Machine tool effective working hour metering method, device, equipment and storage medium

Abstract

The application discloses a machine tool effective working hour metering method, device, equipment and storage medium, and relates to the technical field of numerical control machine tool control. The method comprises the following steps: after detecting a permitted personnel identifier, collecting multi-dimensional running data of a target machine tool in a running process; obtaining a current interactive operation scene triggered by the multi-dimensional running data; and determining effective running working hours of the target machine tool according to the current interactive operation scene and a preset working hour metering rule. The application binds user operation behavior characteristics and machine tool equipment state data, dynamically matches a man-machine interactive operation scene through collected multi-dimensional running data, and accurately meters machine tool effective processing working hours according to a current interactive operation scene recognized in real time and a preset working hour metering rule which is individually set according to different interactive operation scenes. Compared with an existing working hour metering method which relies on manual recording or independent sensors, the application can distinguish different operation scenes, realize scene-based intelligent decision-making, and improve working hour metering accuracy.

Description

Technical Field

[0001] This application relates to the field of CNC machine tool control technology, and in particular to a method, device, equipment and storage medium for measuring the effective working time of a machine tool. Background Technology

[0002] Traditional machine tool time measurement methods usually rely on manual recording or independent sensors, which are easily affected by data acquisition delays. At the same time, traditional methods cannot distinguish between effective cutting and non-productive operations (such as debugging and tool changing), resulting in large errors in time statistics.

[0003] Therefore, how to improve the measurement accuracy of machine tool effective working hours has become an urgent problem to be solved. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, equipment, and storage medium for measuring the effective working time of machine tools, aiming to solve the technical problem of how to improve the measurement accuracy of the effective working time of machine tools.

[0005] To achieve the above objectives, this application proposes a method for measuring the effective working time of a machine tool, the method comprising:

[0006] Once the authorized personnel identifier is detected, multi-dimensional operational data of the target machine tool during operation is collected.

[0007] Obtain the current interactive operation scenario triggered by the multi-dimensional runtime data;

[0008] The effective operating hours of the target machine tool are determined based on the current interactive operation scenario and the preset time measurement rules.

[0009] In one embodiment, the multidimensional operational data includes machining status data, motion status data, tool management data, or alarm log data.

[0010] In one embodiment, the current interactive operation scenario includes: a program debugging scenario, a batch processing scenario, or a technical interruption scenario;

[0011] The step of obtaining the current interactive operation scenario triggered by the multi-dimensional runtime data includes:

[0012] When the target machine tool is in manual data input mode and the frequency of point coordinate modification exceeds a set threshold, the current interactive operation scenario is determined to be the program debugging scenario; or

[0013] When the target machine tool is in automatic operation mode and no abnormal interruption is detected in the continuous workpiece counting, the current interactive operation scenario is determined to be the batch processing scenario; or

[0014] When the remaining tool life of the target machine tool is lower than the warning value and a forced tool change event is triggered, the current interactive operation scenario is determined to be the technical interruption scenario.

[0015] In one embodiment, the effective operating time includes preparation time, processing time, and early warning maintenance time;

[0016] The step of determining the effective operating hours of the target machine tool based on the current interactive operation scenario and preset time measurement rules includes:

[0017] When the current interactive operation scenario is the program debugging scenario, the preparation time is determined based on the manual runtime and the real-time coordinate switching frequency.

[0018] When the current interactive operation scenario is the batch processing scenario, the processing time is determined based on the real-time workpiece count and the feed speed threshold.

[0019] When the current interactive operation scenario is the technical interruption scenario, the tool change interruption duration is determined as the early warning maintenance man-hour.

[0020] In one embodiment, after determining the effective operating hours of the target machine tool based on the current interactive operation scenario and preset time measurement rules, the method further includes:

[0021] Obtain the current operating wear data of the target machine tool;

[0022] The effective operating time is optimized by using the current operating loss data to obtain the optimized effective operating time of the target machine tool.

[0023] In one embodiment, the current operational loss data includes: equipment performance loss data and personnel efficiency loss data;

[0024] The step of obtaining the current operating wear data of the target machine tool includes:

[0025] Obtain the total machine tool operating hours of the target machine tool;

[0026] The equipment performance loss data is determined based on the effective cutting efficiency ratio, performance downtime, and the total operating time of the machine tool.

[0027] The personnel efficiency loss data is determined based on the equipment response delay, speed setting deviation, operation downtime, abnormal tool change time, and the total machine tool operating time.

[0028] In one embodiment, the step of optimizing the effective operating time using the current operating loss data to obtain the optimized effective operating time of the target machine tool includes:

[0029] Obtain the personnel login time and machine tool startup time when the target machine tool is started;

[0030] The current time reference deviation is determined based on the personnel login time and the machine tool startup time.

[0031] The optimized effective working hours of the target machine tool are determined based on the current time base deviation, the equipment performance loss data, and the effective operating hours.

[0032] Furthermore, to achieve the above objectives, this application also proposes a machine tool effective working time measuring device, the device comprising:

[0033] The data acquisition module is used to collect multi-dimensional operational data of the target machine tool during operation after an authorized personnel identifier is detected.

[0034] The scene detection module is used to obtain the current interactive operation scene triggered by the multi-dimensional running data;

[0035] The working time measurement module is used to determine the effective operating time of the target machine tool based on the current interactive operation scenario and preset working time measurement rules.

[0036] In addition, to achieve the above objectives, this application also proposes a machine tool effective working time measurement device, the device comprising: a memory, a processor, and a machine tool effective working time measurement program stored in the memory and executable on the processor, the machine tool effective working time measurement program being configured to implement the steps of the machine tool effective working time measurement method mentioned above.

[0037] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a machine tool effective working time measurement program is stored, and when the machine tool effective working time measurement program is executed by a processor, it implements the steps of the machine tool effective working time measurement method mentioned above.

[0038] This application provides a method, apparatus, device, and storage medium for measuring the effective working time of a machine tool. The method includes: upon detecting an authorized personnel identifier, collecting multi-dimensional operational data of the target machine tool during operation; acquiring the current interactive operation scenario triggered by the multi-dimensional operational data; and determining the effective operating time of the target machine tool based on the current interactive operation scenario and preset time measurement rules. This application can bind user operation behavior characteristics with machine tool status data, dynamically match human-machine interaction operation scenarios through the collected multi-dimensional operational data, and accurately measure the effective processing time of the machine tool based on the real-time distinguished current interactive operation scenario and preset time measurement rules personalized for different interactive operation scenarios. Compared to existing time measurement methods that rely on manual recording or independent sensors, this application can effectively distinguish different operation scenarios, achieve scenario-based intelligent decision-making, and improve the accuracy of time measurement. Attached Figure Description

[0039] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a first flowchart illustrating the first embodiment of the machine tool effective working time measurement method of this application;

[0042] Figure 2 This is a second flowchart illustrating the first embodiment of the machine tool effective working time measurement method of this application;

[0043] Figure 3 This is a schematic diagram of the third process of the first embodiment of the machine tool effective working time measurement method of this application;

[0044] Figure 4 This is a schematic diagram of the first process of the second embodiment of the machine tool effective working time measurement method of this application;

[0045] Figure 5 This is a second flowchart illustrating the second embodiment of the machine tool effective working time measurement method of this application;

[0046] Figure 6 This is a schematic diagram of the module structure of the machine tool effective working time measuring device according to an embodiment of this application;

[0047] Figure 7 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the machine tool effective working time measurement method in the embodiments of this application.

[0048] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0049] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0050] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0051] The main solution of this application is: after detecting the authorized personnel identifier, collect multi-dimensional operation data of the target machine tool during operation; obtain the current interactive operation scenario triggered by the multi-dimensional operation data; and determine the effective operating time of the target machine tool according to the current interactive operation scenario and the preset time measurement rules.

[0052] Traditional machine tool time measurement methods usually rely on manual recording or independent sensors, which are easily affected by data acquisition delays. At the same time, traditional methods cannot distinguish between effective cutting and non-productive operations (such as debugging and tool changing), resulting in large errors in time statistics.

[0053] This application proposes a system and method for dynamic calibration and performance evaluation of machine tool operation time through deep coupling analysis of native data from the CNC system and human operational behavior. This embodiment binds user operational behavior characteristics with machine tool status data, dynamically matches human-machine interaction scenarios through collected multi-dimensional operational data, and accurately measures the effective machining time of the machine tool based on the real-time distinguished current interaction scenario (such as debugging / machining / early warning tool change) and pre-set personalized time calculation rules for different interaction scenarios. Therefore, compared with existing time measurement methods that rely on manual recording or independent sensors, this embodiment can effectively distinguish between effective cutting time and non-productive operations such as program debugging and tool changing, differentiate different operation scenarios, achieve scenario-based intelligent decision-making, and improve the accuracy of time measurement.

[0054] It should be noted that the executing entity in this embodiment can be a machine tool effective working time measurement system, or a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a machine tool effective working time measurement device capable of performing the above functions. This embodiment does not specifically limit it in this way. The following uses a machine tool effective working time measurement device (hereinafter referred to as the measurement device) as the executing entity to describe this embodiment and the following embodiments.

[0055] Based on this, embodiments of this application provide a method for measuring the effective working hours of a machine tool, referring to... Figure 1 , Figure 1 This is a first flowchart illustrating the first embodiment of the machine tool effective working time measurement method of this application.

[0056] In this embodiment, the method for measuring the effective working time of a machine tool includes steps S10 to S30:

[0057] Step S10: After detecting the authorized personnel identifier, collect multi-dimensional operational data of the target machine tool during operation;

[0058] It is easy to understand that this embodiment can pre-authorize personnel for each machine tool based on an operation permission whitelist (G-code editing level). That is, the aforementioned authorized personnel identifier can be the identification information of personnel who are authorized to operate the target machine tool. In an industrial production environment, to ensure safety and standardized operation, only personnel with the corresponding qualifications and authorization can operate the machine tool. This identifier can be verified in various ways, such as using cards with Radio Frequency Identification (RFID) technology, fingerprint recognition, password input, etc. When an authorized personnel identifier is detected, it means that the authorized personnel are ready to operate the machine tool, and the equipment can begin subsequent data acquisition.

[0059] During machine tool operation, measuring equipment can collect various data related to machine tool operation in real time. This data reflects the machine tool's operating status from different dimensions, providing basic information for subsequent time measurement. The data acquisition process can be achieved through sensors, data acquisition cards, and other devices installed on the machine tool or its control system, converting various physical quantities and status information during machine tool operation into electrical or digital signals for subsequent processing and analysis.

[0060] In addition, when collecting operational data, in order to support system data adaptation for multiple versions such as Fanuc 31i, Siemens 840D, and Mitsubishi M700, in this embodiment, the metering device can run a Linux system on an embedded hardware gateway and integrate dedicated protocol drivers for various CNC devices to achieve unified collection of data from different brands of CNC systems.

[0061] In one feasible implementation, the multidimensional operating data in this embodiment includes machining status data, motion status data, tool management data, or alarm log data.

[0062] Understandably, this embodiment can construct a multi-version CNC system protocol parsing layer to collect machining status data, motion status data, tool management data, and alarm log data in real time. Among these, the machining status data mainly reflects whether the target machine tool is performing machining operations and the specific machining mode, with a sampling frequency of 50ms. For example, it includes the machine tool's start, stop, and pause states, as well as whether it is in an automatic machining cycle (Cycle Start) or manual data input (MDI) mode. This data can be used for equipment start / stop determination, i.e., determining whether the machine tool is in an actual production processing stage, thus providing a basic time division basis for time measurement. It can also filter out non-production states such as equipment standby and debugging, accurately defining machining time boundaries.

[0063] Motion status data can involve the motion of each axis of the machine tool, such as position, speed, and acceleration parameters, with a sampling frequency of up to 50ms. This data can describe in detail the motion trajectory and dynamic behavior of the machine tool during machining, which is of great significance for judging whether the machining process is normal and calculating the effective cutting time. For example, by analyzing whether the feed rate exceeds a set threshold, it can be determined whether the machine tool is in an effective cutting state, eliminating invalid strokes such as idle cutting and rapid traverse, thereby accurately measuring the effective cutting time.

[0064] The aforementioned tool management data can include information such as the currently used tool number and the remaining tool life. Data collection is triggered when a tool changeover event occurs. This data helps to understand tool usage and replacement needs, allowing for the rational scheduling of tool change times and the deduction of non-productive time such as tool changes from time measurement, thus improving the accuracy of time measurement. Furthermore, tool life data can be combined with information such as the number of workpieces processed to evaluate tool utilization efficiency and machining costs.

[0065] The final alarm log data records various alarm events that occur during the operation of the target machine tool, including alarm codes, timestamps indicating the occurrence time, duration, and other detailed information. Alarm log data is crucial for analyzing machine tool malfunctions and determining abnormal downtime and causes. Analysis of the alarm logs allows for accurate identification of downtime causes based on fault IDs (identification) and classification (process / equipment / operational issues). It also enables effective statistics on downtime caused by faults, which is then removed from effective operating hours, ensuring the accuracy of time measurement.

[0066] Step S20: Obtain the current interactive operation scenario triggered by the multi-dimensional running data;

[0067] It is important to understand that this embodiment can pre-set different interactive operation scenarios based on various interactions between the operator and the machine tool during operation. Different operational behaviors will lead to different data change patterns, thereby triggering the corresponding interactive operation scenario recognition. For example, when the operator is debugging the program, they will input commands and adjust parameters on the machine tool's manual input interface. The data collected at this time will show specific characteristics, and the measuring device can then identify that the current operation is in a program debugging scenario. Therefore, the measuring device can collect multi-dimensional operating data of the target machine tool in real time, including the current operating mode and current operating status, coordinate parameters, workpiece count, remaining tool life, and tool failure parameters, to determine the current interactive operation scenario.

[0068] In one feasible implementation, the current interactive operation scenario includes: a program debugging scenario, a batch processing scenario, or a technical interruption scenario; refer to Figure 2 , Figure 2 This is a second flowchart illustrating the first embodiment of the machine tool effective working time measurement method of this application. In this embodiment, step S20 may include steps A1 to A3:

[0069] Step A1: When the target machine tool is in manual data input mode and the frequency of point coordinate modification exceeds the set threshold, the current interactive operation scenario is determined to be the program debugging scenario.

[0070] Or in step A2, when the target machine tool is in automatic operation mode and no abnormal interruption is detected in the continuous workpiece counting, the current interactive operation scenario is determined to be the batch processing scenario;

[0071] Alternatively, in step A3, when the remaining tool life of the target machine tool is lower than the warning value and a forced tool change event is triggered, the current interactive operation scenario is determined to be the technical interruption scenario.

[0072] It should be noted that the above-described program debugging scenario corresponds to the process of operators debugging the machining program of a machine tool. In this scenario, operators typically frequently input commands, modify parameters, and perform short-distance manual movements to optimize the machining program and ensure machining accuracy. The Manual Data Input (MDI) mode can be an operating mode of the target machine tool, allowing operators to manually input machining commands and parameters. In this mode, operators can flexibly adjust and test the machining program, but the machine tool's operating speed is relatively slow, and machining efficiency is low.

[0073] Furthermore, during program debugging, operators need to continuously adjust the coordinates of various points along the processing path to achieve the desired processing effect. Therefore, when the measuring equipment detects that the number of coordinate modifications per unit time exceeds a preset threshold, the system can identify this high-frequency coordinate modification behavior and thus determine that the system is currently in a program debugging scenario. For example, if the threshold is set to 5 coordinate modifications per minute, and the actual detected coordinate modification frequency exceeds this value, the program debugging scenario will be identified.

[0074] The aforementioned batch processing scenario refers to the process where a target machine tool continuously processes multiple identical workpieces according to a pre-set processing program. Automatic operation mode is a machine tool operating mode where the machine tool automatically executes processing tasks according to a stored processing program without manual intervention. In this mode, the machine tool operates at a higher speed, the processing is more stable, and efficient batch production can be achieved. During batch processing, the machine tool automatically counts the completed workpieces. If the workpiece count continuously increases over a period of time without processing interruptions due to malfunctions, tool changes, or other reasons, it indicates that the machine tool is in a stable batch processing state. Therefore, the metering equipment can determine whether a batch processing scenario is in progress by monitoring changes in the workpiece count and abnormal interruption signals during the processing.

[0075] It's important to understand that cutting tools gradually wear down during use. When the wear reaches a certain level, it affects machining quality and efficiency, necessitating timely tool replacement. Therefore, the aforementioned technical interruption scenario corresponds to the situation where the tool life of the target machine tool reaches a warning value, triggering a forced tool change event.

[0076] It's easy to understand that tool manufacturers, or based on actual machining experience, will set a remaining life warning value for each tool. When the actual remaining life of the tool falls below this warning value, it means the tool is about to fail and needs to be replaced as soon as possible. Therefore, to ensure machining quality and safe production, it can be pre-set that when the remaining tool life falls below the warning value, the machine tool control system will automatically trigger a forced tool change event, suspend the current machining operation, and prompt the operator to change the tool. This event is triggered to prevent machining accidents and decreased machining quality due to excessive tool wear, and also provides a clear interruption signal for time measurement, so as to accurately count tool change time and technical downtime.

[0077] Step S30: Determine the effective operating hours of the target machine tool based on the current interactive operation scenario and the preset time measurement rules.

[0078] It is easy to understand that data in different interactive operation scenarios have different meanings. This embodiment may need to determine the effective machine tool working hours using appropriate time measurement rules based on the characteristics of the scenario. In one feasible implementation, the effective operating time includes preparation time, processing time, and early warning maintenance time; refer to... Figure 3 , Figure 3 This is a schematic diagram of the third process of the first embodiment of the machine tool effective working time measurement method of this application. In this embodiment, step S30 may include steps B1 to B3:

[0079] Step B1: When the current interactive operation scenario is the program debugging scenario, the preparation time is determined based on the manual running time and the real-time coordinate switching frequency.

[0080] Step B2: When the current interactive operation scenario is the batch processing scenario, the processing time is determined based on the real-time workpiece count and the feed speed threshold.

[0081] Step B3: When the current interactive operation scenario is the technical interruption scenario, the tool change interruption duration is determined as the early warning maintenance man-hour.

[0082] It is easy to understand that in this embodiment, the effective operating time can be the sum of preparation time, processing time, and early warning maintenance time. Preparation time can be the necessary preparation time spent by operators performing various debugging preparations in a program debugging scenario, including but not limited to the time required for starting the machine tool, installing and calibrating tools, inputting and adjusting the machining program, and performing trial cuts. Although these tasks are important components of the machining process, they do not directly produce the processed product; therefore, they need to be partially or entirely converted into preparation time according to certain rules and included in the effective operating time. For example, in a program debugging scenario, the measuring device can determine the portion of the manual running time corresponding to the target machine tool where the real-time coordinate switching frequency exceeds a set threshold as preparation time.

[0083] The aforementioned processing time refers to the actual time the target machine tool spends processing the workpiece in a batch processing scenario. During this time period, the machine tool operates in automatic mode, continuously performing cutting, shaping, and other processing operations on the workpiece according to a preset processing program.

[0084] In batch processing scenarios, machine tool runtime can be directly converted into effective production time. Real-time workpiece count reflects the number of workpieces completed during batch processing, while the feed rate threshold distinguishes between effective cutting time and non-cutting auxiliary time. Therefore, by monitoring whether the real-time feed rate of the target machine tool exceeds the feed rate threshold, and combining this with the increase in the workpiece count, the metering equipment can accurately determine the effective cutting time for each workpiece and accumulate these times to obtain the total effective runtime. This method effectively avoids including non-cutting times such as idle time and rapid traverse in the machining time, accurately correlating the collected multi-dimensional runtime data with actual production time, thus improving the accuracy of time measurement.

[0085] It's important to understand that in technically disruptive scenarios, the downtime caused by tool life warnings and forced tool changes constitutes the aforementioned warning maintenance man-hours. While tool changing is a necessary maintenance operation, it represents a non-productive interruption to the production process. Measuring equipment can directly determine the tool changing downtime in this scenario as warning maintenance man-hours based on timestamps and reasonably incorporate it into the man-hour measurement system, allowing for a more comprehensive assessment of machine tool operating efficiency and maintenance costs.

[0086] In addition, when a conflict scenario is detected in the target machine tool, such as when the equipment is not swiping the card or the program segment remains unchanged, the measuring device can trigger an audible and visual alarm and suspend the execution of G-code; or compare the tool compensation value with the coordinate change to determine the false dead state, issue a machine tool operation warning, and suspend the collection of working time data.

[0087] In this embodiment, user operation behavior characteristics can be bound to machine tool status data. Multi-dimensional operational data is collected to dynamically match human-machine interaction scenarios. Based on the real-time differentiation of the current interaction scenario (such as debugging / machining / early tool change) and pre-set personalized time calculation rules for different interaction scenarios, the effective machining time of the machine tool is accurately measured. Therefore, compared to existing time measurement methods that rely on manual recording or independent sensors, this embodiment can effectively distinguish between effective cutting time and non-productive operations such as program debugging and tool change, achieving scenario-based intelligent decision-making and improving the accuracy of time measurement.

[0088] This embodiment provides a method for measuring the effective working time of a machine tool. The method includes: after detecting an authorized personnel identifier, collecting multi-dimensional operating data of the target machine tool during operation; the multi-dimensional operating data includes machining status data, motion status data, tool management data, or alarm log data. The current interactive operation scenario includes: program debugging scenario, batch processing scenario, or technical interruption scenario; when the target machine tool is in manual data input mode and the frequency of point coordinate modification exceeds a set threshold, the current interactive operation scenario is determined to be the program debugging scenario; or when the target machine tool is in automatic operation mode and the continuous workpiece counting is not abnormally interrupted, the current interactive operation scenario is determined to be the batch processing scenario; or when the remaining tool life of the target machine tool is lower than a warning value and a forced tool change event is triggered, the current interactive operation scenario is determined to be the technical interruption scenario. Effective operating time includes preparation time, machining time, and early warning maintenance time. When the current interactive operation scenario is a program debugging scenario, the preparation time is determined based on the manual running time and real-time coordinate switching frequency. When the current interactive operation scenario is a batch machining scenario, the machining time is determined based on the real-time workpiece count and feed rate threshold. When the current interactive operation scenario is a technical interruption scenario, the tool change interruption time is determined as the early warning maintenance time. The effective operating time is optimized by using current wear data to obtain the effective operating time of the machine tool. This embodiment can bind user operation behavior characteristics with machine tool status data, dynamically match human-machine interaction operation scenarios through collected multi-dimensional operating data, and accurately measure the effective machining time of the machine tool based on the real-time distinguished current interactive operation scenario (such as debugging / machining / early warning tool change) and the time calculation rules pre-set according to different interactive operation scenarios. Therefore, compared with existing time measurement methods that rely on manual recording or independent sensors, this embodiment can effectively distinguish between effective cutting time and non-productive operations such as program debugging and tool changing, realize scenario-based intelligent decision-making, and improve the accuracy of time measurement.

[0089] Based on the first embodiment of this application, in the second embodiment of this application, the same or similar content as the first embodiment described above can be referred to the above description, and will not be repeated hereafter.

[0090] Based on the first embodiment, please refer to Figure 4 , Figure 4 This is a first flowchart illustrating the second embodiment of the machine tool effective working time measurement method of this application. In this embodiment, steps S30 and S50 are included after step S40:

[0091] Step S40: Obtain the current operating wear data of the target machine tool;

[0092] Step S50: Optimize the effective operating time using the current operating loss data to obtain the optimized effective operating time of the target machine tool.

[0093] It is easy to understand that during machine tool operation and time generation, various factors such as equipment performance and operator habits lead to the loss of effective working hours. This loss data reflects the occupation of various non-productive times during actual production and needs to be deducted or adjusted from the initial effective working hours to obtain a more accurate effective machine tool processing time. Therefore, this embodiment can correct and adjust the initially calculated effective operating time by introducing current loss data. At this time, the metering equipment can deduct the corresponding lost time from the effective operating time according to different loss types and degrees, or perform weighted processing on the initial effective working hours, based on certain calculation rules, to obtain a machine tool effective operating time that is closer to the actual effective production time. This step is a crucial step in improving the accuracy of time measurement, enabling the time measurement results to more accurately reflect the actual production efficiency and operating status of the machine tool.

[0094] In one feasible implementation, the current operational loss data includes: equipment performance loss data and personnel efficiency loss data; in this embodiment, step S40 may include steps C1~C3:

[0095] Step C1: Obtain the total machine tool operating hours of the target machine tool;

[0096] Step C2: Determine the equipment performance loss data based on the effective cutting efficiency ratio, performance downtime, and total machine tool operating hours;

[0097] Step C3: Determine the personnel efficiency loss data based on the equipment response delay duration, speed setting deviation, operation downtime, abnormal tool change time, and the total machine tool operating time.

[0098] It is easy to understand that in this embodiment, the total machine tool operating time of the target machine tool can first be determined based on the time difference between the operator logging into and out of the machine tool system using the authorized personnel identifier. The aforementioned equipment performance loss data can be the effective working time loss caused by the performance factors of the target machine tool itself. These losses mainly stem from mechanical wear, electrical faults, and aging control systems, preventing the machine tool from achieving ideal processing efficiency and performance indicators during operation.

[0099] It's important to understand that the aforementioned effective cutting efficiency ratio can be considered an indicator of machine tool cutting efficiency, obtained by comparing the average actual feed rate with the programmed feed rate. The average actual feed rate is usually lower than the programmed feed rate due to factors such as equipment aging and mechanical transmission errors. The difference reflects the degree of performance loss during the cutting process. For example, if the target machine tool's programmed feed rate is 2000 mm / min, while the actual average feed rate is 1800 mm / min, then the effective cutting efficiency ratio is 90% (1800 / 2000), meaning the target machine tool experiences a 10% loss in cutting efficiency.

[0100] The aforementioned performance downtime refers to the downtime of a machine tool caused by equipment performance issues (such as servo overheating, excessive spindle vibration, etc.). These downtime events are usually caused by hardware failures or performance degradation, requiring repair or adjustment to restore production. Performance downtime directly leads to lost production time and is a significant indicator of equipment performance degradation; it can be determined based on alarm log data.

[0101] In this embodiment, the equipment performance loss data can be expressed as: (1 - effective cutting efficiency ratio) * total machine tool operating time + performance downtime. For example, assuming the target machine tool's programmed feed rate is 2000 mm / min and the actual average speed is 1800 mm / min, the effective cutting efficiency ratio is 90%, and the performance downtime is 17 minutes due to spindle overheating. The total machine tool operating time is 6 hours. Then, the equipment performance loss data = ((1 - 90%) * 360 min) + 17 min = 53 min.

[0102] It's easy to understand that the aforementioned personnel efficiency loss data can be attributed to factors such as operators' operating habits, skill levels, and work attitudes, resulting in the loss of effective working hours. Personnel efficiency loss data reflects the impact of human factors on production efficiency and is a key focus for time optimization and personnel training. Specifically, the aforementioned equipment response delay can be the time interval between the operator completing the instruction input and the machine tool actually starting to execute the corresponding operation. This may be due to the operator's unfamiliarity with the machine tool's response speed, or correcting errors. Longer equipment response delays reduce production efficiency and increase non-productive time.

[0103] The aforementioned speed setting deviation refers to the degree of discrepancy between the feed rate set by the operator and the feed rate set in the programming. Due to factors such as the operator's experience and understanding of the machining process, a conservative feed rate setting may be chosen, resulting in the actual machining speed being lower than the ideal speed, thus causing a speed setting deviation. This deviation reflects the efficiency loss of the operator during the operation.

[0104] Furthermore, the aforementioned downtime can be due to operator errors (such as incorrect coordinate system settings or overtravel). Operator errors not only affect production schedules but can also damage machine tools and workpieces, representing a significant source of personnel inefficiency. Abnormal tool change time can be the time spent performing tool changes prematurely. This may be due to inaccurate judgment of tool life by the operator or concerns about tool damage affecting machining quality. Abnormal tool change time increases unnecessary maintenance work and reduces production efficiency.

[0105] In this embodiment, the personnel efficiency loss data can be expressed as: Personnel efficiency loss data = Equipment response delay time + Speed ​​setting deviation * Total machine tool operating time + Operation downtime + Abnormal tool change time. For example, assuming the target machine tool's average response delay is 8 minutes / shift, with three shifts per day; the target machine tool's programmed feed rate is 2000 mm / min, the actual average feed rate is 1500 mm / min, and the total machine tool operating time is 6 hours, then the speed setting deviation * Total machine tool operating time = (2000-1500) / 2000 * 3600 min = 900 min; the operation downtime is 45 min; the number of abnormal tool changes is 5, and the average tool change time is 3 min, that is, the abnormal tool change time is: 5 times x 3 min = 15 min, then the personnel efficiency loss data = 8 * 3 + 900 + 45 + 15 = 984 min.

[0106] Finally, this embodiment can correct and adjust the initially calculated effective operating time by introducing the current loss data, that is, the optimized operating time = effective operating time - equipment performance loss data - personnel efficiency loss data.

[0107] Furthermore, this embodiment can distinguish between mechanical faults and operational errors based on the joint analysis of alarm codes and coordinate offsets. For example, this embodiment can use the alarm code trigger time as a baseline, extending the historical data collection by 50ms before and after, reading parameters such as the equipment program segment number, program step content, historical coordinate data, spindle current changes, and tooling fixture pressure changes, constructing a fault analysis, and classifying different faults according to the alarm code, including but not limited to: mold-related faults, program errors, sensor faults, and equipment hardware faults.

[0108] In one feasible implementation, refer to Figure 5 , Figure 5 This is a second flowchart illustrating the second embodiment of the machine tool effective working time measurement method of this application. In this embodiment, step S50 may include steps D1 to D3:

[0109] Step D1: Obtain the personnel login time and machine tool startup time when the target machine tool is started;

[0110] Step D2: Determine the current time reference deviation based on the personnel login time and the machine tool start-up time;

[0111] Step D3: Determine the optimized effective working hours of the target machine tool based on the current time reference deviation, the equipment performance loss data, and the effective operating hours.

[0112] Understandably, the aforementioned personnel login time can be the time when the operator logs into the machine tool system using an authorized personnel ID. This signifies that the operator has officially begun interacting with the machine tool and is preparing to perform machining operations. The aforementioned machine tool startup time can be the time when the machine tool is powered on and begins operation. After startup, the machine tool needs to undergo a series of initialization operations and self-check procedures before entering normal working condition.

[0113] The aforementioned deviation in the current time reference determined based on the time difference between the operator's login time and the machine tool's startup time may be due to accumulated errors from long-term operation caused by the RFID authentication unit (independent hardware) and the CNC system (embedded device) using their own clock sources. This deviation reflects the time interval between the operator logging into the system and actually starting the machine tool, which may include the time required for the operator to perform preparatory work, check the machine tool status, etc. For example, the operator's corresponding RFID card swipe timestamp is (T1=2025-06-17 09:00:00.123), while the CNC system's power-on signal timestamp is (T2=2025-06-17 09:00:00.250), and there may be a deviation of 127ms between the two.

[0114] Therefore, this embodiment needs to more accurately determine the starting point of the machine tool's actual effective operating time by using the current time base deviation, providing a more precise time base for time measurement. At this time, the measurement equipment can periodically (e.g., every 5 minutes) broadcast NTP protocol time synchronization packets to all terminals (i.e., the target machine tool's CNC system and identification unit), forcibly synchronizing the system clocks of each device to a unified time base. This controls the timestamp deviation between personnel operation (RFID card swiping) and equipment status (CNC system power-on) within ±10ms, meeting the accuracy requirements for event sequence representation in industrial scenarios. This allows for a more comprehensive and accurate determination of the machine tool's actual effective operating time, making the time measurement results more consistent with actual production conditions and providing a reliable basis for production management and cost accounting.

[0115] Furthermore, in this embodiment, the metering device can also periodically store an NTP (Network Time Protocol) time reference (e.g., 72 hours of offline storage) so that when a network interruption is detected, the current reference can be calculated based on the last synchronization time point, and a hardware clock compensation algorithm can be used to suppress drift until the network is restored and the time reference is immediately resynchronized. This avoids the signal failure problem of traditional GPS (Global Positioning System) time synchronization schemes in the metal environment of the workshop, ensuring the continuity of time measurement in complex industrial environments.

[0116] In this embodiment, time reference deviations between multiple systems can also be eliminated by periodically broadcasting NTP time synchronization packets. A global time axis can be constructed based on NTP, enabling personnel behavior, equipment status, and abnormal events to be correlated and analyzed in the same time coordinate system, thus ensuring the spatiotemporal alignment accuracy of the optimized effective working hours of the target machine tool based on human-machine collaborative data calculation.

[0117] In summary, this embodiment constructs a more accurate time measurement benchmark by deeply integrating the native data of the CNC system. Compared with solutions that rely solely on external sensors, it has significant advantages in terms of data real-time performance and system compatibility.

[0118] This embodiment discloses current operational loss data, including: equipment performance loss data and personnel efficiency loss data; it obtains the total machine tool operating time of the target machine tool; it determines equipment performance loss data based on the effective cutting efficiency ratio, performance downtime, and total machine tool operating time; and it determines personnel efficiency loss data based on equipment response delay time, speed setting deviation, operation downtime, abnormal tool change time, and total machine tool operating time. It obtains the personnel login time and machine tool startup time when the target machine tool starts; it determines the current time reference deviation based on the personnel login time and machine tool startup time; and it determines the optimized effective operating time of the target machine tool based on the current time reference deviation, equipment performance loss data, and effective operating time. This embodiment, through deep integration of native CNC system data, constructs a more accurate time measurement benchmark, which has significant advantages in data real-time performance and system compatibility compared to solutions that solely rely on external sensors.

[0119] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the effective working time measurement method of machine tools in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0120] This application also provides a machine tool effective working time measuring device, please refer to... Figure 6 , Figure 6 This is a schematic diagram of the module structure of the machine tool effective working time measuring device according to an embodiment of this application. In this embodiment, the machine tool effective working time measuring device includes:

[0121] The data acquisition module 601 is used to collect multi-dimensional operating data of the target machine tool during operation after detecting the authorized personnel identifier;

[0122] Scene detection module 602 is used to obtain the current interactive operation scene triggered by the multi-dimensional running data;

[0123] The time measurement module 603 is used to determine the effective operating time of the target machine tool based on the current interactive operation scenario and the preset time measurement rules.

[0124] As one possible implementation, in this embodiment, the multi-dimensional operating data collected by the data acquisition module includes: machining status data, motion status data, tool management data, or alarm log data.

[0125] As one possible implementation scenario, the current interactive operation scenario includes: program debugging scenario, batch processing scenario, or technical interruption scenario;

[0126] In this embodiment, the scene detection module 602 is further configured to determine the current interactive operation scene as the program debugging scene when the target machine tool is in manual data input mode and the frequency of point coordinate modification exceeds a set threshold.

[0127] Alternatively, the scene detection module 602 may be further configured to determine that the current interactive operation scene is the batch processing scene when the target machine tool is in automatic operation mode and the continuous workpiece counting is detected to be without abnormal interruption;

[0128] Alternatively, the scene detection module 602 may be further configured to determine that the current interactive operation scene is the technical interruption scene when the remaining tool life of the target machine tool is lower than the warning value and a forced tool change event is triggered.

[0129] As one possible implementation, the effective operating time includes preparation time, processing time, and early warning maintenance time;

[0130] In this embodiment, the time measurement module 603 is also used to determine the manual running time as preparation time when the current interactive operation scenario is the program debugging scenario;

[0131] The time measurement module 603 is also used to determine the processing time based on the real-time workpiece count and the feed speed threshold when the current interactive operation scenario is the batch processing scenario;

[0132] The time measurement module 603 is also used to determine the tool change interruption duration as early warning maintenance time when the current interactive operation scenario is the technical interruption scenario.

[0133] As one possible implementation, in this embodiment, the time measurement module 603 is further used to acquire the current operating loss data of the target machine tool; and to optimize the effective operating time using the current operating loss data to obtain the optimized effective operating time of the target machine tool.

[0134] As one possible implementation method, the current operating loss data includes: equipment performance loss data and personnel efficiency loss data;

[0135] In this embodiment, the time measurement module 603 is also used to determine equipment performance loss data based on the effective cutting efficiency ratio, performance downtime and total machine tool operating time; and to determine personnel efficiency loss data based on equipment response delay time, speed setting deviation, operation downtime, abnormal tool change time and total machine tool operating time.

[0136] As one possible implementation, in this embodiment, the time measurement module 603 is further configured to acquire the personnel login time and machine tool startup time when the target machine tool starts; determine the current time reference deviation based on the personnel login time and the machine tool startup time; and determine the optimized effective working time of the target machine tool based on the current time reference deviation, the equipment performance loss data, and the effective operating time.

[0137] The machine tool effective working time measuring device provided in this application, employing the machine tool effective working time measuring method in the above embodiments, can solve the technical problem of how to improve the measuring accuracy of machine tool effective working time. Compared with the prior art, the beneficial effects of the machine tool effective working time measuring device provided in this application are the same as the beneficial effects of the machine tool effective working time measuring method provided in the above embodiments, and other technical features in the machine tool effective working time measuring device are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0138] This application provides a machine tool effective working time measuring device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the machine tool effective working time measuring method in the above embodiment 1.

[0139] The following is for reference. Figure 7The diagram illustrates a structural schematic suitable for implementing a machine tool effective working time measuring device according to embodiments of this application. The machine tool effective working time measuring device in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The machine tool effective working time measuring device shown is merely an example and should not impose any limitation on the function and scope of use of the embodiments of this application.

[0140] like Figure 7 As shown, the machine tool effective working time measuring device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM) 1004. The random access memory 1004 also stores various programs and data required for the operation of the machine tool effective working time measuring device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the machine tool effective time measuring device to communicate wirelessly or wiredly with other devices to exchange data. Although machine tool effective time measuring devices with various systems are shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0141] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment disclosed in this application includes a machine tool effective working time measurement program product, which includes a machine tool effective working time measurement program carried on a computer-readable medium, the machine tool effective working time measurement program containing program code for performing the methods shown in the flowcharts. In such an embodiment, the machine tool effective working time measurement program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the machine tool effective working time measurement program is executed by processing device 1001, the functions defined above in the methods of the embodiments disclosed in this application are performed.

[0142] The machine tool effective working time measuring device provided in this application, employing the machine tool effective working time measuring method in the above embodiments, can solve the technical problem of how to improve the measuring accuracy of machine tool effective working time. Compared with the prior art, the beneficial effects of the machine tool effective working time measuring device provided in this application are the same as the beneficial effects of the machine tool effective working time measuring method provided in the above embodiments, and other technical features in this machine tool effective working time measuring device are the same as the features disclosed in the method of the previous embodiment, and will not be repeated here.

[0143] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0144] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0145] This application provides a storage medium having computer-readable program instructions (i.e., a machine tool effective working time measurement program) stored thereon, which are used to execute the machine tool effective working time measurement method in the above embodiments.

[0146] The storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of the storage medium may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0147] The aforementioned storage medium may be included in the machine tool effective time measuring device; or it may exist independently and not be assembled into the machine tool effective time measuring device.

[0148] The aforementioned storage medium carries one or more programs. When the aforementioned one or more programs are executed by the machine tool effective working time measuring device, the machine tool effective working time measuring device is enabled to measure the effective working time of the machine tool.

[0149] Machine tool effective working time measurement program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0150] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and machine tool effective time measurement program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0151] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0152] The readable storage medium provided in this application is a storage medium that stores computer-readable program instructions (i.e., machine tool effective working time measurement program) for executing the above-described machine tool effective working time measurement method, and can solve the technical problem of how to improve the measurement accuracy of machine tool effective working time. Compared with the prior art, the beneficial effects of the storage medium provided in this application are the same as the beneficial effects of the machine tool effective working time measurement method provided in the above embodiments, and will not be repeated here.

[0153] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for measuring the effective working time of a machine tool, characterized in that, The method includes: Once an authorized personnel identifier is detected, multi-dimensional operational data of the target machine tool during operation is collected; the multi-dimensional operational data includes machining status data, motion status data, tool management data, or alarm log data. The current interactive operation scenario triggered by the multi-dimensional runtime data is obtained; the current interactive operation scenario includes: program debugging scenario, batch processing scenario, or technical interruption scenario; The effective operating hours of the target machine tool are determined based on the current interactive operation scenario and the preset time measurement rules; the effective operating hours include preparation time, processing time, and early warning maintenance time. The step of obtaining the current interactive operation scenario triggered by the multi-dimensional runtime data includes: When the target machine tool is in manual data input mode and the frequency of point coordinate modification exceeds a set threshold, the current interactive operation scenario is determined to be the program debugging scenario; or When the target machine tool is in automatic operation mode and no abnormal interruption is detected in the continuous workpiece counting, the current interactive operation scenario is determined to be the batch processing scenario; or When the remaining tool life of the target machine tool is lower than the warning value and a forced tool change event is triggered, the current interactive operation scenario is determined to be the technical interruption scenario. The step of determining the effective operating hours of the target machine tool based on the current interactive operation scenario and preset time measurement rules includes: When the current interactive operation scenario is the program debugging scenario, the preparation time is determined based on the manual runtime and the real-time coordinate switching frequency. When the current interactive operation scenario is the batch processing scenario, the processing time is determined based on the real-time workpiece count and the feed speed threshold. When the current interactive operation scenario is the technical interruption scenario, the tool change interruption duration is determined as the early warning maintenance man-hour.

2. The machine tool effective working time measurement method as described in claim 1, characterized in that, After determining the effective operating hours of the target machine tool based on the current interactive operation scenario and the preset time measurement rules, the method further includes: Obtain the current operating wear data of the target machine tool; The effective operating time is optimized by using the current operating loss data to obtain the optimized effective operating time of the target machine tool.

3. The machine tool effective working time measurement method as described in claim 2, characterized in that, The current operational loss data includes: equipment performance loss data and personnel efficiency loss data; The step of obtaining the current operating wear data of the target machine tool includes: Obtain the total machine tool operating hours of the target machine tool; The equipment performance loss data is determined based on the effective cutting efficiency ratio, performance downtime, and the total operating time of the machine tool. The personnel efficiency loss data is determined based on the equipment response delay, speed setting deviation, operation downtime, abnormal tool change time, and the total machine tool operating time.

4. The machine tool effective working time measurement method as described in claim 3, characterized in that, The step of optimizing the effective operating time based on the current operating loss data to obtain the optimized effective operating time of the target machine tool includes: Obtain the personnel login time and machine tool startup time when the target machine tool is started; The current time reference deviation is determined based on the personnel login time and the machine tool startup time. The optimized effective working hours of the target machine tool are determined based on the current time base deviation, the equipment performance loss data, and the effective operating hours.

5. A machine tool effective working time measuring device, characterized in that, The machine tool effective working time measuring device includes: The data acquisition module is used to collect multi-dimensional operational data of the target machine tool during operation after detecting an authorized personnel identifier; the multi-dimensional operational data includes machining status data, motion status data, tool management data, or alarm log data. The scene detection module is used to acquire the current interactive operation scene triggered by the multi-dimensional running data; the current interactive operation scene includes: program debugging scene, batch processing scene or technical interruption scene; The time measurement module is used to determine the effective operating time of the target machine tool based on the current interactive operation scenario and preset time measurement rules; the effective operating time includes preparation time, processing time and early warning maintenance time; The scene detection module is further configured to determine the current interactive operation scenario as the program debugging scenario when the target machine tool is in manual data input mode and the frequency of point coordinate modification exceeds a set threshold; or to determine the current interactive operation scenario as the batch processing scenario when the target machine tool is in automatic operation mode and the continuous workpiece counting is not abnormally interrupted; or to determine the current interactive operation scenario as the technical interruption scenario when the remaining tool life of the target machine tool is lower than the warning value and a forced tool change event is triggered. The time measurement module is further configured to determine the preparation time based on the manual running time and real-time coordinate switching frequency when the current interactive operation scenario is the program debugging scenario; determine the processing time based on the real-time workpiece count and feed speed threshold when the current interactive operation scenario is the batch processing scenario; and determine the tool change interruption time as the early warning maintenance time when the current interactive operation scenario is the technical interruption scenario.

6. A machine tool effective working time measuring device, characterized in that, The device includes: a memory, a processor, and a machine tool effective working time measurement program stored in the memory and executable on the processor, the machine tool effective working time measurement program being configured to implement the steps of the machine tool effective working time measurement method as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium stores a machine tool effective working time measurement program, which, when executed by a processor, implements the steps of the machine tool effective working time measurement method as described in any one of claims 1 to 4.

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