thereby reducing the amount of calculation data during business applications and improving calculation efficiency.

[0018] In the above technical solution, in the fault management module, the fault code dictionary integrates dual data management of the equipment diagnostic manual and the daily maintenance experience of the equipment maintenance engineer.

[0019] In the above technical solution, a file transceiver is provided in the processing program management module. One end of the file transceiver is connected to the CNC processing equipment, and the other end is connected to the cloud server through the workshop's 5G network. It is responsible for packaging and unpacking program files. The processing process includes the following steps:

[0020] (1) Create or update the program list, upload the new program folder to the cloud server, and automatically package it;

[0021] (2) Sending the server program to the data transceiver device of the designated device;

[0022] (3) Receiving data: the file parsing module receives data transmitted from the server through a network connection;

[0023] (4) Decompressing data: the file parsing module will decompress the data using a compression algorithm; the decompression operation will restore the original processing program file and ensure the integrity of the file;

[0024] (5) Data verification: after decompression, the file parsing module performs data verification to ensure that the decompressed file is consistent with the original file;

[0025] (6) Remote program transmission.

[0026] In the above technical solution, the program remote return specifically includes the following steps:

[0027] (6.1) Issue a return instruction on the server side;

[0028] (6.2) Select the program folder to be returned;

[0029] (6.3) Compress the program folder and transmit it back to the cloud server via the 5G network;

[0030] (6.4) Download the feedback program or use the feedback program to directly trigger the version upgrade.

[0031] In the above technical solution, the data analysis module is used to refresh the system page every 10 seconds and display the device status in real time, starting from the current time of selecting the device to view.

[0032] In the above technical solution, the display data showing the device status in real time is as follows:

[0033] The real-time status indicator of the device is: dark green for program running, light green for MDA running, orange for program pause, and yellow for idle.

[0034] The data displayed on the device screen: program name, pre-read statements, cutting speed, tool number;

[0035] The three-phase electricity line chart shows: the horizontal axis is the time axis, and the vertical axis is the three-phase voltage and three-phase current;

[0036] The spindle power curve shows: the horizontal axis is the time axis, and the vertical axis is the spindle power value;

[0037] Tool tip spatial three-coordinate point position: construct three-dimensional space with the equipment's X, Y, and Z axis travel, and collect the current X, Y, and Z axis data point coordinates to drive the tool tip point to move in three-dimensional space.

[0038] In the above technical solution, the data analysis module is used to construct a single-day historical data slice diagram with equipment status data and real-time data, and dynamically display the program name and cutting parameter data at the position pointed by the mouse pointer.

[0039] In the above technical solution, the data analysis module is also used to construct a single-day historical data slice chart with the equipment status data and real-time data, and dynamically display the program name and cutting parameter data at the position pointed by the mouse pointer, which specifically includes the following steps:

[0040] (1) The time period with fault codes in RES is selected as the fault downtime state, marked in red. The rest of the RES states are the equipment idle state, marked in yellow, and the shutdown state is marked in gray;

[0041] (2) The remaining program running status is marked in dark green, the MDA running status is marked in light green, and the program pause status is marked in orange;

[0042] (3) Displaying a slice diagram with time as the horizontal axis and vertical axis, and displaying cutting parameters above the slice diagram, including spindle speed, speed ratio, cutting feed, feed ratio, and spindle power curve, with a display or hide button set above the cutting parameters;

[0043] (4) The mouse pointer moves to dynamically display the current program name, fault code, and actual data values ​​of spindle speed, speed multiplier, cutting feed, feed multiplier, and spindle power;

[0044] (5) Use the mouse wheel to control the zooming in and out of the slice diagram and cutting parameter curve.

[0045] The present invention has the following beneficial effects:

[0046] The data analysis system of the numerical control processing equipment cluster of the present invention is in terms of equipment performance: the performance management platform in the prior art can only collect three data: equipment power-on time, start-up time, and processing time. The technical solution of the present invention belongs to deep data collection. On the basis of the original three times, it can also collect cutting speed, feed rate, spindle power, tool number, program name, etc. Through the equipment operation history slice chart, it can truly reflect the operational normative issues such as cutting speed and feed rate. The equipment operation status data and working time data are more transparent and accurate. Provide data support for technical working time accounting, capacity verification, production planning and scheduling, etc.

[0047] In terms of program management, the data analysis system of the CNC machining equipment cluster of the present invention has the following aspects: program list management and version management are the main audit items in the 22163 system review. The technical solution of the present invention realizes the strong correlation between material number and program name, and the strong correlation between program version and engineering change / process optimization, which greatly standardizes the management of machining programs, ensures the consistency between the equipment execution program and the process planning version, and eliminates the risk of virus transmission through external USB flash drives.

[0048] The data analysis system of the CNC processing equipment cluster of the present invention is in terms of program transmission: the aluminum alloy body processing CNC equipment in the prior art is distributed in multiple sites, and the layout of the equipment is scattered. It takes a lot of time and energy for the process engineer to go to one of the sites. Due to the need for trial production and changes and upgrades of new products, the process engineer needs to frequently run around in different factories and different workstations to adjust the program. By implementing the technical solution of the present invention, a data management platform is established, one end is connected to the process network, and the other end is connected to the equipment network. Through 5G, remote data communication is achieved, the program goes directly to the workstation, and the program modified by the production line is directly returned to the process engineer, which greatly improves work efficiency and saves a lot of non-value-added work time for the process engineer.

[0049] The data analysis system of the CNC machining equipment cluster of the present invention has the following advantages in terms of fault handling: through the real-time collection of equipment fault codes, it is currently possible to sort by multiple dimensions such as fault code duration, frequency of occurrence, fault downtime, etc., while at the same time achieving the interconnection between the fault code and the equipment diagnostic electronic manual, so that the handling method can be viewed online in real time.

[0050] In terms of energy / carbon emission management, the data analysis system of the CNC machining equipment cluster of the present invention has established production line group level, production line level, and key equipment power data monitoring statistics and threshold warning mechanisms, providing basic data and a carrier platform for energy optimization management and carbon emission accounting. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0052] Figure 1 It is a structural schematic diagram of the data analysis system of the CNC machining equipment cluster of the present invention.

[0053] Figure 2 It is a structural schematic diagram of a data acquisition module in the data analysis system of the numerical control processing equipment cluster of the present invention.

[0054] Figure 3 It is a structural schematic diagram of a file receiving and sending device in a data analysis system of a numerical control processing equipment cluster of the present invention. DETAILED DESCRIPTION

[0055] The present invention is described in detail below with reference to the accompanying drawings.

[0056] like Figure 1 As shown, the data analysis system of the CNC machining equipment cluster of the present invention includes: a data acquisition module, a data storage module, a machining program management module, a data analysis module and a fault management module. Among them:

[0057] The data acquisition module is connected to the CNC machining equipment end and is used to collect the equipment end operation data, such as program name, speed, feed, ratio, spindle power, equipment status and other data;

[0058] like Figure 2As shown, the data acquisition module includes: MPI protocol conversion hardware, data processing module, data cache module, data cleaning module and 5G network module. The MPI protocol conversion hardware is responsible for converting the specific protocol used on the MPI bus to the protocol used on the Ethernet; the data processing module communicates with the processing equipment NCU, intercepts data from the MPI communication bus of the NCU machine tool, and performs data processing functions such as data packet parsing, checksum calculation, data reorganization, etc. These functions help to ensure the integrity and accuracy of the data; the data cache module includes a data buffer or cache memory for temporary storage and management of data, providing fast data reading and writing operations, and can optimize data storage and management according to system requirements; the data cleaning module includes a data preprocessing algorithm, a filter and a data verification mechanism for cleaning, filtering and verifying the received data, correcting errors, and removing abnormal data to ensure the accuracy and integrity of the data; the 5G network module is responsible for processing the communication and interaction with the 5G network, including data transmission, connection management, signaling processing and other functions. These modules together constitute a complete data transmission system that can realize data parsing, storage, processing and transmission, and is used to upload the network data after data clarity to the Internet of Things platform.

[0059] The data storage module is used to store the collected data and is located on the cloud server. The data storage module is provided with a data dictionary.

[0060] The data storage module adopts the front-end server-back-end server mode. The front-end server CPU and memory use high configuration, which is responsible for running the MQTT service and receiving data intermediate files to avoid competing with the application part for CPU, IO and memory resources. After receiving the real-time data of the device in the front-end server, the real-time data of the device in the cache Redis is updated at the same time, and another thread is used to save the data to the back-end server. The back-end server uses the Influx DB time series database.

[0061] The data storage module is used to provide historical data supplementation function when the original data is lost. The device records the data that failed to be transmitted and supplements the data that failed to be transmitted at a regular interval. The system supplements the lost historical data into the original data.

[0062] The data storage module is used to uniformly store the database table fields in VARCHAR type when some parameters collected during the device collection and transmission process are null, NaN, nill or empty. When performing business operations, they are converted and supplemented into the required data type according to business requirements.

[0063] The data dictionary is used to build structured storage tags for data with different variable names and different usage meanings, and set key fields such as parameter identification, parameter description, device identification, parameter channel category, etc., in preparation for the expansion of new parameters for different devices. For example, for voltage, the parameter identification can be defined as voltage in the system, and then this parameter can be added to the device acquisition parameter list in the device management configuration, and the actual parameter name of the device can be entered in its device identification.

[0064] The data storage module has the function of downsampling configuration of acquisition parameters. For unnecessary variables, the system will only extract the parameters configured by the application after configuring the device acquisition parameters. Other unnecessary variables will still remain in the time series database. The storage frequency of each parameter can be configured in the system. The system will use the configured storage frequency for downsampling processing during the original data cleaning process, thereby reducing the amount of calculation data during business applications and improving calculation efficiency.

[0065] The processing program management module has the functions of program list management, program version management and program transmission management. It uses a file receiving device with network function to connect to the equipment. It has the functions of remotely receiving programs issued by the server and supporting remote viewing, deletion and return of services. Specifically, the processing program management module is used to manage the program list corresponding to the CNC program folder name and material number, and to perform version management on each program folder. The reasons for upgrading the program folder are selected or combined according to the two reasons of drawing change and process optimization. Drawing changes require filling in the change notification number, and process optimization requires describing the optimization content.

[0066] The processing program management module is equipped with a file sending and receiving device, such as Figure 3 As shown, one end of the file transceiver is connected to the U port of the CNC equipment, and the other end is connected to the cloud server through the 5G network of the workshop. The file transceiver is responsible for packaging and unpacking program files. The processing process includes the following steps:

[0067] (1) Create or update the program list, upload the new program folder to the cloud server, and automatically package it;

[0068] (2) Sending the server program to the data transceiver device of the designated device;

[0069] (3) Receiving data: the file parsing module receives data transmitted from the server through a network connection;

[0070] (4) Decompress the data. The file parsing module will decompress the data using a compression algorithm (such as ZIP or GZIP). The decompression operation will restore the original processing program file and ensure the integrity of the file;

[0071] (5) Data verification: After decompression, the file parsing module will perform data verification to ensure that the decompressed file is consistent with the original file. Common data verification methods include checksum, hash verification (such as MD5 or SHA), etc.

[0072] (6) The remote transmission of the program includes the following steps:

[0073] (6.1) Issue a return instruction on the server side;

[0074] (6.2) Select the program folder to be returned

[0075] (6.3) Compress the program folder and transmit it back to the cloud server via the 5G network

[0076] (6.4) Download the feedback program or use the feedback program to directly trigger the version upgrade.

[0077] The file receiving and sending device in the processing program management module has the functions of data exception handling, remote viewing and remote deletion. Data exception handling refers to the exceptions that occur during the decompression or data verification process. The file parsing module will handle these exceptions and send error information to the operator or remote server to ensure that the data is not lost or damaged during the file transmission process in the case of network fluctuations; the remote viewing function allows the operator to remotely access the receiving device to view the currently stored processing program files for operation and monitoring; the remote server is allowed to send deletion instructions and delete the processing program files that have been received.

[0078] Data analysis module, including: real-time data display, historical data analysis and threshold indicator data alarm functions;

[0079] The data analysis module is used to select the current time of the device as the starting time, refresh the system page every 10 seconds, and display the device status in real time. The displayed data is as follows:

[0080] The real-time status indicator of the equipment is: dark green for program running (CYC_START), light green for MDA running (CYC_SOTP), orange for program pause (HOLD), and yellow for idle (RES);

[0081] The data displayed on the device screen: program name, pre-read statements, cutting speed, tool number;

[0082] The three-phase electricity line chart shows: the horizontal axis is the time axis, and the vertical axis is the three-phase voltage and three-phase current;

[0083] The spindle power curve shows: the horizontal axis is the time axis, and the vertical axis is the spindle power value;

[0084] Tool tip spatial three-coordinate point position: construct three-dimensional space with the equipment's X, Y, and Z axis travel, and collect the current X, Y, and Z axis data point coordinates to drive the tool tip point to move in three-dimensional space.

[0085] The data analysis module is also used to construct a single-day historical data slice chart with equipment status data and real-time data, and dynamically display the program name and cutting parameter data at the location pointed by the mouse pointer. Specifically, the following steps are included:

[0086] (1) The time period with fault codes in RES is selected as the fault downtime state, marked in red. The rest of the RES states are the equipment idle state, marked in yellow, and the shutdown state is marked in gray;

[0087] (2) The remaining program running (CYC_START) status is marked in dark green, the MDA running status is marked in light green (CYC_SOTP), and the program pause (HOLD) status is marked in orange;

[0088] (3) Displaying a slice diagram with time as the horizontal axis and vertical axis, and displaying cutting parameters above the slice diagram, including spindle speed, speed ratio, cutting feed, feed ratio, spindle power curve, and setting a display / hide button above the cutting parameters;

[0089] (4) The mouse pointer moves to dynamically display the current program name, fault code, and actual data values ​​of spindle speed, speed multiplier, cutting feed, feed multiplier, and spindle power;

[0090] (5) Use the mouse wheel to control the zooming in and out of the slice diagram and cutting parameter curve.

[0091] The fault management module is used for fault statistical analysis and predictive maintenance, and provides a variety of analysis methods such as sorting by fault code frequency, sorting by fault code duration, sorting by fault code downtime, etc. The fault management module includes: fault code dictionary; the fault code dictionary integrates dual data management of equipment diagnostic manual and daily maintenance experience of equipment maintenance engineers.

[0092] The data analysis system of the numerical control processing equipment cluster of the present invention is in terms of equipment performance: the performance management platform in the prior art can only collect three data: equipment power-on time, start-up time, and processing time. The technical solution of the present invention belongs to deep data collection. On the basis of the original three times, it can also collect cutting speed, feed rate, spindle power, tool number, program name, etc. Through the equipment operation history slice chart, it can truly reflect the operational normative issues such as cutting speed and feed rate. The equipment operation status data and working time data are more transparent and accurate. Provide data support for technical working time accounting, capacity verification, production planning and scheduling, etc.

[0093] In terms of program management, the data analysis system of the CNC machining equipment cluster of the present invention has the following aspects: program list management and version management are the main audit items in the 22163 system review. The technical solution of the present invention realizes the strong correlation between material number and program name, and the strong correlation between program version and engineering change / process optimization, which greatly standardizes the management of machining programs, ensures the consistency between the equipment execution program and the process planning version, and eliminates the risk of virus transmission through external USB flash drives.

[0094] The data analysis system of the CNC processing equipment cluster of the present invention is in terms of program transmission: the aluminum alloy body processing CNC equipment in the prior art is distributed in multiple sites, and the layout of the equipment is scattered. It takes a lot of time and energy for the technician to go to one of the sites. Due to the need for trial production and changes and upgrades of new products, the technician needs to frequently run around in different factories and different workstations to adjust the program. By implementing the technical solution of the present invention, a data management platform is established, one end is connected to the process network, and the other end is connected to the equipment network. Through 5G, remote data communication is achieved, the program goes directly to the workstation, and the program modified by the production line is directly returned to the technician, which greatly improves work efficiency and saves a lot of non-value-added work time for the technician.

[0095] The data analysis system of the CNC machining equipment cluster of the present invention has the following advantages in terms of fault handling: through the real-time collection of equipment fault codes, it is currently possible to sort by multiple dimensions such as fault code duration, frequency of occurrence, fault downtime, etc., while at the same time achieving the interconnection between the fault code and the equipment diagnostic electronic manual, so that the handling method can be viewed online in real time.

[0096] In terms of energy / carbon emission management, the data analysis system of the CNC machining equipment cluster of the present invention has established production line group level, production line level, and key equipment power data monitoring statistics and threshold warning mechanisms, providing basic data and a carrier platform for energy optimization management and carbon emission accounting.

[0097] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A data analysis system for a CNC machining equipment cluster, characterized in that: include: Data acquisition module, data storage module, processing program management module, data analysis module and fault management module; The data acquisition module is connected to the CNC machining equipment end and is used to collect the operation data of the equipment end, including: program name, speed, feed, magnification, spindle power, equipment status, fault code, etc. The acquisition module has a temporary storage function, which is used to provide historical data supplement function when the network is temporarily interrupted or the transmission fails; The data storage module is used to store collected data. It is located on the cloud server side and is used to build the original database for equipment operation. The database table fields are uniformly stored in VARCHAR type. When performing business operations, they are converted and supplemented into the required data type according to business requirements. The processing program management module is used for program list management, program version management and program transmission management. It uses a file receiving device with network function to connect to the CNC processing equipment, and has the functions of remotely receiving programs issued by the server, and supporting remote viewing, deletion and return services. It is also used for program list management corresponding to the CNC program folder name and material number, and version management for each program folder. The reasons for program folder upgrades are selected or combined according to the two reasons of drawing change and process optimization. Drawing changes require filling in the change notification number, and process optimization requires description of the optimization content. The data analysis module is used for real-time data display, historical data analysis, and threshold indicator data alarms; The fault management module is used for fault statistical analysis and predictive maintenance, and provides analysis methods for sorting by fault code frequency, fault code duration, and fault code downtime.

2. The data analysis system for a CNC machining equipment cluster according to claim 1, characterized in that: The data storage module is provided with a data dictionary; The data dictionary is used to build structured storage tags for data with different variable names and different usage meanings, and to set key fields such as parameter identification, parameter description, device identification, parameter channel category, etc., in preparation for new parameter expansion for different devices.

3. The data analysis system for a CNC machining equipment cluster according to claim 1, characterized in that: The data storage module has the function of downsampling configuration of acquisition parameters. For unnecessary variables, the system will only extract the parameters configured by the application after configuring the device acquisition parameters. Other unnecessary variables will remain in the time series database. The storage frequency of each parameter is configured in the system. The system will use the configured storage frequency for downsampling processing during the original data cleaning process, thereby reducing the amount of calculation data during business applications and improving calculation efficiency.

4. The data analysis system for a CNC machining equipment cluster according to claim 1, characterized in that: In the fault management module, the fault code dictionary integrates dual data management of the equipment diagnostic manual and the daily maintenance experience of equipment maintenance engineers.

5. The data analysis system for a CNC machining equipment cluster according to claim 1, characterized in that: The processing program management module is equipped with a file transceiver. One end of the file transceiver is connected to the CNC processing equipment, and the other end is connected to the cloud server through the workshop's 5G network. It is responsible for packaging and unpacking program files. The processing process includes the following steps: (1) Create or update the program list, upload the new program folder to the cloud server, and automatically package it; (2) Sending the server program to the data transceiver device of the designated device; (3) Receiving data: the file parsing module receives data transmitted from the server through a network connection; (4) Decompressing data: the file parsing module will decompress the data using a compression algorithm; the decompression operation will restore the original processing program file and ensure the integrity of the file; (5) Data verification: after decompression, the file parsing module performs data verification to ensure that the decompressed file is consistent with the original file; (6) Remote program transmission.

6. The data analysis system for a CNC machining equipment cluster according to claim 5, characterized in that: The program remote transmission specifically includes the following steps: (6.1) Issue a return instruction on the server side; (6.2) Select the program folder to be returned; (6.3) Compress the program folder and transmit it back to the cloud server via the 5G network; (6.4) Download the feedback program or use the feedback program to directly trigger the version upgrade.

7. The data analysis system for a CNC machining equipment cluster according to claim 1, characterized in that: The data analysis module is used to refresh the system page every 10 seconds and display the device status in real time, starting from the current time of the selected device.

8. The data analysis system for a CNC machining equipment cluster according to claim 7, characterized in that: The display data of real-time display device status is as follows: The real-time status indicator of the device is: dark green for program running, light green for MDA running, orange for program pause, and yellow for idle. The data displayed on the device screen: program name, pre-read statements, cutting speed, tool number; The three-phase electricity line chart shows: the horizontal axis is the time axis, and the vertical axis is the three-phase voltage and three-phase current; The spindle power curve shows: the horizontal axis is the time axis, and the vertical axis is the spindle power value; Tool tip spatial three-coordinate point position: construct three-dimensional space with the equipment's X, Y, and Z axis travel, and collect the current X, Y, and Z axis data point coordinates to drive the tool tip point to move in three-dimensional space.

9. The data analysis system for a CNC machining equipment cluster according to claim 1, characterized in that: The data analysis module is used to construct a single-day historical data slice chart with equipment status data and real-time data, and dynamically display the program name and cutting parameter data at the location pointed by the mouse pointer.

10. The data analysis system for a CNC machining equipment cluster according to claim 9, characterized in that: The data analysis module is also used to construct a single-day historical data slice chart with equipment status data and real-time data, and dynamically display the program name and cutting parameter data at the location pointed by the mouse pointer. Specifically, the following steps are included: (1) The time period with fault codes in RES is selected as the fault downtime state, marked in red. The rest of the RES states are the equipment idle state, marked in yellow, and the shutdown state is marked in gray; (2) The remaining program running status is marked in dark green, the MDA running status is marked in light green, and the program pause status is marked in orange; (3) Displaying a slice diagram with time as the horizontal axis and vertical axis, and displaying cutting parameters above the slice diagram, including spindle speed, speed ratio, cutting feed, feed ratio, and spindle power curve, with a display or hide button set above the cutting parameters; (4) The mouse pointer moves to dynamically display the current program name, fault code, and actual data values ​​of spindle speed, speed multiplier, cutting feed, feed multiplier, and spindle power; (5) Use the mouse wheel to control the zooming in and out of the slice diagram and cutting parameter curve.

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