A CNC lathe cluster status analysis system and method based on the Internet of Things

Abstract

This invention discloses a CNC lathe cluster status analysis system and method based on the Internet of Things (IoT), belonging to the field of CNC lathe cluster status analysis technology. The invention includes: S10: obtaining historical key operation datasets and historical production datasets for each non-faulty CNC lathe during unplanned downtime periods; S20: analyzing the linkage impact index of faulty CNC lathes on each non-faulty CNC lathe; S30: analyzing the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster; S40: determining the operation and maintenance sequence of each non-faulty CNC lathe in the CNC lathe cluster. This invention avoids the uniformity of cluster status analysis results by analyzing the linkage response between faulty CNC lathes and non-faulty CNC lathes in the CNC lathe cluster, and avoids the response lag of cluster status analysis results by analyzing the real-time operation and maintenance status of non-faulty CNC lathes.

Description

Technical Field

[0001] This invention relates to the field of CNC lathe cluster status analysis technology, specifically a CNC lathe cluster status analysis system and method based on the Internet of Things. Background Technology

[0002] CNC lathe cluster status analysis refers to the centralized monitoring, data collection, and comprehensive analysis of the operating status of a group of CNC lathes. The aim is to fully understand the equipment's operating efficiency, health status, and production performance, thereby achieving the goals of cost reduction, efficiency improvement, enhanced management, and overall production level improvement.

[0003] Traditional methods for analyzing the status of CNC lathe clusters mainly rely on manual inspection, basic data collection, and experience-based judgment. These methods focus on monitoring and analyzing the independent operating status of individual CNC lathes in the cluster, without delving into the interconnected response between the CNC lathes. This results in delayed and limited cluster status analysis results, making it difficult to meet the demands of modern intelligent manufacturing for real-time perception, intelligent analysis, and collaborative optimization of cluster status. Summary of the Invention

[0004] The purpose of this invention is to provide a CNC lathe cluster status analysis system and method based on the Internet of Things to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for analyzing the status of a CNC lathe cluster based on the Internet of Things, the method comprising:

[0006] S10: Connect the CNC lathe cluster using an industrial intelligent gateway and configure the communication protocol to collect real-time key operation data and real-time production data of each non-faulty CNC lathe in the CNC lathe cluster during unplanned downtime, and obtain the historical key operation dataset and historical production dataset of each non-faulty CNC lathe during unplanned downtime.

[0007] S20: Based on time series analysis, the dynamic deviation between the actual and theoretical operating performance of each non-faulty CNC lathe during unplanned downtime is determined, and abnormal data of each non-faulty CNC lathe during unplanned downtime is identified. Based on the type of abnormal data and the continuity of abnormal data over time, a mutation feature matrix of each non-faulty CNC lathe during unplanned downtime is generated, and then the linkage influence index of the faulty CNC lathe on each non-faulty CNC lathe is analyzed.

[0008] S30: Analyze the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster based on the real-time linkage impact index of the faulty CNC lathe on each non-faulty CNC lathe.

[0009] S40: Determine the maintenance sequence of each non-faulty CNC lathe in the CNC lathe cluster based on the real-time maintenance status of each non-faulty CNC lathe in the CNC lathe cluster.

[0010] Furthermore, S10 includes:

[0011] S101: Connect all CNC lathes in a CNC lathe cluster that use different communication protocols to a unified industrial intelligent gateway. When the CNC lathes in the CNC lathe cluster... When a CNC lathe is in an unplanned downtime state, the CNC lathe in the unplanned downtime state will be... CNC lathes marked as faulty are monitored using an industrial intelligent gateway during unplanned downtime periods for each non-faulty CNC lathe in the CNC lathe cluster. Real-time key operational data and real-time production data are collected, including... This indicates the number corresponding to each CNC lathe in the CNC lathe cluster. This represents the total number of CNC lathes present in the CNC lathe cluster. Indicates CNC lathe The point in time when the system began to experience unplanned downtime. Indicates CNC lathe The point in time when the system transitions from an unplanned downtime state to a normal operating state;

[0012] S102: Key operating data includes spindle speed Machining feed rate Processing feed rate Production data includes CNC lathe productivity. and CNC lathe utilization rate ;

[0013] Identify non-faulty CNC lathes During unplanned downtime Historical critical runtime dataset and historical production datasets ;

[0014] ;

[0015] ;

[0016] in, and , This indicates the data collection interval of the industrial smart gateway. , , These represent non-faulty CNC lathes. exist Spindle speed, machining feed rate, and machining feed amount at any given time. , These represent non-faulty CNC lathes. exist The productivity and utilization rate of CNC lathes at any given time.

[0017] Using key operating data and production data of each non-faulty CNC lathe during unplanned downtime periods as the data source to be analyzed is beneficial for analyzing the actual operating status of each non-faulty CNC lathe during unplanned downtime periods.

[0018] Furthermore, S20 includes:

[0019] S201: Based on non-faulty CNC lathes During unplanned downtime Preset processing program Obtain non-faulty CNC lathes During unplanned downtime Theoretical key running dataset within and theoretical production dataset ;

[0020] ;

[0021] ;

[0022] in, , , These represent non-faulty CNC lathes. exist The theoretical spindle speed, theoretical machining feed rate, and theoretical machining feed amount at any given time. , These represent non-faulty CNC lathes. exist Theoretical CNC lathe productivity and theoretical CNC lathe utilization rate at any given time;

[0023] Based on time series Obtain historical critical runtime datasets With theoretical critical running dataset The first dynamic deviation dataset between Historical production datasets With theoretical production dataset The second dynamic deviation dataset between ;

[0024] ;

[0025] ;

[0026] S202: For non-faulty CNC lathes The corresponding first dynamic deviation dataset Second dynamic deviation dataset ,like If it is not within the error range, then it is considered... If it is an outlier in the dynamic bias dataset, then it is considered... This is not outlier data in the dynamic bias dataset, where... This indicates that the industrial smart gateways are processed in chronological order. The data collection time points within the time period are numbered. This represents the data deviation corresponding to any one of the data types in the set consisting of critical operational data types and production data types.

[0027] According to non-faulty CNC lathes exist The time sequence of abnormal production data within a time period was used to identify non-faulty CNC lathes. exist The timing sequence of abnormal spindle speed, abnormal machining feed rate, abnormal machining feed amount, and abnormal utilization rate within a time period, specifically for timestamps. Non-faulty CNC lathe exist If abnormal production data is present at all times, but abnormal machining feed rates are not present, then data 0 is considered a non-faulty CNC lathe. exist Abnormal processing feed rate at any given time;

[0028] Building a fault-free CNC lathe mutation feature matrix Among them, the abnormal spindle speed timing, abnormal machining feed rate timing, abnormal machining feed amount timing, and abnormal utilization rate timing are respectively used as the mutation feature matrix. The first line, the second line, the third line, and the fourth line;

[0029] Based on the mutation feature matrix determined by the time series of abnormal production data of each non-faulty CNC lathe, it can accurately reflect the impact of the shutdown of the faulty CNC lathe on the abnormal production of each non-faulty CNC lathe, realize the linkage response analysis between CNC lathes in the cluster, and improve the accuracy of the cluster status analysis results.

[0030] S203: Regarding the mutation feature matrix The number of non-zero elements and the mutation feature matrix The ratio between the total number of elements present in the sample Perform calculations;

[0031] For non-faulty CNC lathes exist All elements stored in the time series of abnormal production data within a time period are accumulated, and the result of the accumulation is denoted as . ;

[0032] according to For faulty CNC lathes vs. non-faulty CNC lathes The linkage impact index is calculated, where, Indicates an exponential function with base and .

[0033] By analyzing the real-time key operating data, real-time production data, and real-time unfinished workload of each non-faulty CNC lathe, the real-time operation and maintenance indicators of each non-faulty CNC lathe are analyzed, thereby realizing the analysis of the real-time operation and maintenance status of each non-faulty CNC lathe, which to a certain extent eliminates the impact lag of cluster status analysis results.

[0034] Furthermore, S30 includes:

[0035] S301: Obtain the faulty CNC lathe's data for the non-faulty CNC lathe. exist Momentary linkage impact index and non-faulty CNC lathes During the remaining planned processing time Unfinished work within ,in, Indicates a non-faulty CNC lathe The corresponding end time within the planned processing period;

[0036] S302: For non-faulty CNC lathes During the remaining planned processing time Unfinished work within Comparison of faulty CNC lathes with non-faulty CNC lathes exist Momentary linkage impact index The product between them is calculated to obtain the non-faulty CNC lathe. exist Operational metrics at any time ;

[0037] like This indicates a non-faulty CNC lathe. exist The current maintenance status is emergency maintenance;

[0038] like This indicates a non-faulty CNC lathe. exist The current maintenance status is "pending maintenance," where... Indicates a non-faulty CNC lathe At the maximum additional daily production level;

[0039] The operational status analyzed by operational indicators can ensure that non-faulty CNC lathes can complete production tasks within the remaining planned processing time after timely operational maintenance.

[0040] Furthermore, S40 includes:

[0041] S401: Based on the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster, a first operation and maintenance set and a second operation and maintenance set for the CNC lathe cluster are obtained. The first operation and maintenance set stores the numbers of non-faulty CNC lathes with an operation and maintenance status of emergency operation and maintenance, and the second operation and maintenance set stores the numbers of non-faulty CNC lathes with an operation and maintenance status of pending operation and maintenance.

[0042] S402: Reorder the numbers of all non-faulty CNC lathes stored in the first maintenance central storage according to the order of the maintenance indicators of each non-faulty CNC lathe in the CNC lathe cluster from largest to smallest.

[0043] According to the order of the maintenance indicators of each non-faulty CNC lathe in the CNC lathe cluster from largest to smallest, the numbers of each non-faulty CNC lathe stored in the second maintenance center are reordered.

[0044] Based on the reordered first and second maintenance sets, the maintenance order of each non-faulty CNC lathe in the CNC lathe cluster is determined. Specifically, after all the non-faulty CNC lathes corresponding to the numbers stored in the first maintenance set have been processed, the non-faulty CNC lathes corresponding to the numbers stored in the second maintenance set are then processed.

[0045] A CNC lathe cluster status analysis system based on the Internet of Things, the system includes a historical dataset acquisition module, a linkage impact index analysis module, a CNC lathe cluster operation and maintenance status analysis module, and an operation and maintenance sequence determination module;

[0046] The historical dataset acquisition module is used to acquire the historical key operation dataset and historical production dataset of each non-faulty CNC lathe during unplanned downtime periods.

[0047] The linkage impact index analysis module is used to analyze the linkage impact index of the faulty CNC lathe on each non-faulty CNC lathe.

[0048] The CNC lathe cluster operation and maintenance status analysis module is used to analyze the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster.

[0049] The maintenance sequence determination module is used to determine the maintenance sequence of each non-faulty CNC lathe in the CNC lathe cluster.

[0050] Furthermore, the historical dataset acquisition module includes a historical data collection unit and a historical dataset acquisition unit;

[0051] The historical data acquisition unit uses an industrial intelligent gateway to collect historical key operating data and historical production data of each non-faulty CNC lathe in the CNC lathe cluster during unplanned downtime periods.

[0052] The historical data acquisition unit is used to acquire historical key operation datasets and historical production datasets for each non-faulty CNC lathe during unplanned downtime periods.

[0053] Furthermore, the linkage impact index analysis module includes a dynamic deviation dataset acquisition unit, a mutation feature matrix determination unit, and a linkage impact index analysis unit;

[0054] The dynamic deviation dataset acquisition unit acquires, based on time series, the first dynamic deviation dataset between the historical key operation dataset and the theoretical key operation dataset of each non-faulty CNC lathe during the unplanned downtime period, and the second dynamic deviation dataset between the historical production dataset and the theoretical production dataset.

[0055] The mutation feature matrix determination unit determines the mutation feature matrix of each non-faulty CNC lathe during unplanned downtime based on the temporal continuity between the abnormal data in the first dynamic deviation dataset and the abnormal data in the second dynamic deviation dataset.

[0056] The linkage impact index analysis unit analyzes the linkage impact index of the faulty CNC lathe on each non-faulty CNC lathe based on the mutation characteristic matrix of each non-faulty CNC lathe during unplanned downtime.

[0057] Furthermore, the data lathe cluster operation and maintenance status analysis module includes a data acquisition unit and an operation and maintenance status analysis unit;

[0058] The data acquisition unit is used to acquire the real-time linkage impact index of the faulty CNC lathe on each non-faulty CNC lathe, as well as the unfinished workload of each non-faulty CNC lathe during the remaining planned processing time.

[0059] The operation and maintenance analysis unit analyzes the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster based on the real-time operation and maintenance indicators of each non-faulty CNC lathe.

[0060] Furthermore, the operation and maintenance sequence determination module includes an operation and maintenance classification unit and an operation and maintenance sequence determination unit;

[0061] The operation and maintenance classification unit obtains a first operation and maintenance set and a second operation and maintenance set for the CNC lathe cluster based on the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster.

[0062] The maintenance sequence determination unit determines the maintenance sequence of each non-faulty CNC lathe in the CNC lathe cluster based on the reordered first and second maintenance sets.

[0063] Compared with the prior art, the beneficial effects of the present invention are:

[0064] 1. This invention analyzes the operational deviations of each non-faulty CNC lathe during unplanned downtime periods by analyzing industrial data (key operating data and production data) of each non-faulty CNC lathe in a CNC lathe cluster. This enables the analysis of the linkage response between faulty CNC lathes and non-faulty CNC lathes in the CNC lathe cluster, avoiding the uniformity of cluster status analysis results.

[0065] 2. This invention analyzes the production completion status of each non-faulty CNC lathe during the planned working period by using the real-time linkage impact index of each non-faulty CNC lathe. Based on the analysis results, it analyzes the real-time operation and maintenance status of each non-faulty CNC lathe, thereby ensuring that each non-faulty CNC lathe can complete the preset plan within the planned working period, avoiding the response lag of the cluster status analysis results.

[0066] 3. This invention performs real-time intelligent analysis of industrial data collected by an industrial intelligent gateway from each CNC lathe in the CNC lathe cluster, thereby achieving real-time perception of the status of the CNC lathe cluster. Based on the real-time operation and maintenance indicators and real-time operation and maintenance status of each non-faulty CNC lathe, it achieves collaborative optimization of the CNC lathe cluster. Attached Figure Description

[0067] Figure 1 This is a schematic diagram illustrating the workflow of a CNC lathe cluster status analysis method based on the Internet of Things according to the present invention. Detailed Implementation

[0068] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0069] like Figure 1 As shown, this invention provides a technical solution for a CNC lathe cluster status analysis method based on the Internet of Things (IoT). The method includes:

[0070] S10: Connect the CNC lathe cluster using an industrial intelligent gateway and configure the communication protocol to collect real-time key operation data and real-time production data of each non-faulty CNC lathe in the CNC lathe cluster during unplanned downtime, and obtain the historical key operation dataset and historical production dataset of each non-faulty CNC lathe during unplanned downtime.

[0071] S10 includes:

[0072] S101: Connect all CNC lathes in a CNC lathe cluster that use different communication protocols to a unified industrial intelligent gateway. When the CNC lathes in the CNC lathe cluster... When a CNC lathe is in an unplanned shutdown state, it indicates that the CNC lathe is forced to stop operating due to unforeseen circumstances without prior arrangement. A CNC lathe in an unplanned shutdown state will be... CNC lathes marked as faulty are monitored using an industrial intelligent gateway during unplanned downtime periods for each non-faulty CNC lathe in the CNC lathe cluster. Real-time key operational data and real-time production data are collected. Non-faulty CNC lathes are included in the remaining CNC lathe cluster, excluding the faulty CNC lathes. This indicates the number corresponding to each CNC lathe in the CNC lathe cluster. This represents the total number of CNC lathes present in the CNC lathe cluster. Indicates CNC lathe The point in time when the system began to experience unplanned downtime. Indicates CNC lathe The point in time when the system transitions from an unplanned downtime state to a normal operating state;

[0073] S102: Key operating data includes spindle speed Machining feed rate Processing feed rate Production data includes CNC lathe productivity. and CNC lathe utilization rate CNC lathe productivity is used to express the number of qualified workpieces processed by a CNC lathe per unit time, while CNC lathe utilization rate is used to express the percentage of actual working time of a CNC lathe to the planned available time.

[0074] Identify non-faulty CNC lathes During unplanned downtime Historical critical runtime dataset and historical production datasets ;

[0075] ;

[0076] ;

[0077] in, and , This indicates the data collection interval of the industrial smart gateway. , , These represent non-faulty CNC lathes. exist Spindle speed, machining feed rate, and machining feed amount at any given time. , These represent non-faulty CNC lathes. exist The productivity and utilization rate of CNC lathes at any given time;

[0078] S20: Based on time series analysis, the dynamic deviation between the actual and theoretical operating performance of each non-faulty CNC lathe during unplanned downtime is determined, and abnormal data of each non-faulty CNC lathe during unplanned downtime is identified. Based on the type of abnormal data and the continuity of abnormal data over time, a mutation feature matrix of each non-faulty CNC lathe during unplanned downtime is generated, and then the linkage influence index of the faulty CNC lathe on each non-faulty CNC lathe is analyzed.

[0079] S20 includes:

[0080] S201: Based on non-faulty CNC lathes During unplanned downtime Preset processing program Obtain non-faulty CNC lathes During unplanned downtime Theoretical key running dataset within and theoretical production dataset ;

[0081] ;

[0082] ;

[0083] in, , , These represent non-faulty CNC lathes. exist The theoretical spindle speed, theoretical machining feed rate, and theoretical machining feed amount at any given time. , These represent non-faulty CNC lathes. exist Theoretical CNC lathe productivity and theoretical CNC lathe utilization rate at any given time;

[0084] In CNC simulation software, multiple simulation software instances are launched simultaneously. Each instance simulates one CNC lathe in a CNC lathe cluster, and each virtual CNC lathe is assigned its own position within the cluster. Preset processing program executed within the time period The system and equipment aging conditions of each virtual CNC lathe are identical to those of its corresponding physical CNC lathe. Then, the CNC simulation software automatically generates the physical CNC lathe corresponding to each virtual CNC lathe. Execute the preset processing program within the time period The theoretical key running dataset and theoretical production dataset at that time;

[0085] Based on time series Obtain historical critical runtime datasets With theoretical critical running dataset The first dynamic deviation dataset between Historical production datasets With theoretical production dataset The second dynamic deviation dataset between ;

[0086] ;

[0087] ;

[0088] S202: For non-faulty CNC lathes The corresponding first dynamic deviation dataset Second dynamic deviation dataset ,like If it is outside the error range, and the error range is set manually, then it is considered... If it is an outlier in the dynamic bias dataset, then it is considered... This is not outlier data in the dynamic bias dataset, where... This indicates that the industrial smart gateways are processed in chronological order. The data collection time points within the time period are numbered. This represents the data deviation corresponding to any one of the data types in the set consisting of critical operational data types and production data types. These data types include spindle speed, machining feed rate, machining feed amount, CNC lathe productivity, and CNC lathe utilization rate. Specifically, it can be represented as Corresponding Specifically, it can be represented as ;

[0089] According to non-faulty CNC lathes exist The time sequence of abnormal production data within a time period was used to identify non-faulty CNC lathes. exist The abnormal spindle speed time series, abnormal machining feed rate time series, abnormal machining feed amount time series, and abnormal utilization rate time series within a time period share the same set of timestamps as the abnormal spindle speed time series, abnormal machining feed rate time series, abnormal machining feed amount time series, and abnormal utilization rate time series. Specifically, for the timestamps... Non-faulty CNC lathe exist If abnormal production data is present at all times, but abnormal machining feed rates are not present, then data 0 is considered a non-faulty CNC lathe. exist Abnormal processing feed rate at any given time;

[0090] Building a fault-free CNC lathe mutation feature matrix Among them, the abnormal spindle speed timing, abnormal machining feed rate timing, abnormal machining feed amount timing, and abnormal utilization rate timing are respectively used as the mutation feature matrix. The first line, the second line, the third line, and the fourth line;

[0091] S203: Regarding the mutation feature matrix The number of non-zero elements and the mutation feature matrix The ratio between the total number of elements present in the sample Perform calculations;

[0092] For non-faulty CNC lathes exist All elements stored in the time series of abnormal production data within a time period are accumulated, and the result of the accumulation is denoted as . ;

[0093] according to For faulty CNC lathes vs. non-faulty CNC lathes The linkage impact index is calculated, where, Indicates an exponential function with base and ;

[0094] S30: Analyze the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster based on the real-time linkage impact index of the faulty CNC lathe on each non-faulty CNC lathe.

[0095] S30 includes:

[0096] S301: Obtain the faulty CNC lathe's data for the non-faulty CNC lathe. exist Momentary linkage impact index and non-faulty CNC lathes During the remaining planned processing time Unfinished work within ,in, Indicates a non-faulty CNC lathe The corresponding end time in the planned processing period. ;

[0097] S302: For non-faulty CNC lathes During the remaining planned processing time Unfinished work within Comparison of faulty CNC lathes with non-faulty CNC lathes exist Momentary linkage impact index The product between them is calculated to obtain the non-faulty CNC lathe. exist Operational metrics at any time ;

[0098] like This indicates a non-faulty CNC lathe. exist The current maintenance status is emergency maintenance;

[0099] like This indicates a non-faulty CNC lathe. exist The current maintenance status is "pending maintenance," where... Indicates a non-faulty CNC lathe The maximum additional daily production capacity refers to the production capacity of non-faulty CNC lathes according to the production plan. The daily production capacity is 10 units, excluding faulty CNC lathes. If the daily production limit is 12 units, then the non-faulty CNC lathe... The maximum additional production per day is 2.

[0100] S40: Determine the maintenance sequence of each non-faulty CNC lathe in the CNC lathe cluster based on the real-time maintenance status of each non-faulty CNC lathe in the CNC lathe cluster.

[0101] S40 includes:

[0102] S401: Based on the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster, a first operation and maintenance set and a second operation and maintenance set for the CNC lathe cluster are obtained. The first operation and maintenance set stores the numbers of non-faulty CNC lathes with an operation and maintenance status of emergency operation and maintenance, and the second operation and maintenance set stores the numbers of non-faulty CNC lathes with an operation and maintenance status of pending operation and maintenance.

[0103] S402: Reorder the numbers of all non-faulty CNC lathes stored in the first maintenance central storage according to the order of the maintenance indicators of each non-faulty CNC lathe in the CNC lathe cluster from largest to smallest.

[0104] According to the order of the maintenance indicators of each non-faulty CNC lathe in the CNC lathe cluster from largest to smallest, the numbers of each non-faulty CNC lathe stored in the second maintenance center are reordered.

[0105] Based on the reordered first and second maintenance sets, the maintenance order of each non-faulty CNC lathe in the CNC lathe cluster is determined. Specifically, after all non-faulty CNC lathes corresponding to their respective numbers stored in the first maintenance set have been processed, the non-faulty CNC lathes corresponding to their respective numbers stored in the second maintenance set are then processed. For faulty CNC lathes, then... The maintenance personnel should be notified in a timely manner to handle the maintenance. Each CNC lathe in the CNC lathe cluster operates independently. Therefore, the failure of a faulty CNC lathe will not directly cause the failure of non-faulty CNC lathes. However, the downtime of a faulty CNC lathe will still have an indirect impact on the overall production efficiency and production plan of the CNC lathe cluster.

[0106] A CNC lathe cluster status analysis system based on the Internet of Things includes a historical dataset acquisition module, a linkage impact index analysis module, a CNC lathe cluster operation and maintenance status analysis module, and an operation and maintenance sequence determination module.

[0107] The historical dataset acquisition module is used to acquire historical key operation datasets and historical production datasets for each non-faulty CNC lathe during unplanned downtime periods;

[0108] The historical dataset acquisition module includes a historical data collection unit and a historical dataset acquisition unit;

[0109] The historical data acquisition unit uses an industrial intelligent gateway to collect historical key operating data and historical production data of each non-faulty CNC lathe in the CNC lathe cluster during unplanned downtime periods;

[0110] The historical data acquisition unit is used to acquire historical key operation datasets and historical production datasets for each non-faulty CNC lathe during unplanned downtime periods;

[0111] The linkage impact index analysis module is used to analyze the linkage impact index of faulty CNC lathes on each non-faulty CNC lathe;

[0112] The linkage impact index analysis module includes a dynamic deviation dataset acquisition unit, a mutation feature matrix determination unit, and a linkage impact index analysis unit;

[0113] The dynamic deviation dataset acquisition unit acquires the first dynamic deviation dataset between the historical key operation dataset and the theoretical key operation dataset, and the second dynamic deviation dataset between the historical production dataset and the theoretical production dataset for each non-faulty CNC lathe during unplanned downtime periods, based on time series data.

[0114] The mutation feature matrix determination unit determines the mutation feature matrix of each non-faulty CNC lathe during unplanned downtime based on the temporal continuity between the abnormal data in the first dynamic deviation dataset and the abnormal data in the second dynamic deviation dataset.

[0115] The linkage impact index analysis unit analyzes the linkage impact index of the faulty CNC lathe on each non-faulty CNC lathe based on the mutation characteristic matrix of each non-faulty CNC lathe during unplanned downtime.

[0116] The CNC lathe cluster operation and maintenance status analysis module is used to analyze the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster;

[0117] The data lathe cluster operation and maintenance status analysis module includes a data acquisition unit and an operation and maintenance status analysis unit;

[0118] The data acquisition unit is used to acquire the real-time linkage impact index of the faulty CNC lathe on each non-faulty CNC lathe, as well as the unfinished workload of each non-faulty CNC lathe during the remaining planned processing time.

[0119] The operation and maintenance analysis unit analyzes the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster based on the real-time operation and maintenance indicators of each non-faulty CNC lathe.

[0120] The maintenance sequence determination module is used to determine the maintenance sequence of each non-faulty CNC lathe in the CNC lathe cluster;

[0121] The operation and maintenance sequence determination module includes an operation and maintenance classification unit and an operation and maintenance sequence determination unit;

[0122] The operation and maintenance classification unit obtains the first operation and maintenance set and the second operation and maintenance set of the CNC lathe cluster based on the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster.

[0123] The maintenance sequence determination unit determines the maintenance sequence of each non-faulty CNC lathe in the CNC lathe cluster based on the reordered first and second maintenance sets.

[0124] Example 1: Set up a mutation feature matrix The number of non-zero elements in the matrix is ​​6, and the mutation feature matrix is... If the total number of elements in the array is 20, then... ;

[0125] Assume a non-faulty CNC lathe exist The cumulative processing result of all elements stored in the time series of abnormal production data within a time period. Then the faulty CNC lathe will affect the non-faulty CNC lathe. The linkage impact index is:

[0126] ;

[0127] The faulty CNC lathe corresponds to the non-faulty CNC lathe. The linkage impact index is 0.3.

[0128] Example 2: Assume a non-faulty CNC lathe During the remaining planned processing time Unfinished work within Faulty CNC lathe to non-faulty CNC lathe exist Momentary linkage impact index Then, the non-faulty CNC lathe exist The operational metrics at any given time are

[0129] ;

[0130] Assume a non-faulty CNC lathe Maximum daily extra production , ,but:

[0131] ;

[0132] Non-faulty CNC lathe exist The current maintenance status is emergency maintenance.

[0133] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing the status of a CNC lathe cluster based on the Internet of Things, characterized in that: The method includes: S10: Connect the CNC lathe cluster using an industrial intelligent gateway and configure the communication protocol to collect real-time key operation data and real-time production data of each non-faulty CNC lathe in the CNC lathe cluster during unplanned downtime, and obtain the historical key operation dataset and historical production dataset of each non-faulty CNC lathe during unplanned downtime. S20: Based on time series analysis, the dynamic deviation between the actual and theoretical operating performance of each non-faulty CNC lathe during unplanned downtime is determined, and abnormal data of each non-faulty CNC lathe during unplanned downtime is identified. Based on the type of abnormal data and the continuity of abnormal data over time, a mutation feature matrix of each non-faulty CNC lathe during unplanned downtime is generated, and then the linkage influence index of the faulty CNC lathe on each non-faulty CNC lathe is analyzed. S30: Analyze the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster based on the real-time linkage impact index of the faulty CNC lathe on each non-faulty CNC lathe. S40: Determine the maintenance sequence of each non-faulty CNC lathe in the CNC lathe cluster based on the real-time maintenance status of each non-faulty CNC lathe in the CNC lathe cluster.

2. The method for analyzing the status of a CNC lathe cluster based on the Internet of Things according to claim 1, characterized in that: S10 includes: S101: Connect all CNC lathes in the CNC lathe cluster that use different communication protocols to the industrial intelligent gateway. When any CNC lathe in the CNC lathe cluster is in an unplanned downtime state, mark the CNC lathe in the unplanned downtime state as a faulty CNC lathe. Use the industrial intelligent gateway to collect historical key operation data and historical production data of each non-faulty CNC lathe in the CNC lathe cluster during the unplanned downtime period. S102: Determine the historical critical operation data set and historical production data set for each non-faulty CNC lathe during unplanned downtime periods. The critical operation data includes spindle speed, machining feed rate, and machining feed amount, while the production data includes CNC lathe productivity and CNC lathe utilization rate.

3. The method for analyzing the status of a CNC lathe cluster based on the Internet of Things according to claim 2, characterized in that: S20 includes: S201: Based on the preset machining program of each non-faulty CNC lathe during the unplanned downtime period, obtain the theoretical key operation dataset and theoretical production dataset of each non-faulty CNC lathe during the unplanned downtime period. Based on the time series, obtain the first dynamic deviation dataset between the historical key operation dataset and the theoretical key operation dataset and the second dynamic deviation dataset between the historical production dataset and the theoretical production dataset of each non-faulty CNC lathe during the unplanned downtime period. The dynamic deviation dataset is used to represent the data set of historical key operation data or historical production data determined based on the time series that deviates from the theoretical key operation data or theoretical production data. S202: Filter out the abnormal data in the first dynamic deviation dataset and the second dynamic deviation dataset corresponding to each non-faulty CNC lathe, and determine the mutation feature matrix of each non-faulty CNC lathe during the unplanned downtime based on the temporal continuity between the abnormal data in the first dynamic deviation dataset and the abnormal data in the second dynamic deviation dataset. S203: Based on the mutation characteristic matrix of each non-faulty CNC lathe during unplanned downtime, analyze the linkage influence index of the faulty CNC lathe on each non-faulty CNC lathe. The linkage influence index is used to represent the degree of abnormal influence of the faulty CNC lathe on the non-faulty CNC lathe.

4. The method for analyzing the status of a CNC lathe cluster based on the Internet of Things according to claim 3, characterized in that: S30 includes: S301: Obtain the real-time linkage impact index of the faulty CNC lathe on each non-faulty CNC lathe, as well as the unfinished workload of each non-faulty CNC lathe during the remaining planned machining time period; S302: Based on the real-time operation and maintenance indicators of each non-faulty CNC lathe, analyze the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster. The operation and maintenance indicators are used to represent the product between the unfinished workload of the non-faulty CNC lathe in the remaining planned processing time and the real-time linkage impact index of the non-faulty CNC lathe.

5. The method for analyzing the status of a CNC lathe cluster based on the Internet of Things according to claim 4, characterized in that: S40 includes: S401: Based on the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster, a first operation and maintenance set and a second operation and maintenance set for the CNC lathe cluster are obtained. The first operation and maintenance set stores the numbers of non-faulty CNC lathes with an operation and maintenance status of emergency operation and maintenance, and the second operation and maintenance set stores the numbers of non-faulty CNC lathes with an operation and maintenance status of pending operation and maintenance. S402: Based on the maintenance indicators of each non-faulty CNC lathe in the CNC lathe cluster, reorder the first maintenance set and the second maintenance set, and determine the maintenance order of each non-faulty CNC lathe in the CNC lathe cluster based on the reordered first maintenance set and second maintenance set.

6. An IoT-based CNC lathe cluster status analysis system applied to the IoT-based CNC lathe cluster status analysis method according to any one of claims 1-5, characterized in that: The system includes a historical dataset acquisition module, a linkage impact index analysis module, a CNC lathe cluster operation and maintenance status analysis module, and an operation and maintenance sequence determination module. The historical dataset acquisition module is used to acquire the historical key operation dataset and historical production dataset of each non-faulty CNC lathe during unplanned downtime periods. The linkage impact index analysis module is used to analyze the linkage impact index of the faulty CNC lathe on each non-faulty CNC lathe. The CNC lathe cluster operation and maintenance status analysis module is used to analyze the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster. The maintenance sequence determination module is used to determine the maintenance sequence of each non-faulty CNC lathe in the CNC lathe cluster.

7. The IoT-based CNC lathe cluster status analysis system according to claim 6, characterized in that: The historical dataset acquisition module includes a historical data collection unit and a historical dataset acquisition unit; The historical data acquisition unit uses an industrial intelligent gateway to collect historical key operating data and historical production data of each non-faulty CNC lathe in the CNC lathe cluster during unplanned downtime periods. The historical data acquisition unit is used to acquire historical key operation datasets and historical production datasets for each non-faulty CNC lathe during unplanned downtime periods.

8. The IoT-based CNC lathe cluster status analysis system according to claim 7, characterized in that: The linkage impact index analysis module includes a dynamic deviation dataset acquisition unit, a mutation feature matrix determination unit, and a linkage impact index analysis unit. The dynamic deviation dataset acquisition unit acquires, based on time series, the first dynamic deviation dataset between the historical key operation dataset and the theoretical key operation dataset of each non-faulty CNC lathe during the unplanned downtime period, and the second dynamic deviation dataset between the historical production dataset and the theoretical production dataset. The mutation feature matrix determination unit determines the mutation feature matrix of each non-faulty CNC lathe during unplanned downtime based on the temporal continuity between the abnormal data in the first dynamic deviation dataset and the abnormal data in the second dynamic deviation dataset. The linkage impact index analysis unit analyzes the linkage impact index of the faulty CNC lathe on each non-faulty CNC lathe based on the mutation characteristic matrix of each non-faulty CNC lathe during unplanned downtime.

9. The IoT-based CNC lathe cluster status analysis system according to claim 8, characterized in that: The data lathe cluster operation and maintenance status analysis module includes a data acquisition unit and an operation and maintenance status analysis unit; The data acquisition unit is used to acquire the real-time linkage impact index of the faulty CNC lathe on each non-faulty CNC lathe, as well as the unfinished workload of each non-faulty CNC lathe during the remaining planned processing time. The operation and maintenance analysis unit analyzes the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster based on the real-time operation and maintenance indicators of each non-faulty CNC lathe.

10. The IoT-based CNC lathe cluster status analysis system according to claim 9, characterized in that: The operation and maintenance sequence determination module includes an operation and maintenance classification unit and an operation and maintenance sequence determination unit; The operation and maintenance classification unit obtains a first operation and maintenance set and a second operation and maintenance set for the CNC lathe cluster based on the real-time operation and maintenance status of each non-faulty CNC lathe in the CNC lathe cluster. The maintenance sequence determination unit determines the maintenance sequence of each non-faulty CNC lathe in the CNC lathe cluster based on the reordered first and second maintenance sets.

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