Parameter identification system based on cloud computing numerical control machine tool equipment

Abstract

The invention discloses a parameter identification system based on cloud computing numerical control machine tool equipment, and relates to the technical field of numerical control machine tools, the parameter identification system comprises a data acquisition module, a data processing module, a cloud computing module, a parameter identification module, a control module, a fault analysis and optimization module and a user interface module, the system has the advantages that operation parameters of numerical control machine tool equipment are collected in real time through the data collection module, the accuracy and consistency of data are ensured through cleaning and normalization processing of the data processing module, and an equipment parameter recognition model is established through the cloud computing module by utilizing a machine learning algorithm; the key parameters of the equipment can be accurately identified, and the running state of the equipment can be analyzed in real time through the parameter identification module, so that potential abrasion or fault problems of the equipment can be found in time, measures can be taken in advance, shutdown caused by excessive abrasion or sudden faults of the equipment is avoided, and the running efficiency and the machining precision of the equipment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of numerical control machine tools, and in particular to a parameter identification system for numerical control machine tool equipment based on cloud computing. Background Art

[0002] In modern manufacturing, CNC machine tools are high-precision and high-efficiency processing equipment, and their operating status directly affects product quality and production efficiency. However, with the widespread application and increasing complexity of CNC machine tools, the operation management and maintenance of equipment face many challenges. Traditional CNC machine tool monitoring and troubleshooting methods often rely on manual experience and regular maintenance. This method is not only inefficient, but also difficult to detect and solve potential wear or failure problems of equipment in a timely manner, which can easily lead to extended equipment downtime, reduced processing accuracy, and even safety accidents. To this end, we propose a parameter identification system for CNC machine tools based on cloud computing. Summary of the invention

[0003] The object of the present invention is to provide a parameter identification system for CNC machine tool equipment based on cloud computing.

[0004] In order to solve the problems raised in the above background technology, the present invention provides the following technical solutions: a parameter identification system for CNC machine tool equipment based on cloud computing, comprising the following modules:

[0005] A data acquisition module, wherein the data acquisition module is used to collect operating parameters of CNC machine tools;

[0006] A data processing module, which is used to clean and normalize the data transmitted by the data acquisition module;

[0007] A cloud computing module, which is used to store the collected CNC machine tool data in a cloud database and use a machine learning algorithm to establish an equipment parameter identification model;

[0008] A parameter identification module, which is used to apply the trained parameter identification model to the newly collected CNC machine tool data, identify the key parameters of the equipment, and analyze the operating status of the equipment;

[0009] A control module, the control module is used to send control instructions to corresponding devices in the CNC machine tool according to the analysis results of the parameter identification module;

[0010] A fault analysis and optimization module, which is used to predict the time and type of fault occurrence and formulate an optimization plan;

[0011] A user interface module is used to display data of CNC machine tool equipment parameters to the user.

[0012] As a further solution of the present invention: the data acquisition module includes a sensor unit, a marking unit and a data transmission unit. The sensor unit is used to collect various sensor data installed on the CNC machine tool equipment, the marking unit is used to mark the CNC machine tool equipment corresponding to the data, and the data transmission unit is used to transmit the data collected by the sensor unit to the data processing module by wireless transmission.

[0013] As a further solution of the present invention: the data processing module includes a data cleaning unit and a normalization unit, the data cleaning unit is used to remove noise, abnormal values ​​and erroneous data in the collected data, and the normalization unit is used to normalize the cleaned data.

[0014] As a further solution of the present invention: the cloud computing module includes a data storage unit and a model training unit, the data storage unit is used to store the collected data in a cloud database, the cloud database is divided into three data areas: an original data area, a processed data area and a simulation data area, the original data area is used to store unprocessed original data collected from CNC machine tools, the processed data area is used to store data cleaned and normalized by the data processing module, the model training unit will extract the data in the processed data area in the cloud database, and use a machine learning algorithm to establish a device parameter identification model, and the simulation data area is used to store the output results of the machine learning model.

[0015] As a further solution of the present invention: the parameter identification module includes a model application unit and a state analysis unit. The model application unit is used to apply the trained parameter identification model to the newly collected CNC machine tool data to identify the key parameters of the equipment. The key parameter of the equipment is the wear rate of the tool. The state analysis unit will analyze the operating state of the equipment according to the identified wear rate of the tool. The specific formula is as follows:

[0016]

[0017] Among them, K represents the wear rate of the current tool, L1 represents the current tool diameter, L0 represents the initial tool diameter, when K<0.1, it means that the current tool wear is in a normal state, when 0.1≤K<0.2, it means that the current tool has a wear problem and a tool replacement plan needs to be arranged, and when K≥0.2, it means that the current tool has been severely worn and needs to be shut down for maintenance.

[0018] As a further solution of the present invention: the control module includes an instruction generation unit and an instruction transmission unit. The instruction generation unit will generate corresponding control instructions according to the analysis results of the parameter identification module, and the instruction transmission unit will transmit the generated control instructions to the corresponding equipment in the CNC machine tool.

[0019] As a further solution of the present invention: the fault analysis and optimization module includes a fault prediction unit and an optimization scheme formulation unit. The fault analysis unit collects historical operating data of various parameters of CNC machine tool equipment, as well as past fault records and maintenance information from the cloud database, and associates different types of faults with corresponding parameter changes to establish a fault feature database. At the same time, an abnormality detection algorithm is used to identify abnormal conditions in equipment operation. When an abnormal condition is detected, the fault analysis unit further analyzes the abnormal characteristics and severity, and generates a prediction result based on the abnormal characteristics and severity. The abnormal characteristics are the duration of the abnormality and the size of the abnormal value. After receiving the prediction result from the fault prediction unit, the optimization scheme formulation unit will evaluate the fault. The evaluation content includes the possibility, severity, and impact of the fault on equipment performance and production. According to the type of fault and the evaluation result, a corresponding optimization scheme is formulated and transmitted to the control module.

[0020] As a further solution of the present invention: the optimization plan formulated by the optimization plan formulation unit comes from an optimization plan database, and the optimization plan database is established based on historical fault records and manufacturer fault test records. A cost calculation unit is inserted into the optimization plan formulation unit, and the cost calculation unit calculates the price cost and time cost of the optimization plan.

[0021] As a further solution of the present invention: the user interface module includes a data display unit and an interaction unit. The data display unit will display the data of the CNC machine tool equipment parameters to the user in the form of a chart. The interaction unit is used for the user to query the historical data of the equipment, set parameters and view fault analysis results through the interface. At the same time, the system can also send notifications and alarm information to the user through the interface. The data display unit can intelligently adjust the content and form of data display according to the user's browsing history and focus. At the same time, the arrangement order of the chart data is arranged according to the number of clicks of the user. For abnormal data, the data display unit will mark and move the data to the first row for display.

[0022] By adopting the above technical solution, compared with the prior art, the beneficial effects of the present invention are:

[0023] 1. The present invention collects the operating parameters of the CNC machine tool equipment in real time through the data acquisition module, and after cleaning and normalization processing by the data processing module, the accuracy and consistency of the data are ensured. The equipment parameter identification model is established by the cloud computing module using the machine learning algorithm, which can accurately identify the key parameters of the equipment, and the operating status of the equipment is analyzed in real time through the parameter identification module, which helps to timely discover potential wear or failure problems of the equipment, so as to take measures in advance to avoid the equipment from being shut down due to excessive wear or sudden failure, thereby improving the operating efficiency and processing accuracy of the equipment;

[0024] 2. The present invention collects historical operation data and fault records through the fault analysis and optimization module, establishes a fault feature database, and uses an abnormality detection algorithm to monitor the operating status of the equipment in real time. Once an abnormality is detected, it can quickly predict the time and type of the fault, and formulate an optimization plan based on the prediction result. The optimization plan formulation unit not only considers the type and severity of the fault, but also evaluates the price cost and time cost of different optimization plans through the cost calculation unit, so as to select the optimal plan, which helps enterprises to prevent faults, reduce downtime, reduce maintenance costs, and improve overall production efficiency and economic benefits;

[0025] 3. The present invention introduces an intelligent data priority display function in the user interface module. The system can intelligently adjust the content and form of data display according to the user's browsing history and focus. In particular, the arrangement order of chart data is dynamically adjusted according to the number of clicks of the user, and the data that the user pays most attention to or views most frequently is placed in a prominent position. For abnormal data, the system will not only automatically mark it, but also move the data to the first row for display, ensuring that the user can notice potential problems at the first time. This intelligent data priority display mechanism improves the level of personalization of the user experience, allowing users to obtain key information more quickly, thereby more effectively monitoring and troubleshooting equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Schematic diagram of the parameter identification system flow in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0028] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0029] Please see attached Figure 1 The present invention provides a parameter identification system for CNC machine tool equipment based on cloud computing, comprising the following modules:

[0030] Data acquisition module, which is used to collect the operating parameters of CNC machine tools;

[0031] A data processing module, which is used to clean and normalize the data transmitted by the data acquisition module;

[0032] Cloud computing module: The cloud computing module is used to store the collected CNC machine tool data in the cloud database and use machine learning algorithms to establish equipment parameter identification models;

[0033] Parameter identification module, which is used to apply the trained parameter identification model to the newly collected CNC machine tool data, identify the key parameters of the equipment, and analyze the operating status of the equipment;

[0034] A control module, which is used to send control instructions to corresponding devices in the CNC machine tool according to the analysis results of the parameter identification module;

[0035] Fault analysis and optimization module: The fault analysis and optimization module is used to predict the time and type of faults and develop optimization plans;

[0036] The user interface module is used to display the data of CNC machine tool equipment parameters to the user.

[0037] In one embodiment of the present invention: the data acquisition module includes a sensor unit, a marking unit and a data transmission unit, the sensor unit is used to collect various sensor data installed on the CNC machine tool equipment, the marking unit is used to mark the CNC machine tool equipment corresponding to the data, and the data transmission unit is used to transmit the data collected by the sensor unit to the data processing module by wireless transmission.

[0038] In one embodiment of the present invention: the data processing module includes a data cleaning unit and a normalization unit, the data cleaning unit is used to remove noise, abnormal values ​​and erroneous data in the collected data, and the normalization unit is used to normalize the cleaned data.

[0039] In one embodiment of the present invention: the cloud computing module includes a data storage unit and a model training unit. The data storage unit is used to store the collected data in a cloud database. The cloud database is divided into three data areas: an original data area, a processed data area, and a simulation data area. The original data area is used to store the unprocessed original data collected from the CNC machine tool equipment. The processed data area is used to store the data cleaned and normalized by the data processing module. The model training unit will extract the data in the processed data area in the cloud database, and use the machine learning algorithm to establish a device parameter recognition model. The simulation data area is used to store the output results of the machine learning model.

[0040] In one embodiment of the present invention: the parameter identification module includes a model application unit and a state analysis unit. The model application unit is used to apply the trained parameter identification model to the newly collected CNC machine tool data to identify the key parameters of the equipment. The key parameter of the equipment is the wear rate of the tool. The state analysis unit will analyze the operating state of the equipment according to the identified wear rate of the tool. The specific formula is as follows:

[0041]

[0042] Among them, K represents the wear rate of the current tool, L1 represents the current tool diameter, L0 represents the initial tool diameter, when K<0.1, it means that the current tool wear is in a normal state, when 0.1≤K<0.2, it means that the current tool has a wear problem and a tool replacement plan needs to be arranged, and when K≥0.2, it means that the current tool has been severely worn and needs to be shut down for maintenance.

[0043] In one embodiment of the present invention: the control module includes an instruction generation unit and an instruction transmission unit. The instruction generation unit will generate corresponding control instructions according to the analysis results of the parameter identification module, and the instruction transmission unit will transmit the generated control instructions to the corresponding equipment in the CNC machine tool.

[0044] In one embodiment of the present invention: the fault analysis and optimization module includes a fault prediction unit and an optimization plan formulation unit. The fault analysis unit collects historical operating data of various parameters of CNC machine tool equipment, as well as past fault records and maintenance information from the cloud database, and associates different types of faults with corresponding parameter changes to establish a fault feature database. At the same time, an abnormality detection algorithm is used to identify abnormal conditions in equipment operation. When an abnormal condition is detected, the fault analysis unit further analyzes the abnormal characteristics and severity, and generates a prediction result based on the abnormal characteristics and severity. The abnormal characteristics are the duration of the abnormality and the size of the abnormal value. After receiving the prediction result from the fault prediction unit, the optimization plan formulation unit will evaluate the fault. The evaluation content includes the possibility, severity, and impact of the fault on equipment performance and production. According to the type of fault and the evaluation result, a corresponding optimization plan is formulated and the plan is transmitted to the control module.

[0045] In one embodiment of the present invention: the optimization plan formulated by the optimization plan formulation unit comes from the optimization plan database, which is established based on historical fault records and manufacturer fault test records. A cost calculation unit is inserted into the optimization plan formulation unit, and the cost calculation unit calculates the price cost and time cost of the optimization plan.

[0046] In one embodiment of the present invention: the user interface module includes a data display unit and an interaction unit. The data display unit will display the data of the CNC machine tool equipment parameters to the user in the form of a chart. The interaction unit is used for the user to query the historical data of the equipment, set parameters and view fault analysis results through the interface. At the same time, the system can also send notifications and alarm information to the user through the interface. The data display unit can intelligently adjust the content and form of data display according to the user's browsing history and focus. At the same time, the order of the chart data is arranged according to the number of clicks of the user. For abnormal data, the data display unit will mark and move the data to the first row for display.

[0047] Example 1, please refer to the attached Figure 1 In a large-scale machining workshop, a parameter identification system for CNC machine tools based on cloud computing was installed. First, the data acquisition module began to play a key role. The sensor unit accurately collected various operating parameters such as temperature, pressure, and speed on the CNC machine tools. These sensors are distributed in key parts of the equipment and can monitor the operating status of the equipment in real time. The marking unit adds a clear mark to each set of collected data to ensure that the data can accurately correspond to the specific CNC machine tools, providing clear source information for subsequent analysis and processing. The data transmission unit uses a stable wireless transmission method to quickly transmit the collected data to the data processing module, avoiding the problems of complex wiring and difficult maintenance that may be caused by traditional wired transmission. After the data processing module cleans and normalizes the data, the model training unit in the cloud computing module regularly extracts data from the processed data area and uses powerful machine learning algorithms, such as neural networks or random forest algorithms, to establish a model that can accurately identify the key parameters of the equipment. With the continuous accumulation of data and the continuous optimization of the model, the accuracy and generalization ability of the model are gradually improved;

[0048] When new data is collected, the parameter identification module takes action quickly. The model application unit applies the trained model to the new data to accurately identify key parameters such as tool wear rate. The state analysis unit uses a specific formula to make judgments based on the tool wear rate. If a tool replacement plan is required, the control module responds immediately. The instruction generation unit quickly generates corresponding adjustment instructions, such as reducing the cutting speed or adjusting the feed rate, to reduce the degree of tool wear. The instruction transmission unit accurately transmits the instructions to the corresponding CNC machine tool equipment to achieve real-time control of the equipment. At the same time, the fault analysis and optimization module continuously collects and analyzes data, predicts faults in advance, and formulates optimization plans.

[0049] The user interface module provides users with intuitive equipment parameter display and convenient interactive functions. In addition, the data display unit can intelligently adjust the content and form of data display according to the user's browsing history and focus. For example, if the user often pays attention to tool wear rate and temperature parameters, the system will place the data of these parameters in a more prominent position. The arrangement order of chart data is dynamically adjusted according to the number of clicks of the user, and the data that the user pays most attention to or views most frequently is placed in the front row, so that users can quickly obtain key information. For abnormal data, the system will not only automatically mark it, but also move this data to the first row for display, ensuring that users can notice potential problems at the first time, improving the efficiency of equipment monitoring and troubleshooting.

[0050] Example 2, please refer to the attached Figure 1 An automobile parts manufacturer introduced the parameter identification system of the present invention. The data acquisition module collects the operating parameters of multiple CNC machine tools in real time to ensure the accuracy of the data. The cloud computing module efficiently stores data and establishes an accurate parameter identification model. When the parameter identification module finds that the tool wear rate of a certain device is close to the threshold that requires a tool replacement plan, the system immediately issues an alarm, and the control module responds quickly to generate instructions to adjust the equipment operating parameters to reduce the tool wear rate. The fault analysis and optimization module predicts the possible fault type and time by analyzing historical data and real-time monitoring, formulates optimization plans and considers cost calculations, and the user interface module displays the equipment parameter data in the form of charts, which is convenient for users to query historical data and view fault analysis results, thereby improving the efficiency and accuracy of equipment management.

[0051] Example 3, please refer to the attached Figure 1 In the aerospace parts manufacturing plant, a parameter identification system for CNC machine tools based on cloud computing is used. The data acquisition module comprehensively collects the operating parameters of various CNC machine tools and equipment, which are stored in the cloud database after being processed by the data processing module. The parameter identification module accurately identifies key equipment parameters, such as tool wear rate. When the tool wear rate shows that the equipment is in a state where it needs to be shut down for maintenance, the control module immediately stops the equipment to avoid further damage. The fault analysis and optimization module quickly formulates optimization plans based on the optimization plan database established based on historical fault records and manufacturer fault test records, and evaluates the plan cost through the cost calculation unit. The user interface module intelligently adjusts the data display content and form to facilitate users to quickly obtain key information, conduct troubleshooting and equipment maintenance in a timely manner, and ensure production efficiency and stability.

[0052] Specifically, the operating parameters of CNC machine tools are collected in real time through the data acquisition module, and are cleaned and normalized by the data processing module to ensure the accuracy and consistency of the data. The cloud computing module uses machine learning algorithms to establish an equipment parameter identification model, which can accurately identify the key parameters of the equipment and analyze the operating status of the equipment in real time through the parameter identification module. This helps to promptly discover potential wear or failure problems of the equipment, so that measures can be taken in advance to avoid equipment downtime due to excessive wear or sudden failure, thereby improving the operating efficiency and processing accuracy of the equipment.

[0053] Specifically, the fault analysis and optimization module collects historical operating data and fault records, establishes a fault feature database, and uses an anomaly detection algorithm to monitor the operating status of the equipment in real time. Once an abnormal situation is detected, it can quickly predict the time and type of the fault, and formulate an optimization plan based on the prediction results. The optimization plan formulation unit not only considers the type and severity of the fault, but also evaluates the price and time costs of different optimization plans through the cost calculation unit, so as to select the optimal plan. This helps enterprises prevent faults, reduce downtime, reduce maintenance costs, and improve overall production efficiency and economic benefits.

[0054] Specifically, by introducing the intelligent data priority display function in the user interface module, the system can intelligently adjust the content and form of data display according to the user's browsing history and focus. In particular, the arrangement order of chart data is dynamically adjusted according to the number of clicks of the user, and the data that the user pays most attention to or views most frequently is placed in a prominent position. For abnormal data, the system will not only automatically mark it, but also move the data to the first row for display, ensuring that users can notice potential problems at the first time. This intelligent data priority display mechanism improves the level of personalization of the user experience, allowing users to obtain key information faster, thereby more effectively monitoring and troubleshooting equipment.

[0055] The above-mentioned front, back, left, right, top and bottom are all based on the figures in the specification. Figure 1 As a benchmark, according to the person's observation perspective, the side of the device facing the observer is defined as the front, the left side of the observer is defined as the left, and so on.

[0056] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the scope of protection of the present invention.

[0057] It should be noted that the device structure and the drawings of the present invention mainly describe the principle of the present invention. In terms of the technology of the design principle, the settings of the power mechanism, power supply system and control system of the device are not fully described. On the premise that the technical personnel in this field understand the principle of the above invention, the details of the power mechanism, power supply system and control system can be clearly known. The control method of the application document is automatic control through a controller, and the control circuit of the controller can be realized by simple programming by the technical personnel in this field.

[0058] The standard parts used therein can all be purchased from the market and can be customized according to the instructions and drawings. The specific connection methods of each part adopt conventional means such as mature bolts, rivets, welding, etc. in the prior art. The machinery, parts and equipment all adopt conventional models in the prior art, and the structures and principles of the components known to technical personnel in this field can be known by these technical personnel through technical manuals or through conventional experimental methods.

[0059] The embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions and variations are made to these embodiments without departing from the principle and spirit of the present invention, and they still fall within the protection scope of the present invention.

Claims

1. A parameter identification system for CNC machine tools based on cloud computing, characterized in that: Includes the following modules: A data acquisition module, wherein the data acquisition module is used to collect operating parameters of CNC machine tools; A data processing module, which is used to clean and normalize the data transmitted by the data acquisition module; A cloud computing module, which is used to store the collected CNC machine tool data in a cloud database and use a machine learning algorithm to establish an equipment parameter identification model; A parameter identification module, which is used to apply the trained parameter identification model to the newly collected CNC machine tool data, identify the key parameters of the equipment, and analyze the operating status of the equipment; A control module, the control module is used to send control instructions to corresponding devices in the CNC machine tool according to the analysis results of the parameter identification module; A fault analysis and optimization module, which is used to predict the time and type of fault occurrence and formulate an optimization plan; A user interface module is used to display data of CNC machine tool equipment parameters to the user.

2. According to claim 1, a parameter identification system for CNC machine tools based on cloud computing is characterized in that: The data acquisition module includes a sensor unit, a marking unit and a data transmission unit. The sensor unit is used to collect various sensor data installed on the CNC machine tool equipment. The marking unit is used to mark the CNC machine tool equipment corresponding to the data. The data transmission unit is used to transmit the data collected by the sensor unit to the data processing module by wireless transmission.

3. The parameter identification system for CNC machine tools based on cloud computing according to claim 1 is characterized in that: The data processing module includes a data cleaning unit and a normalization unit. The data cleaning unit is used to remove noise, abnormal values ​​and erroneous data in the collected data, and the normalization unit is used to perform normalization processing on the cleaned data.

4. The parameter identification system for CNC machine tools based on cloud computing according to claim 1 is characterized in that: The cloud computing module includes a data storage unit and a model training unit. The data storage unit is used to store the collected data in a cloud database. The cloud database is divided into three data areas: an original data area, a processed data area, and a simulation data area. The original data area is used to store unprocessed original data collected from CNC machine tools. The processed data area is used to store data cleaned and normalized by the data processing module. The model training unit will extract the data in the processed data area in the cloud database and use a machine learning algorithm to establish a device parameter identification model. The simulation data area is used to store the output results of the machine learning model.

5. The parameter identification system for CNC machine tools based on cloud computing according to claim 1 is characterized in that: The parameter identification module includes a model application unit and a state analysis unit. The model application unit is used to apply the trained parameter identification model to the newly collected CNC machine tool data to identify the key parameters of the equipment. The key parameter of the equipment is the wear rate of the tool. The state analysis unit will analyze the operating state of the equipment according to the identified wear rate of the tool. The specific formula is as follows: Among them, K represents the wear rate of the current tool, L1 represents the current tool diameter, L0 represents the initial tool diameter, when K<0.1, it means that the current tool wear is in a normal state, when 0.1≤K<0.2, it means that the current tool has a wear problem and a tool replacement plan needs to be arranged, and when K≥0.2, it means that the current tool has been severely worn and needs to be shut down for maintenance.

6. The parameter identification system for CNC machine tools based on cloud computing according to claim 1 is characterized in that: The control module includes an instruction generation unit and an instruction transmission unit. The instruction generation unit will generate corresponding control instructions according to the analysis results of the parameter identification module, and the instruction transmission unit will transmit the generated control instructions to the corresponding equipment in the CNC machine tool.

7. The parameter identification system for CNC machine tools based on cloud computing according to claim 1 is characterized in that: The fault analysis and optimization module includes a fault prediction unit and an optimization plan formulation unit. The fault analysis unit collects historical operating data of various parameters of CNC machine tool equipment, as well as past fault records and maintenance information from the cloud database, and associates different types of faults with corresponding parameter changes to establish a fault feature database. At the same time, an abnormality detection algorithm is used to identify abnormal conditions in equipment operation. When an abnormal condition is detected, the fault analysis unit further analyzes the abnormal characteristics and severity, and generates a prediction result based on the abnormal characteristics and severity. The abnormal characteristics are the duration of the abnormality and the size of the abnormal value. After receiving the prediction result from the fault prediction unit, the optimization plan formulation unit will evaluate the fault. The evaluation content includes the possibility, severity, and impact of the fault on equipment performance and production. According to the type of fault and the evaluation result, a corresponding optimization plan is formulated and the plan is transmitted to the control module.

8. The parameter identification system for CNC machine tools based on cloud computing according to claim 7 is characterized in that: The optimization plan formulated by the optimization plan formulation unit comes from an optimization plan database, which is established based on historical fault records and manufacturer fault test records. A cost calculation unit is inserted into the optimization plan formulation unit, and the cost calculation unit calculates the price cost and time cost of the optimization plan.

9. The parameter identification system for CNC machine tools based on cloud computing according to claim 1, characterized in that: The user interface module includes a data display unit and an interaction unit. The data display unit will display the data of CNC machine tool equipment parameters to the user in the form of a chart. The interaction unit is used for the user to query the historical data of the equipment, set parameters and view fault analysis results through the interface. At the same time, the system can also send notifications and alarm information to the user through the interface. The data display unit can intelligently adjust the content and form of data display according to the user's browsing history and focus. At the same time, the order of chart data is arranged according to the number of user clicks. For abnormal data, the data display unit will mark and move the data to the first row for display.

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