A safe production data detection method and system

Abstract

The application provides a safe production data detection method and system, which is applied to the technical field of lock processing, can more accurately and comprehensively identify and attribute potential abnormalities in the production process by introducing comparative analysis of multi-dimensional current characteristics and combining standardized processing and micro-cutting strategies, effectively solves the problems of inaccurate diagnosis and easy misjudgment in the prior art, thereby significantly improves the safe production level and efficiency in the field of precision manufacturing such as lock processing, avoids unnecessary production interruption and resource waste, and enhances the credibility of the automatic diagnosis system.

Description

Technical Field

[0001] This application relates to the field of lock manufacturing technology, and in particular to a method and system for detecting safety production data. Background Technology

[0002] In modern industrial production, especially in precision machining fields like lock manufacturing, the operational status of automated machine tools is crucial to product quality and production efficiency. These machine tools typically assess their load by monitoring the real-time current data of their spindle motors, using this as a basis for determining whether the equipment is operating normally. However, existing motor load detection methods often rely excessively on instantaneous current amplitude changes or simple long-term trend analysis, which proves inadequate when faced with complex and subtle anomalies.

[0003] For example, during lock manufacturing, when the physical properties of the raw materials deviate slightly but continuously, or when the cutting tool experiences normal wear, the motor current will show an increase in amplitude. However, the "time evolution characteristics" or "signal texture" of these current increases caused by different reasons may differ significantly within the processing cycle. If the system cannot accurately distinguish these subtle signal patterns, it may lead to incorrect diagnosis, such as misjudging raw material abnormalities as cutting tool wear, resulting in unnecessary production interruptions, resource waste, and even damage to the reliability of the automated diagnostic system. Existing technologies fail to capture and utilize these differences for accurate fault attribution, resulting in an inability to effectively distinguish motor current changes caused by different reasons, thereby affecting the stability and efficiency of the production process.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] In view of the shortcomings of the prior art, this application provides a safety production data detection method and system, which is applied to the field of lock processing technology. It aims to solve the problem that the prior art cannot accurately distinguish the changes in motor current caused by different reasons in motor load detection, resulting in inaccurate diagnosis and affecting production stability and efficiency.

[0006] Firstly, a method for detecting safety production data, the method comprising the following steps:

[0007] S1: Obtain preset cutting parameters, standardize the processing of raw materials for lock manufacturing, and collect standard current data of the motor during cutting;

[0008] S2: Using the preset cutting parameters, perform micro-cutting on the raw material of the lock to be processed, and collect the real-time current data of the motor during cutting;

[0009] S3: Compare the standard current data with the real-time current data to obtain the first relative deviation value of the current rise time, the second relative deviation value of the average current value during the cutting stabilization period, and the third relative deviation value of the standard deviation of the current during the cutting stabilization period.

[0010] S4: Determine whether the first relative deviation value, the second relative deviation value, and the third relative deviation value are within their respective preset allowable deviation ranges, obtain the determination results, and issue corresponding safety alarms based on the determination results.

[0011] This application provides a safety production data detection method that, by collecting standard current data and real-time current data and comparing the relative deviations of multi-dimensional current characteristics (including current rise time, average current during the cutting stabilization period, and standard deviation of current during the cutting stabilization period), can more comprehensively and meticulously reflect abnormal situations in the cutting process. This effectively distinguishes motor current changes caused by different reasons (such as abnormal raw materials or abnormal cutting tools), solves the problem of inaccurate diagnosis in existing technologies, and improves the early warning capability for safety production.

[0012] Further, in step S4, the preset allowable deviation range includes a first preset allowable deviation range, a second preset allowable deviation range, and a third preset allowable deviation range. Step S4 includes:

[0013] S41: When the first relative deviation value, the second relative deviation value, and the third relative deviation value are within their respective preset allowable deviation ranges, a first safety alarm is issued to remind the user that the current processing conditions for the lock are qualified.

[0014] S42: When the first relative deviation value exceeds the first preset allowable deviation range and / or the third relative deviation value exceeds the third preset allowable deviation range, a second safety alarm is issued to remind the user that the cutting tool of the lock to be processed is installed abnormally.

[0015] S43: When the second relative deviation value exceeds the second preset allowable deviation range, a third safety alarm is issued to remind the user that the hardness of the raw material of the lock to be processed is abnormal.

[0016] This application provides a safety production data detection method that can issue targeted safety alarms based on deviations in different current characteristics. For example, it can distinguish between abnormal cutting tool installation and abnormal raw material hardness, achieving accurate attribution of abnormal causes, avoiding misjudgments and unnecessary downtime, and further improving the accuracy of diagnosis and production efficiency.

[0017] Furthermore, step S1 includes:

[0018] S11: Obtain raw materials from different batches for lock processing and set multiple sets of cutting parameters;

[0019] S12: Using multiple sets of cutting parameters, different batches of raw materials are processed one by one, and multiple response current data of the motor are collected during cutting;

[0020] S13: Analyze each of the aforementioned response current data to extract the first type of cutting response features and the second type of cutting response features;

[0021] S14: Based on the first type of cutting response characteristics and the second type of cutting response characteristics, determine the preset cutting parameters, use the preset cutting parameters to standardize the processing of raw materials for lock manufacturing, and collect standard current data of the motor during cutting.

[0022] This application provides a method for detecting safety production data. By conducting multi-parameter cutting tests on different batches of raw materials and extracting multiple types of cutting response characteristics, the optimal preset cutting parameters and standard current data are determined, ensuring the representativeness and accuracy of the standard data and providing a reliable benchmark for subsequent real-time data comparison, thereby improving the robustness of the detection.

[0023] Furthermore, step S13 includes:

[0024] S131: Perform low-pass filtering on each of the response current data, and calculate the current rise time, the average current during the cutting steady-state period, and the standard deviation of the current during the cutting steady-state period.

[0025] The current rise time, the average current during the cutting steady-state period, and the standard deviation of the current during the cutting steady-state period are the first type of cutting response characteristics;

[0026] S132: Calculate the instantaneous rate of change of each of the response current data. When the absolute value of the instantaneous rate of change exceeds a preset absolute value and falls back within a preset time, it is determined that a current pulse event has occurred, and the number of times the current pulse event has occurred is recorded.

[0027] S133: Perform a short-time Fourier transform on each of the current response data, analyze its energy distribution in a preset frequency band, and obtain the number of high-frequency fluctuation events based on the energy distribution;

[0028] The number of current pulse events and the number of high-frequency fluctuation events are the second type of cutting response characteristics.

[0029] This application provides a safety production data detection method that extracts multi-dimensional cutting response features in the time domain (current rise time, average current during the cutting stabilization period, and standard deviation of current during the cutting stabilization period), event domain (number of current pulse events), and frequency domain (number of high-frequency fluctuations). This method can capture subtle changes in the cutting process more comprehensively and meticulously, providing richer and more accurate data support for subsequent parameter optimization and anomaly judgment.

[0030] Furthermore, step S14 includes:

[0031] S141: Based on the first type of cutting response characteristics, select one or more sets of candidate cutting parameters from the multiple sets of cutting parameters, so that when cutting different batches of raw materials, the current rise time of the motor, the average current during the cutting stabilization period, and the standard deviation of the current during the cutting stabilization period present different values.

[0032] S142: Based on the second type of cutting response characteristics, select a set of candidate cutting parameters from one or more sets of candidate cutting parameters that maximizes the sum of the number of current pulse events and the number of high-frequency fluctuation events of the motor when cutting different batches of raw materials, and use it as the preset cutting parameters.

[0033] S143: The raw materials for lock processing are processed in a standardized manner using the preset cutting parameters, and the standard current data of the motor is collected during cutting.

[0034] Furthermore, step S2 includes:

[0035] S21: Obtain the geometry of the raw material for the lock to be processed;

[0036] S22: Based on the geometry, identify the non-critical areas of the raw material for the lock to be processed;

[0037] S23: Using the preset cutting parameters, perform micro-cutting on the raw material of the lock to be processed in the non-critical area, and collect the real-time current data of the motor during cutting.

[0038] Furthermore, step S22 includes:

[0039] S221: Obtain the design geometry information of the finished lock;

[0040] S222: Compare the geometric shape information of the raw material with the design geometric shape information of the finished product to determine the material area on the raw material that will be removed by the main processing program;

[0041] S223: Within the area of ​​material to be removed, based on a preset cutting allowance and a preset minimum distance required to be maintained between the material and the functional area of ​​the finished lock, identify non-critical areas that meet the preset cutting allowance and distance conditions.

[0042] Furthermore, step S223 includes:

[0043] S2231: Obtain the geometric data of the material region to be removed, and determine discrete points within the region based on the geometric data;

[0044] S2232: Calculate the first shortest distance from each of the discrete points to the design surface of the finished lock, and the second shortest distance from each of the discrete points to each functional area of ​​the finished lock;

[0045] S2233: Filter out the first discrete point corresponding to the first shortest distance being greater than or equal to the preset cutting allowance, and filter out the second discrete point corresponding to the second shortest distance being greater than or equal to the preset minimum distance that needs to be maintained between the functional area of ​​the finished lock.

[0046] S2234: The set of the first discrete point and the second discrete point is taken as the non-critical region.

[0047] Furthermore, step S3 includes:

[0048] S31: Synchronize the real-time current data in time;

[0049] S32: Perform noise suppression on the real-time current data after time synchronization;

[0050] S33: Compare the real-time current data after time synchronization and noise suppression with the standard current data to obtain the first relative deviation value of the current rise time, the second relative deviation value of the average current value during the cutting stabilization period, and the third relative deviation value of the standard deviation of the current during the cutting stabilization period.

[0051] Secondly, a safety production data detection system is provided for implementing any of the methods described above, the system comprising:

[0052] First acquisition module: acquires preset cutting parameters, performs standardized processing on raw materials for lock manufacturing, and acquires standard current data of the motor during cutting;

[0053] The second acquisition module uses the preset cutting parameters to perform micro-cutting on the raw material of the lock to be processed, and acquires the real-time current data of the motor during cutting.

[0054] Comparison module: Compares the standard current data with the real-time current data to obtain the first relative deviation value of the current rise time, the second relative deviation value of the average current value during the cutting steady-state period, and the third relative deviation value of the standard deviation of the current during the cutting steady-state period.

[0055] Safety alarm module: It determines whether the first relative deviation value, the second relative deviation value, and the third relative deviation value are within their respective preset allowable deviation ranges, obtains the judgment results, and issues corresponding safety alarms based on the judgment results.

[0056] Beneficial effects: The safety production data detection method and system proposed in this application, by introducing comparative analysis of multi-dimensional current characteristics and combining standardized processing and micro-cutting strategies, can more accurately and comprehensively identify and attribute potential anomalies in the production process. It effectively solves the problems of inaccurate diagnosis and easy misjudgment in the prior art, thereby significantly improving the safety production level and efficiency in precision manufacturing fields such as lock processing, avoiding unnecessary production interruptions and resource waste, and enhancing the reliability of automated diagnostic systems. Attached Figure Description

[0057] Figure 1 This is a flowchart of a safety production data detection method proposed in this application.

[0058] Figure 2 This is a structural diagram of a safety production data detection system proposed in this application.

[0059] Figure 3 This is an architecture diagram of a safety production data detection system proposed in this application.

[0060] Labeling explanation: 201, First acquisition module; 202, Second acquisition module; 203, Comparison module; 204, Security alarm module. Detailed Implementation

[0061] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and marked in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0062] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0063] Please refer to Figure 1 A method for detecting safety production data, the method includes the following steps:

[0064] S1: Obtain preset cutting parameters, standardize the processing of raw materials for lock manufacturing, and collect standard current data of the motor during cutting;

[0065] S2: Using preset cutting parameters, perform micro-cutting on the raw material of the lock to be processed, and collect the real-time current data of the motor during cutting;

[0066] S3: Compare the standard current data with the real-time current data to obtain the first relative deviation value of the current rise time, the second relative deviation value of the average current value during the cutting steady period, and the third relative deviation value of the standard deviation of the current during the cutting steady period.

[0067] S4: Determine whether the first relative deviation value, the second relative deviation value, and the third relative deviation value are within their respective preset allowable deviation ranges, obtain the judgment results, and issue corresponding safety alarms based on the judgment results.

[0068] This application provides a method for detecting safety production data, primarily applied in precision machining fields, such as lock manufacturing. The core of this method lies in the refined analysis of the current data of the spindle motor of an automated machine tool during the cutting process, in order to determine abnormalities in processing conditions and raw materials.

[0069] Specifically, cutting parameters refer to a series of set values ​​used to control the cutting process during machine tool processing, including but not limited to spindle speed, feed rate, depth of cut, and tool type. These parameters directly affect cutting force, cutting temperature, and motor load. As one implementation method, preset cutting parameters can be manually set by experienced technicians based on historical production data and material characteristics. For example, the spindle speed can be set to 2000 rpm, the feed rate to 100 mm / min, and the depth of cut to 0.5 mm. Subsequently, a batch of raw materials with known quality is selected, placed on an automated machine tool, and machined using the manually set cutting parameters. During processing, a current sensor connected to the spindle motor monitors and records the motor's current data in real time. This data is then processed to form standard current data.

[0070] Standardized machining refers to machining in a controlled environment using known qualified raw materials and equipment, according to preset cutting parameters, in order to obtain motor current data as a reference.

[0071] Standard current data refers to motor current data collected during standardized manufacturing processes, which represents the current response under ideal or normal conditions.

[0072] Micro-cutting refers to small-scale, shallow cutting operations on the raw material to be processed, aiming to simulate the motor load characteristics during actual machining while avoiding impact on the final product. As one implementation method, on an actual production line, when a new batch of raw materials for locks enters the processing area, the operator can manually select a non-critical area on the raw material that does not affect the function of the final product, such as an edge or a reserved scrap portion. Then, the machine tool is instructed to perform a small-scale cutting operation in the selected area using the preset cutting parameters determined in step S1. During micro-cutting, a current sensor connected to the spindle motor collects the motor's current data in real time, and this data is transmitted to the data processing unit to form real-time current data.

[0073] Real-time current data is the motor current data collected synchronously when the raw material of the lock to be processed is being micro-cut.

[0074] In data analysis, the current rise time refers to the time required for the motor current to rapidly increase from an unloaded state to a cutting load state, reflecting the instantaneous impact characteristics of the tool entering the material. The average current during the cutting steady-state period refers to the average value of the motor current after the cutting process enters a stable phase, reflecting the magnitude of the continuous cutting load. The standard deviation of the current during the cutting steady-state period refers to the dispersion of motor current fluctuations within the cutting steady-state period, reflecting the smoothness of the cutting process or the presence of periodic disturbances.

[0075] The relative deviation value refers to the degree of difference between the real-time acquired current characteristic values ​​and the corresponding characteristic values ​​in the standard current data. It is usually expressed as a percentage and is used to quantify the deviation of the current machining state from the standard state. As one implementation method, after acquiring the standard current data and real-time current data, the current rise point, the start point of the cutting stabilization period, and the end point of the two sets of data can be identified manually or through a simple script. Then, the current rise time, the average current during the cutting stabilization period, and the standard deviation of the current during the cutting stabilization period for both the standard current data and the real-time current data are calculated manually or programmatically. Subsequently, a simple mathematical formula, such as (real-time value - standard value) / standard value, is used to calculate the current rise time, average current during the cutting stabilization period, and standard deviation of the current during the cutting stabilization period for both the standard current data and the real-time current data. 100%, calculate the first relative deviation value, the second relative deviation value and the third relative deviation value respectively.

[0076] The preset allowable deviation range is the acceptable fluctuation range set for each relative deviation value. If it exceeds this range, it is considered to be abnormal.

[0077] A safety alarm is a notification or signal issued by the system when an anomaly is detected, used to remind operators or automatic control systems to take appropriate measures.

[0078] As one implementation method, the allowable range for each relative deviation value can be preset. For example, the first relative deviation value is allowed within ±5%, the second relative deviation value is allowed within ±3%, and the third relative deviation value is allowed within ±8%. After obtaining the calculated first, second, and third relative deviation values, the system will compare them one by one with these preset ranges. If any deviation value exceeds its corresponding allowable range, the system will trigger a general safety alarm, such as illuminating a red indicator light or emitting a buzzer, to alert the operator that an abnormality has occurred.

[0079] Compared to existing technologies that rely excessively on instantaneous current amplitude changes or simple long-term trend analysis, this application introduces three independent current characteristics—current rise time, average current during the cutting stabilization period, and standard deviation of current during the cutting stabilization period—and calculates their relative deviations from the benchmark values ​​obtained from standardized machining. This effectively distinguishes motor current changes caused by different reasons, avoids the misdiagnosis problem common in traditional methods, and improves the accuracy and reliability of diagnosis.

[0080] Furthermore, in step S4, the preset allowable deviation range includes a first preset allowable deviation range, a second preset allowable deviation range, and a third preset allowable deviation range. Step S4 includes:

[0081] S41: When the first relative deviation value, the second relative deviation value, and the third relative deviation value are within their respective preset allowable deviation ranges, a first safety alarm is issued to remind the user that the current processing conditions of the lock are qualified.

[0082] S42: When the first relative deviation value exceeds the first preset allowable deviation range and / or the third relative deviation value exceeds the third preset allowable deviation range, a second safety alarm is issued to remind the user that the cutting tool of the lock to be processed is installed abnormally.

[0083] S43: When the second relative deviation value exceeds the second preset allowable deviation range, a third safety alarm is issued to remind the user that the hardness of the raw material of the lock to be processed is abnormal.

[0084] This application's solution achieves refined diagnosis of potential production anomalies by linking safety alarms to specific current deviation types and their corresponding preset allowable deviation ranges. Specifically, the first relative deviation value of the current rise time mainly reflects the smoothness of the initial contact between the tool and the workpiece and the cutting process; its abnormality is often related to the tool's installation state or initial cutting conditions. The second relative deviation value of the average current value during the cutting stabilization period directly reflects the average energy required during the cutting process, which is closely related to the physical properties of the raw material, such as hardness and toughness. The third relative deviation value of the standard deviation of the current during the cutting stabilization period characterizes the stability of the cutting process; increased fluctuations usually indicate tool vibration, wear, or unstable installation. By independently judging these deviations with different characteristics and issuing specific alarms, it is possible to effectively distinguish between tool installation problems and raw material characteristic problems, thereby avoiding the diagnostic ambiguity caused by general alarms.

[0085] The above technical solution enables more precise and targeted monitoring of safety conditions during lock manufacturing. When an anomaly occurs, the system can automatically identify and issue a safety alarm indicating a specific problem type based on specific current characteristic deviations, such as "abnormal cutting tool installation" or "abnormal raw material hardness." This significantly improves the efficiency and accuracy of fault diagnosis, allowing operators to quickly locate the root cause of the problem and take targeted corrective measures, thereby reducing downtime, lowering the scrap rate, and ultimately improving overall production efficiency and safety.

[0086] Furthermore, step S1 includes:

[0087] S11: Obtain raw materials from different batches for lock processing and set multiple sets of cutting parameters;

[0088] S12: Use multiple sets of cutting parameters to process different batches of raw materials one by one, and collect multiple response current data of the motor during cutting;

[0089] S13: Analyze each response current data and extract the first type of cutting response features and the second type of cutting response features;

[0090] S14: Based on the first type of cutting response characteristics and the second type of cutting response characteristics, determine the preset cutting parameters, use the preset cutting parameters to standardize the processing of the raw materials for lock processing, and collect the standard current data of the motor during cutting.

[0091] This application's solution effectively addresses the issue of potentially unrepresentative standard current data in traditional methods by introducing a systematic process for determining preset cutting parameters. Specifically, step S11 involves acquiring different batches of raw materials and setting multiple sets of cutting parameters, ensuring a comprehensive consideration of raw material diversity and parameter impact. Subsequently, in step S12, these multiple sets of cutting parameters are used to experimentally process different batches of raw materials and collect response current data, providing a rich data foundation for subsequent parameter optimization. In step S13, by analyzing the response current data and extracting first and second type cutting response features, the characteristics of the cutting process can be quantified from multiple dimensions, such as cutting stability, efficiency, and potential abnormal fluctuations. Finally, in step S14, the optimal preset cutting parameters are determined based on these extracted features, and standardized processing is performed using these parameters to collect standard current data. This method ensures that the determined preset cutting parameters better adapt to the characteristics of different batches of raw materials and that the collected standard current data more accurately reflects the ideal and stable cutting state, thus providing a more reliable and accurate benchmark for subsequent real-time data comparison.

[0092] In some preferred embodiments, a specific example is given below. Suppose a lock manufacturing company needs to conduct safety production data testing on several newly purchased batches of raw materials.

[0093] First, in step S11, the company acquires three batches of raw materials from different suppliers or with different production dates (e.g., batch A, batch B, and batch C). Simultaneously, multiple sets of cutting parameters are set, such as parameter group P1 (spindle speed X1, feed rate Y1, depth of cut Z1), parameter group P2 (spindle speed X2, feed rate Y2, depth of cut Z2), and parameter group P3 (spindle speed X3, feed rate Y3, depth of cut Z3).

[0094] Next, in step S12, the company will use parameter group P1 to perform micro-cutting on the raw materials of batches A, B, and C respectively, and collect the corresponding motor response current data; then, it will use parameter group P2 to perform micro-cutting on the raw materials of batches A, B, and C respectively, and collect the corresponding motor response current data; and so on, until all parameter groups have performed experimental processing on all batches of raw materials and collected response current data.

[0095] Subsequently, in step S13, each acquired response current data point is analyzed. For example, for each current data curve, its current rise time, average current during the cutting steady-state period, and standard deviation of the current during the cutting steady-state period can be extracted as first-type cutting response features. Simultaneously, by analyzing the instantaneous rate of change and frequency domain energy distribution of the current data, the number of current pulse events and the number of high-frequency fluctuation events can be extracted as second-type cutting response features.

[0096] Finally, in step S14, preset cutting parameters are determined based on these extracted features. For example, parameter groups that produce stable and distinctive first-type features across different batches of raw materials can be preferentially selected as candidates. Then, from these candidate parameter groups, those that more clearly reflect material or tool anomalies (e.g., generating more current pulses or high-frequency fluctuations) during the cutting process are further selected as the final preset cutting parameters. Once the optimal preset cutting parameters are determined, the raw materials for lock manufacturing are standardized using these parameters, and standard current data of the motor is collected at this time. This data will serve as the benchmark for subsequent real-time detection. In this way, it is ensured that the standard current data is obtained under parameters that best reflect the cutting characteristics and have good discriminative power, thereby improving the accuracy of detection.

[0097] Furthermore, step S13 includes:

[0098] S131: Perform low-pass filtering on each response current data and calculate the current rise time, the average current during the cutting steady-state period, and the standard deviation of the current during the cutting steady-state period.

[0099] The current rise time, the average current during the cutting steady-state period, and the standard deviation of the current during the cutting steady-state period are the first type of cutting response characteristics;

[0100] S132: Calculate the instantaneous rate of change of each response current data. When the absolute value of the instantaneous rate of change exceeds the preset absolute value and falls back within the preset time, it is determined that a current pulse event has occurred, and the number of current pulse events is recorded.

[0101] S133: Perform a short-time Fourier transform on each current response data, analyze its energy distribution in the preset frequency band, and obtain the number of high-frequency fluctuation events based on the energy distribution;

[0102] The number of current pulse events and the number of high-frequency fluctuation events are the second type of cutting response characteristics.

[0103] Specifically, in step S131, each response current data is low-pass filtered to remove high-frequency noise interference and ensure the accuracy of subsequent feature extraction. Based on this, the current rise time, the average current during the cutting steady-state period, and the standard deviation of the current during the cutting steady-state period are calculated. The current rise time refers to the time required from the start of cutting to the current reaching a steady state, reflecting the start-up characteristics of the cutting process; the average current during the cutting steady-state period refers to the average value during the current stabilization phase of the cutting process, reflecting the overall level of the cutting load; and the standard deviation of the current during the cutting steady-state period reflects the stability of current fluctuations during the cutting process. These three indicators are defined as the first type of cutting response characteristics, which mainly characterize the macroscopic and steady-state characteristics of the cutting process.

[0104] Furthermore, in step S132, rapid fluctuations in current can be captured by calculating the instantaneous rate of change of each response current data. When the absolute value of the instantaneous rate of change exceeds a preset absolute value and falls back within a preset time, this typically indicates the occurrence of a current pulse event, such as an impact caused by instantaneous contact or separation between the tool and the material, or internal defects in the material. Recording the number of such current pulse events quantifies the frequency of transient impacts or abnormal events during the cutting process.

[0105] The preset absolute value refers to a numerical threshold set by technicians. This threshold is used to determine whether the instantaneous rate of change of the motor's response current data has reached a level sufficient to be classified as a "current pulse event." The preset time is a pre-defined time window that defines the maximum allowable time required for the instantaneous rate of change of the current to recover from an abnormally high (or low) value to the normal range. If the absolute value of the instantaneous rate of change fails to fall back within the preset time after exceeding the preset absolute value, it indicates that this is not a transient pulse event, but rather a potentially continuous change.

[0106] Furthermore, in step S133, a short-time Fourier transform is performed on each current response data to analyze its frequency components in different time periods. The short-time Fourier transform is a commonly used method in the prior art for converting current data into time-frequency information. The main purpose of this application using this method is to identify high-frequency fluctuations in the current response data. Specifically, this can be achieved by analyzing its energy distribution within a preset frequency band and capturing high-frequency fluctuations exceeding a preset energy threshold within that energy distribution.

[0107] In the context of machining, fluctuations at different frequencies in motor current signals may correspond to different physical phenomena. For example, certain high-frequency fluctuations may be associated with abnormalities such as tool wear, machine tool vibration, or subtle inhomogeneities within the raw material. Therefore, to effectively identify these specific anomalies, technicians pre-define one or more frequency ranges of interest, i.e., preset frequency bands, based on experience, theoretical analysis, or previous experimental data. By analyzing the energy distribution of current response data within these preset frequency bands, the system can identify the presence of abnormal high-frequency fluctuations. For example, if the energy in a certain preset frequency band increases significantly, exceeding a preset energy threshold, this may indicate abnormal vibration during the cutting process. The degree of the anomaly can then be quantified by the frequency of such high-frequency fluctuations. This method allows the system to selectively extract high-frequency features related to specific fault modes from complex current signals, thereby improving the sensitivity and accuracy of anomaly detection. The preset energy threshold is a pre-set maximum value of energy fluctuations under normal conditions.

[0108] As one implementation, high-frequency vibrations are assumed to be caused by tool wear, vibration, or material inhomogeneity. The number of high-frequency fluctuation events, obtained from the energy distribution, can reflect the microscopic instabilities or abnormal vibrations during the cutting process. The number of current pulse events and the number of high-frequency fluctuation events are defined as second-type cutting response characteristics, which primarily characterize transient, microscopic, or abnormal properties during the cutting process.

[0109] Furthermore, step S14 includes:

[0110] S141: Based on the first type of cutting response characteristics, select one or more sets of candidate cutting parameters from multiple sets of cutting parameters so that when cutting different batches of raw materials, the current rise time of the motor, the average current during the cutting steady period, and the standard deviation of the current during the cutting steady period present different values.

[0111] S142: Based on the second type of cutting response characteristics, select a set of candidate cutting parameters from one or more sets of candidate cutting parameters that maximizes the sum of the number of current pulse events and the number of high-frequency fluctuation events of the motor when cutting different batches of raw materials, and use it as the preset cutting parameters.

[0112] S143: Standardize the processing of raw materials for lock manufacturing using preset cutting parameters, and collect standard current data of the motor during cutting.

[0113] Among them, the first type of cutting response characteristics, namely the current rise time, the average current during the cutting steady-state period, and the standard deviation of the current during the cutting steady-state period, are usually closely related to the physical properties of the raw materials (such as hardness and toughness) and the stability of the cutting process. By selecting cutting parameters that can make these characteristics present obvious numerical differences between different batches of raw materials, it can be ensured that the selected parameters are highly sensitive to the inherent differences of the raw materials, and can better "amplify" the subtle differences between different batches of raw materials, thereby providing more representative basic data for subsequent standardized processing and deviation detection.

[0114] Meanwhile, the second type of cutting response characteristics, namely the number of current pulse events and the number of high-frequency fluctuation events, are usually related to defects inside the raw material (such as inclusions and cracks) or abnormal phenomena during the cutting process (such as tool wear and chipping). Selecting a set of candidate cutting parameters that maximizes the sum of the occurrences of these abnormal events can expose potential defects in the raw material or abnormalities that may occur during the machining process to the greatest extent possible during standardized machining. This ensures that the collected standard current data not only reflects normal conditions but also implicitly contains sensitivity to abnormal conditions.

[0115] In some preferred embodiments, a specific example is given below. Suppose there are three batches of lock raw materials from different sources (batch A, batch B, and batch C), and five different sets of cutting parameters (parameter groups P1, P2, P3, P4, and P5) are preset.

[0116] First, in step S141, parameter groups P1 to P5 are applied one by one to the raw materials of batches A, B, and C for cutting, and the motor response current data is collected. Then, these response current data are analyzed to extract the first type of cutting response characteristics, namely, the current rise time, the average current during the cutting steady-state period, and the standard deviation of the current during the cutting steady-state period. For example, if parameter groups P2 and P4 show the most significant differences in the values ​​of current rise time, average current during the cutting steady-state period, and standard deviation of the current during the cutting steady-state period when cutting batches A, B, and C, and can clearly distinguish these three batches of raw materials, then P2 and P4 will be selected as candidate cutting parameters.

[0117] Next, in step S142, candidate cutting parameters P2 and P4 will be further evaluated. P2 and P4 are used again to cut raw materials from batches A, B, and C, and the second type of cutting response characteristics, namely the number of current pulse events and the number of high-frequency fluctuation times, are extracted. Assume that under parameter group P2, the sum of the number of current pulse events and the number of high-frequency fluctuation times generated when cutting different batches of raw materials is 100, while under parameter group P4, this sum is 120. According to the principle of the proposed scheme, the parameter group that maximizes the sum of these abnormal events is selected; therefore, parameter group P4 will be ultimately determined as the preset cutting parameters.

[0118] Finally, in step S143, the raw material for lock manufacturing is standardized using the selected preset cutting parameters P4, and standard current data of the motor is collected during cutting. This standard current data will serve as a benchmark for subsequent real-time detection. Since it is obtained under conditions that maximize the exposure of differences in raw material characteristics and potential anomalies, it ensures that subsequent deviation judgments are more accurate and reliable.

[0119] Furthermore, step S2 includes:

[0120] S21: Obtain the geometry of the raw material for the lock to be processed;

[0121] S22: Identify non-critical areas of the raw materials for locks to be processed based on geometry;

[0122] S23: Using preset cutting parameters, perform micro-cutting on the raw material of the lock to be processed in non-critical areas, and collect real-time current data of the motor during cutting.

[0123] Specifically, in step S21, obtaining the geometry of the raw material to be processed can be understood as obtaining the initial three-dimensional model data of the raw material. This can be achieved in various ways, such as importing the design drawings or three-dimensional model files of the raw material through computer-aided design (CAD) software, or scanning the actual raw material with a three-dimensional scanning device to generate its point cloud data, thereby obtaining the geometry of the raw material to be processed.

[0124] In step S22, identifying non-critical areas of the raw material for the lock to be processed, based on the geometry, refers to defining one or more areas on the raw material that will be completely removed during subsequent main machining processes, or whose final function and appearance will not be affected even if micro-machining is performed. For example, these areas may be machining allowances reserved on the raw material, the location of risers and gatings formed during casting or forging, or auxiliary structures that do not play any functional role in the final product. The purpose of identifying non-critical areas is to ensure that micro-machining operations will not have any negative impact on the final performance of the lock.

[0125] In step S23, the preset cutting parameters are used to perform micro-cutting on the raw material of the lock to be processed in the non-critical area, and real-time current data of the motor is collected during cutting. This means that the micro-cutting operation is precisely limited to the identified non-critical area. The preset cutting parameters are consistent with the parameters used for standardized processing in step S1 to ensure the validity of data comparison. During the micro-cutting process, the motor current data is monitored and collected in real time by sensors, and this data will serve as an important basis for evaluating the condition of the raw material.

[0126] Furthermore, step S22 includes:

[0127] S221: Obtain the design geometry information of the finished lock;

[0128] S222: Compare the geometric information of the raw material with the design geometric information of the finished product to determine the material area on the raw material that will be removed by the main processing program;

[0129] S223: Within the area of ​​material to be removed, based on the preset cutting allowance and the preset minimum distance required to maintain between the functional area of ​​the finished lock, identify non-critical areas that meet the preset cutting allowance and distance conditions.

[0130] Specifically, obtaining the design geometry information of the finished lock S221 refers to obtaining the three-dimensional model data, CAD drawings or detailed dimensions of the final lock product determined in the design stage. Its purpose is to provide an accurate reference benchmark for subsequent material area identification.

[0131] Step S222 can be understood as using geometric modeling software or data processing algorithms to superimpose or perform difference operations on the initial shape of the raw material of the lock to be processed and the final shape of the finished lock, thereby accurately identifying the material parts that need to be cut and removed during the conventional main processing.

[0132] Step S223 refers to further defining a precise range suitable for micro-machining within the already identified area of ​​material to be removed. The preset cutting allowance refers to the extra material thickness reserved before the main machining process is completed to ensure the dimensional accuracy and surface quality of the final product. Micro-machining operations must be performed outside this allowance to avoid affecting the accuracy of the main machining. The preset minimum distance required between the micro-machining operation and the functional areas of the finished lock is a safety distance set to protect the internal structure, critical mating surfaces, or functional components (such as lock cylinder holes, pin holes, keyways, etc.) from accidental damage during micro-machining operations. Only areas that simultaneously meet both conditions are ultimately identified as non-critical areas suitable for micro-machining.

[0133] The above technical solutions enable precise control of the micro-cutting area, significantly improving the reliability and safety of safety production data detection methods.

[0134] Furthermore, step S223 includes:

[0135] S2231: Obtain the geometric data of the material region to be removed, and determine the discrete points within the region based on the geometric data;

[0136] S2232: Calculate the first shortest distance from each discrete point to the finished design surface of the lock, and the second shortest distance from each discrete point to each functional area of ​​the finished lock.

[0137] S2233: Filter out the first discrete point corresponding to the first shortest distance being greater than or equal to the preset cutting allowance, and filter out the second discrete point corresponding to the second shortest distance being greater than or equal to the preset minimum distance that needs to be maintained between the functional area of ​​the finished lock.

[0138] S2234: The set of the first discrete point and the second discrete point is taken as the non-critical region.

[0139] In step S2231, the discrete points refer to a series of representative points distributed within or on the surface of the material region. Specifically, these points are obtained by dividing the material region into a regular grid and selecting the intersections of the grids or the center point of each grid as discrete points. This method ensures uniform distribution and comprehensive coverage of the points.

[0140] By determining these discrete points, precise coordinates can be provided for subsequent calculations of the shortest distance from each point to the finished lock design surface and functional areas. This lays the foundation for determining which areas meet the preset cutting allowance and minimum distance conditions, and thus identifying the truly non-critical areas.

[0141] Calculating the first shortest distance refers to calculating the Euclidean distance from each discrete point to the nearest point on the finished lock surface. Meanwhile, the functional areas of the finished lock refer to the parts of the lock that have specific functions, such as the lock cylinder hole, keyway, screw holes, or mounting surfaces. These areas typically have high requirements for dimensional accuracy and surface quality. Therefore, calculating the second shortest distance refers to calculating the Euclidean distance from each discrete point to the nearest point in all functional areas of the finished lock.

[0142] The preset cutting allowance refers to the minimum material thickness reserved during micro-cutting operations to avoid damage to the surface of the finished lock design. A point is considered safe only when the first shortest distance from the discrete point to the finished design surface is greater than or equal to this preset cutting allowance; these points are selected as the first discrete points.

[0143] Meanwhile, the preset minimum distance between the lock's finished functional areas and the target functional areas refers to the minimum safe distance that must be maintained during micro-machining to protect the integrity and functionality of these areas. A point is considered safe only when its second shortest distance to any functional area is greater than or equal to this preset minimum distance; these points are then selected as second discrete points. The purpose of this step is to initially eliminate points that are too close to the finished surface or functional areas by setting a safety threshold, thereby ensuring the safety of the micro-machining operation.

[0144] By selecting the intersection of the first and second discrete points, the non-critical region can be obtained, which is the region formed by discrete points that simultaneously satisfy both conditions.

[0145] Furthermore, step S3 includes:

[0146] S31: Time synchronization of real-time current data;

[0147] S32: Perform noise suppression on the real-time current data after time synchronization;

[0148] S33: Compare the real-time current data after time synchronization and noise suppression with the standard current data to obtain the first relative deviation value of the current rise time, the second relative deviation value of the average current value during the cutting stabilization period, and the third relative deviation value of the standard deviation of the current during the cutting stabilization period.

[0149] In step S31, time synchronization of real-time current data refers to aligning the timestamp of the collected real-time current data with the timestamp of the standard current data to ensure that subsequent data comparisons are performed at the same time point or within the same time period, thus avoiding comparison errors caused by time offset.

[0150] In step S32, noise suppression is performed on the time-synchronized real-time current data. This can be understood as using existing signal processing techniques to eliminate or reduce random noise, power frequency interference, or other ineffective signals present in the real-time current data. For example, noise can be filtered out using a digital filter.

[0151] The proposed solution first performs time synchronization and noise suppression on the real-time current data before comparing it with standard current data. This ensures that the data used for comparison is precisely aligned in the time dimension and eliminates environmental interference or random noise introduced during the acquisition process. This improved data quality allows the subsequently calculated first relative deviation of the current rise time, the second relative deviation of the average current value during the cutting stabilization period, and the third relative deviation of the standard deviation of the current during the cutting stabilization period to more accurately reflect the actual cutting status of the lock being processed, thus providing reliable data for subsequent safety alarm judgments.

[0152] Please refer to Figure 2 , Figure 3 A safety production data detection system, used to implement any of the above methods, the system comprising:

[0153] First acquisition module 201: acquires preset cutting parameters, performs standardized processing on the raw materials for lock manufacturing, and acquires standard current data of the motor during cutting;

[0154] The second acquisition module 202: uses preset cutting parameters to perform micro-cutting on the raw material of the lock to be processed, and acquires the real-time current data of the motor during cutting;

[0155] Comparison module 203: Compares standard current data with real-time current data to obtain the first relative deviation value of current rise time, the second relative deviation value of average current during the cutting steady period, and the third relative deviation value of current standard deviation during the cutting steady period.

[0156] Safety alarm module 204: Determines whether the first relative deviation value, the second relative deviation value, and the third relative deviation value are within their respective preset allowable deviation ranges, obtains the judgment results, and issues corresponding safety alarms based on the judgment results.

[0157] This system provides an integrated solution by modularizing complex data detection methods. It automates data acquisition, comparison, and alerting, effectively overcoming the limitations of traditional methods in distinguishing different causes of anomalies and improving the stability and efficiency of the production process. The collaborative work of each module ensures a smooth and efficient process from data acquisition to anomaly detection and alert issuance.

[0158] The various steps of the safety production data detection method have been described in detail in some of the embodiments described above in this application, and will not be repeated here. It should be emphasized that this application further provides a safety production data detection system, which realizes the automation and integration of the method by transforming the above method steps into specific hardware or software modules.

[0159] Specifically, the first acquisition module 201 can be understood as a functional unit responsible for acquiring preset cutting parameters, standardizing the processing of raw materials for lock manufacturing, and acquiring standard current data of the motor during cutting. In practical applications, the first acquisition module 201 can be integrated into the CNC system of the machine tool, utilizing the current monitoring interface and data recording function built into the CNC system to complete the acquisition of standard current data. However, this integration method may have limitations in terms of data sampling rate or processing flexibility.

[0160] The second acquisition module 202 can be understood as a functional unit responsible for performing micro-cutting on the raw material of the lock to be processed using preset cutting parameters and acquiring real-time current data of the motor during cutting. In practical applications, the second acquisition module 202 can share some hardware resources with the first acquisition module 201, such as sharing the current sensor and data acquisition card, but its operating logic and data storage path may be independent. For example, the second acquisition module 202 can be a programmable logic controller (PLC) or an embedded system, which receives micro-cutting instructions from the main control system and controls the machine tool to perform micro-cutting operations, while simultaneously acquiring motor current data in real time.

[0161] Furthermore, the comparison module 203 can be understood as a functional unit responsible for comparing standard current data with real-time current data, and obtaining a first relative deviation value of the current rise time, a second relative deviation value of the average current value during the cutting steady-state period, and a third relative deviation value of the standard deviation of the current during the cutting steady-state period. In practical applications, the comparison module can be a dedicated digital signal processor (DSP) or a field-programmable gate array (FPGA) to achieve high-speed, parallel current characteristic comparison and deviation calculation through hardware acceleration. However, this hardware implementation may be limited in terms of algorithm updates and flexibility.

[0162] Therefore, the safety alarm module 204 can be understood as a functional unit responsible for judging whether the first relative deviation value, the second relative deviation value, and the third relative deviation value are within their respective preset allowable deviation ranges, obtaining the judgment result, and issuing a corresponding safety alarm based on the judgment result. In practical applications, the safety alarm module 204 can be a simple relay control circuit that triggers an audible and visual alarm when an abnormal signal is received.

[0163] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0164] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for detecting safety production data, characterized in that, The method includes the following steps: S1: Obtain preset cutting parameters, standardize the processing of raw materials for lock manufacturing, and collect standard current data of the motor during cutting; S2: Using the preset cutting parameters, perform micro-cutting on the raw material of the lock to be processed, and collect the real-time current data of the motor during cutting; S3: Compare the standard current data with the real-time current data to obtain the first relative deviation value of the current rise time, the second relative deviation value of the average current value during the cutting stabilization period, and the third relative deviation value of the standard deviation of the current during the cutting stabilization period. S4: Determine whether the first relative deviation value, the second relative deviation value, and the third relative deviation value are within their respective preset allowable deviation ranges, obtain the determination results, and issue corresponding safety alarms based on the determination results; The preset allowable deviation range includes a first preset allowable deviation range, a second preset allowable deviation range, and a third preset allowable deviation range; Step S4 includes: S41: When the first relative deviation value, the second relative deviation value, and the third relative deviation value are within their respective preset allowable deviation ranges, a first safety alarm is issued to remind the user that the current processing conditions for the lock are qualified. S42: When the first relative deviation value exceeds the first preset allowable deviation range and / or the third relative deviation value exceeds the third preset allowable deviation range, a second safety alarm is issued to remind the user that the cutting tool of the lock to be processed is installed abnormally. S43: When the second relative deviation value exceeds the second preset allowable deviation range, a third safety alarm is issued to remind the user that the hardness of the raw material of the lock to be processed is abnormal.

2. The method for detecting safety production data according to claim 1, characterized in that, Step S1 includes: S11: Obtain raw materials from different batches for lock processing and set multiple sets of cutting parameters; S12: Using multiple sets of cutting parameters, different batches of raw materials are processed one by one, and multiple response current data of the motor are collected during cutting; S13: Analyze each of the aforementioned response current data to extract the first type of cutting response features and the second type of cutting response features; S14: Based on the first type of cutting response characteristics and the second type of cutting response characteristics, determine the preset cutting parameters, use the preset cutting parameters to standardize the processing of raw materials for lock manufacturing, and collect standard current data of the motor during cutting.

3. The method for detecting safety production data according to claim 2, characterized in that, Step S13 includes: S131: Perform low-pass filtering on each of the response current data, and calculate the current rise time, the average current during the cutting steady-state period, and the standard deviation of the current during the cutting steady-state period. The current rise time, the average current during the cutting steady-state period, and the standard deviation of the current during the cutting steady-state period are the first type of cutting response characteristics; S132: Calculate the instantaneous rate of change of each of the response current data. When the absolute value of the instantaneous rate of change exceeds a preset absolute value and falls back within a preset time, it is determined that a current pulse event has occurred, and the number of times the current pulse event has occurred is recorded. S133: Perform a short-time Fourier transform on each of the response current data, analyze its energy distribution in a preset frequency band, and obtain the number of high-frequency fluctuation events based on the energy distribution; The number of current pulse events and the number of high-frequency fluctuation events are the second type of cutting response characteristics.

4. The method for detecting safety production data according to claim 3, characterized in that, Step S14 includes: S141: Based on the first type of cutting response characteristics, select one or more sets of candidate cutting parameters from the multiple sets of cutting parameters, so that when cutting different batches of raw materials, the current rise time of the motor, the average current during the cutting stabilization period, and the standard deviation of the current during the cutting stabilization period present different values. S142: Based on the second type of cutting response characteristics, select a set of candidate cutting parameters from one or more sets of candidate cutting parameters that maximizes the sum of the number of current pulse events and the number of high-frequency fluctuation events of the motor when cutting different batches of raw materials, and use it as the preset cutting parameters. S143: The raw materials for lock processing are processed in a standardized manner using the preset cutting parameters, and the standard current data of the motor is collected during cutting.

5. The method for detecting safety production data according to claim 1, characterized in that, Step S2 includes: S21: Obtain the geometry of the raw material for the lock to be processed; S22: Based on the geometry, identify the non-critical areas of the raw material for the lock to be processed; S23: Using the preset cutting parameters, perform micro-cutting on the raw material of the lock to be processed in the non-critical area, and collect the real-time current data of the motor during cutting.

6. The method for detecting safety production data according to claim 5, characterized in that, Step S22 includes: S221: Obtain the design geometry information of the finished lock; S222: Compare the geometric shape information of the raw material with the design geometric shape information of the finished product to determine the material area on the raw material that will be removed by the main processing program; S223: Within the area of ​​material to be removed, based on a preset cutting allowance and a preset minimum distance required to be maintained between the material and the functional area of ​​the finished lock, identify non-critical areas that meet the preset cutting allowance and distance conditions.

7. A method for detecting safety production data according to claim 6, characterized in that, Step S223 includes: S2231: Obtain the geometric data of the material region to be removed, and determine discrete points within the region based on the geometric data; S2232: Calculate the first shortest distance from each of the discrete points to the design surface of the finished lock, and the second shortest distance from each of the discrete points to each functional area of ​​the finished lock; S2233: Filter out the first discrete point corresponding to the first shortest distance being greater than or equal to the preset cutting allowance, and filter out the second discrete point corresponding to the second shortest distance being greater than or equal to the preset minimum distance that needs to be maintained between the functional area of ​​the finished lock. S2234: The set of the first discrete point and the second discrete point is taken as the non-critical region.

8. The method for detecting safety production data according to claim 1, characterized in that, Step S3 includes: S31: Synchronize the real-time current data in time; S32: Perform noise suppression on the real-time current data after time synchronization; S33: Compare the real-time current data after time synchronization and noise suppression with the standard current data to obtain the first relative deviation value of the current rise time, the second relative deviation value of the average current value during the cutting stabilization period, and the third relative deviation value of the standard deviation of the current during the cutting stabilization period.

9. A safety production data detection system, characterized in that, The system for implementing the method according to any one of claims 1-8 comprises: First acquisition module: acquires preset cutting parameters, performs standardized processing on raw materials for lock manufacturing, and acquires standard current data of the motor during cutting; The second acquisition module uses the preset cutting parameters to perform micro-cutting on the raw material of the lock to be processed, and acquires the real-time current data of the motor during cutting. Comparison module: Compares the standard current data with the real-time current data to obtain the first relative deviation value of the current rise time, the second relative deviation value of the average current value during the cutting steady-state period, and the third relative deviation value of the standard deviation of the current during the cutting steady-state period. Safety alarm module: It determines whether the first relative deviation value, the second relative deviation value, and the third relative deviation value are within their respective preset allowable deviation ranges, obtains the determination results, and issues corresponding safety alarms based on the determination results; the preset allowable deviation ranges include a first preset allowable deviation range, a second preset allowable deviation range, and a third preset allowable deviation range; The safety alarm module is also used to issue a first safety alarm when the first relative deviation value, the second relative deviation value, and the third relative deviation value are within their respective preset allowable deviation ranges, so as to remind the user that the current processing conditions for the lock are qualified. When the first relative deviation value exceeds the first preset allowable deviation range and / or the third relative deviation value exceeds the third preset allowable deviation range, a second safety alarm is issued to remind the user that the cutting tool of the lock to be processed is installed abnormally. When the second relative deviation value exceeds the second preset allowable deviation range, a third safety alarm is issued to remind the user that the hardness of the raw material of the lock to be processed is abnormal.

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