Method for operating an intelligent machine tool factory based on the internet of things

Abstract

This invention relates to the field of intelligent factory management technology, specifically to an IoT-based intelligent machining factory operation management method, comprising the following steps: collecting spindle running time and temperature difference to generate thermal stability markers, extracting current interval standard deviation to generate load balancing markers, combining temperature, humidity, and air pressure to generate environmental compensation intensity instructions, verifying data time consistency to generate data alignment markers, and matching task priorities to generate a factory operation management plan. In this invention, the standard deviation of current peak intervals improves the accuracy of load disturbance identification; the combined indicators of temperature, humidity, and air pressure, along with the proportion of thin-walled parts, enhance the expressive power of environmental parameters; timestamp verification ensures consistency in multi-source data processing; and dynamic scheduling optimization is achieved by matching air pressure adjustment windows based on multi-factor task rearrangement, thereby improving the thermal stability, load balance, and environmental adaptability of the machining process, and enhancing the stability and precision control capabilities of task execution.

Description

Technical Field

[0001] This invention relates to the field of smart factory management technology, and in particular to a method for the operation and management of a smart machining factory based on the Internet of Things. Background Technology

[0002] The field of intelligent factory management technology encompasses manufacturing system control and optimization technologies that combine industrial automation, informatization, and intelligentization. The core of this technology lies in integrating sensors, controllers, actuators, and network communication platforms to achieve real-time monitoring, coordinated scheduling, and optimized control of the entire factory production process, thereby realizing efficient resource utilization and improved operational management capabilities of the manufacturing system. This field typically covers production scheduling optimization, equipment status monitoring, energy consumption management, inventory control, and personnel collaboration. By constructing a multi-layered information processing architecture, it achieves collaborative management from the workshop level to the enterprise level, and has complex information interaction and control logic design requirements.

[0003] Among them, the IoT-based intelligent machining factory operation management method refers to the construction of a perception and control system covering the machining process, which addresses aspects such as production planning, equipment usage scheduling, machining task execution status monitoring, environmental data collection and analysis, operational safety control, and energy consumption index collection in the machining workshop. This system utilizes IoT sensing terminals and communication networks to build a comprehensive perception and control system. The factory operation is organized and managed through equipment-side data uploading, edge-side data aggregation, and platform-side rule control. This approach typically includes deploying embedded temperature and humidity acquisition devices for environmental monitoring, using machining equipment operation data collectors to monitor load and start / stop status, uploading data wirelessly to an edge server for real-time aggregation and processing, judging machining status and operational anomalies according to set rules, and dynamically allocating and adjusting machining tasks based on pre-defined task priority rules.

[0004] Existing technologies in machining process management suffer from fragmented processing of multi-source parameters. Data such as thermal state, current fluctuations, and environmental disturbances are often analyzed independently as single factors, lacking a dynamic fusion mechanism based on task hierarchy. This fails to construct correlation paths between complex variables, resulting in some anomalies not being identified or accurately traced in their early stages. Regarding load state identification, processing methods often rely on motor start / stop or average current values, failing to refine the temporal characteristics of cutting fluctuations. This leads to coarse assessments of task execution stability, making it difficult to reflect true load dynamics. Environmental parameter acquisition is mostly used for independent monitoring or early warning triggering, without cross-analysis of the results with the task structure. Especially in sensitive processes such as thin-walled parts, the potential impact of environmental disturbances on machining accuracy lacks a systematic feedback mechanism, easily causing inconsistencies in batch product quality. In terms of sensing data coordination, data synchronization control mechanisms lack integrity verification processes, resulting in issues such as overlapping time ranges and disordered command response sequences, affecting the accuracy and effectiveness of scheduling strategies. Task scheduling methods are based on fixed priority rules and lack the ability to dynamically adjust the task structure according to equipment status and environment, limiting the response efficiency for task collaboration and resource matching in complex production scenarios. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing an intelligent machining factory operation and management method based on the Internet of Things.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent machining factory operation management method based on the Internet of Things, comprising the following steps:

[0007] S1: Collect the continuous running timestamp of the CNC spindle, extract the time length of the continuous machining segment without interruption between tool change operation points, obtain the temperature difference within the time period, and generate a thermal stability mark by comparing it with the critical threshold of thermal deformation in the equipment manual.

[0008] S2: Based on the time window of the thermal stability mark, collect the current fluctuation data of the spindle motor, calculate the standard deviation of the time interval of the current peak, and generate a load balancing mark by comparing the interval fluctuation range under the rated power.

[0009] S3: Call the thermal stability marker timestamp, collect the average temperature and humidity values ​​in the workshop, multiply them, subtract the air pressure value of the processing table, and generate an environmental compensation intensity command;

[0010] S4: Receive the start and end time of the thermal stability mark, verify whether the current acquisition covers the spindle cycle, detect whether the compensation command is delayed, remove data segments whose deviation exceeds the machining cycle, and generate a data alignment mark.

[0011] S5: Based on the data alignment mark, input the thermal deformation level value of the thermal stability mark, retrieve whether the current interval standard deviation in the load balancing mark exceeds the limit, match the thin-walled part task compensation priority in the environmental compensation intensity instruction, insert air pressure adjustment in the idle window, and generate a factory operation management plan.

[0012] As a further aspect of the present invention, the thermal stability marker includes temperature difference value, continuous processing time, temperature difference fluctuation rate per unit time, and thermal deformation level; the load balancing marker includes current peak interval standard deviation, fluctuation range comparison results, and load stability level; the environmental compensation intensity command includes average temperature and humidity value, air pressure correction parameter, thin-walled part order ratio, and compensation intensity level; the data alignment marker includes current acquisition time coverage status, command generation timing relationship, and processing cycle deviation rejection identifier; and the factory operation management scheme includes task number reordering, compensation task identification, and idle time window insertion strategy.

[0013] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0014] S101: Collect CNC spindle running timestamps, identify tool change operation points, extract the start and end times of uninterrupted machining segments between adjacent tool change points, calculate the corrected duration of the machining segment, and generate continuous machining time.

[0015] S102: Call the time period corresponding to the continuous processing time, synchronously collect the temperature sensor values ​​of the front end and the tail end of the spindle, calculate the absolute value of the difference between the front end temperature and the tail end temperature in each time period, and generate the temperature difference result.

[0016] S103: Divide the temperature difference result by the continuous processing time of the corresponding time period to obtain the temperature difference change rate per unit time. Call the temperature fluctuation rate threshold range in the spindle thermal deformation critical threshold table in the equipment manual to determine whether the current temperature difference change rate exceeds the upper or lower limit of the corresponding range and generate a thermal stability mark.

[0017] As a further aspect of the present invention, the formula for calculating the corrected duration of the processing segment is as follows:

[0018] ;

[0019] in, Representing the The corrected duration of each processing segment Representing the The end timestamp of each processing segment Representing the The start timestamp of each processing segment Representing the Spindle load fluctuation coefficient between adjacent timestamps This represents the total number of timestamp sampling points within the current processing segment. This represents the load compensation factor generated based on historical downtime data.

[0020] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0021] S201: Based on the time window of the thermal stability mark, collect the current waveform data of the spindle drive motor, identify the current peak point of the cutting feed and record the corresponding timestamp, and generate the current peak sequence;

[0022] S202: Call the time interval between adjacent peaks in the current peak sequence, calculate the standard deviation of all interval values, remove interference terms during the idling phase, and generate peak interval fluctuation.

[0023] S203: Compare the peak interval fluctuation with the upper and lower limits of the interval range corresponding to the rated power of the motor in the equipment manual. If the fluctuation exceeds the range, mark it as abnormal and generate a load balancing mark.

[0024] As a further aspect of the present invention, the formula for calculating the standard deviation of all interval values ​​is as follows:

[0025]

[0026] in, The standard deviation represents the total range of values. Representing the The time interval between adjacent current peak values. The arithmetic mean of all time intervals between current peaks. Representing the The current value at the previous peak point in the group. Representing the The current value at the last peak point in the group. Represents the current value of the previous point among all peak points. The average value, Represents the current value at the last point among all peak points. The average value, This represents the number of available adjacent peak interval groups.

[0027] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0028] S301: Based on the timestamp range of the thermal stability marker, synchronously call the temperature monitoring value and humidity monitoring value of the workshop temperature and humidity monitoring point within the time period, calculate the arithmetic mean of the two, and generate the temperature and humidity monitoring result;

[0029] S302: Based on the multiplication of the average temperature and average humidity in the temperature and humidity monitoring results, call the real-time air pressure monitoring value of the processing table, subtract the air pressure monitoring value from the product result, and generate the environmental compensation base.

[0030] S303: Based on the environmental compensation base, obtain the number of thin-walled part processing orders and the total number of orders in the current task queue, calculate the ratio between the two, multiply the environmental compensation base by the ratio, and generate an environmental compensation intensity instruction.

[0031] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0032] S401: Call the timestamp start and end points of the thermal stability marker to obtain the start and end times of the spindle running cycle, compare whether the start point of the current acquisition time period marked by the load balancing marker is earlier than the start point of the spindle running cycle and whether the end point is later than the end point of the spindle running cycle, and generate a coverage status determination.

[0033] S402: Based on the timestamp range of the thermal stability mark and the timestamp range of the load balancing mark, extract the generation time of the environmental compensation intensity command, calculate the absolute value of the difference between the generation time and the starting point of the first two marks, and generate the hysteresis deviation.

[0034] S403: Based on the coverage status determination and the hysteresis deviation, call the single processing cycle value set by the processing table, determine whether the absolute value of the time coverage deviation and the hysteresis deviation in the coverage status determination exceed the cycle value, calculate the timestamp range quality assurance of all out-of-limit data segments, remove them, and generate data alignment marks.

[0035] As a further aspect of the present invention, the formula for calculating the absolute value of the time coverage deviation in the coverage status determination is as follows:

[0036]

[0037] This represents the absolute value of the time coverage deviation in the coverage status determination. This represents the amount of lag bias. This represents a dynamic correction coefficient based on the historical lag deviation mean. Represents the time coverage weighting factor. It represents the smallest positive constant that prevents the denominator from being zero.

[0038] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0039] S501: Call the time range of the data alignment mark, extract the thermal deformation level value of the thermal stability mark, the current interval standard deviation exceeding the limit state of the load balancing mark, and the thin-walled component priority coefficient of the environmental compensation intensity command, and generate a task parameter set;

[0040] S502: Based on the numerical values ​​of the thermal deformation levels in the task parameter set, sort the task numbers in descending order, adjust the sorting weights in combination with the priority coefficient of thin-walled parts, and generate a task sorting index.

[0041] S503: Locate the task node requiring compensation in the task sorting index, call the start time and duration of the idle time window after the air pressure adjustment, insert the corresponding task's subsequent node gap, and generate the factory operation management plan.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0043] In this invention, the accuracy of load disturbance identification is improved by the standard deviation of current peak interval; the combination of temperature, humidity and air pressure composite indicators with the proportion of thin-walled parts enhances the ability to express environmental parameters; timestamp verification ensures the consistency of multi-source data processing; and dynamic scheduling optimization is achieved by matching air pressure adjustment windows based on multi-factor task rearrangement. This improves the thermal stability, load balance and environmental adaptability of the processing process, and enhances the stability and accuracy control capability of task execution. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of the steps of the present invention. Detailed Implementation

[0046] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0047] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0048] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0049] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0050] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0051] Please see Figure 1 The IoT-based intelligent machining factory operation and management method includes the following steps:

[0052] S1: Collect the continuous running timestamp of the CNC spindle, extract the time length of the continuous machining segment without interruption between tool change operation points, obtain the temperature difference value between the front temperature sensor and the tail temperature sensor of the spindle within the time period, divide the temperature difference value by the continuous machining segment length to obtain the temperature difference fluctuation rate per unit time, compare it with the spindle thermal deformation critical threshold table in the equipment manual, and generate a thermal stability mark.

[0053] S2: Based on the time window length corresponding to the thermal stability mark, collect the current fluctuation waveform data of the spindle drive motor in the continuous machining section, calculate the standard deviation of the time interval between two adjacent cutting feed current peaks, compare the interval fluctuation range under the rated power of the motor, and generate a load balancing mark.

[0054] S3: Call the timestamp range of the thermal stability marker, synchronously obtain the average value of the workshop temperature and humidity monitoring points within the time period, multiply the temperature value by the humidity value and subtract the real-time air pressure monitoring value of the processing table, and combine it with the ratio of the number of thin-walled part processing orders in the current task queue to the total number of orders to generate an environmental compensation intensity command.

[0055] S4: Receive the start and end timestamps of the thermal stability marker, verify whether the current acquisition time period of the load balancing marker fully covers the spindle running cycle, detect whether the generation time of the environmental compensation intensity command lags behind the first two markers, remove data segments whose timestamp coverage deviation exceeds a single machining cycle, and generate a data alignment marker.

[0056] S5: Based on the data alignment mark, input the thermal deformation level value of the thermal stability mark, retrieve whether the current interval standard deviation in the load balancing mark exceeds the limit, match the thin-walled part task compensation priority in the environmental compensation intensity instruction, rearrange the task number according to the thermal deformation level, insert the idle time window after the air pressure adjustment takes effect for the task that needs compensation, and generate the factory operation management plan.

[0057] Thermal stability markers include temperature difference values, continuous processing time, temperature difference fluctuation rate per unit time, and thermal deformation level. Load balancing markers include current peak interval standard deviation, fluctuation range comparison results, and load stability level. Environmental compensation intensity instructions include average temperature and humidity values, air pressure correction parameters, thin-walled part order ratio, and compensation intensity level. Data alignment markers include current acquisition time coverage status, instruction generation timing relationship, and processing cycle deviation rejection mark. Factory operation management schemes include task number reordering, compensation task identification, and idle time window insertion strategy.

[0058] The specific steps of S1 are as follows:

[0059] S101: Collect CNC spindle running timestamps, identify tool change operation points, extract the start and end times of uninterrupted machining segments between adjacent tool change points, calculate the corrected duration of the machining segment, and generate continuous machining time.

[0060] The formula for calculating the corrected duration of the processing section is as follows:

[0061] ;

[0062] in, Representing the The corrected duration of each processing segment Representing the The end timestamp of each processing segment Representing the The start timestamp of each processing segment Representing the Spindle load fluctuation coefficient between adjacent timestamps This represents the total number of timestamp sampling points within the current processing segment. This represents the load compensation factor generated based on historical downtime data;

[0063] Parameter assignment and calculation process:

[0064] Basic time difference calculation:

[0065] No. Start timestamp of each processing segment The time stamp is 2025-04-21-08:15:30 (corresponding to a second-level timestamp 1713672930), and the end timestamp is... It is 2025-04-21-08:45:30 (corresponding to the second-level timestamp 1713674730).

[0066] Initial time difference Seconds (30 minutes).

[0067] Spindle load fluctuation coefficient :

[0068] For the first The standard deviation of spindle load fluctuation between adjacent timestamps is calculated by real-time monitoring of spindle load current data.

[0069] Data source: Sampling interval is 0.5 seconds, with a total of 60 timestamps in the current processing segment ( Standard deviation of load current between two adjacent timestamps The calculation results are shown in the following example:

[0070] (Unit: A) , , ..., (the remaining All values ​​are calculated based on actual monitoring data and meet the standard deviation range of 0.5~2.0A in industrial scenarios.

[0071] Sum of squares calculation: .

[0072] Load compensation factor :

[0073] Generated from historical downtime data, this data is derived by fitting the correlation between downtime intervals and load fluctuations of the same CNC machine tool model over the past month. (Reasonable range: 0.1~0.3).

[0074] Correction term calculation:

[0075] Second.

[0076] Final duration calculation:

[0077] Seconds (approximately 29.9995 minutes).

[0078] Parameter setting basis and data quantification:

[0079] and The timestamps are derived from the CNC system logs and are accurate to the second level.

[0080] The load current is collected in real time by a spindle current sensor, and the fluctuation intensity is quantified by the standard deviation of the current values ​​between adjacent timestamps.

[0081] The time stamp sampling frequency (0.5 seconds) and the total processing time (30 minutes) are determined. However, the actual calculation example is simplified. To shorten the calculation cycle;

[0082] Based on historical data statistics, the correlation between downtime intervals and load fluctuations was analyzed through linear regression and then set. The larger the value, the higher the weight of the impact of load fluctuations on time correction.

[0083] Correlation between numerical results and steps:

[0084] Calculation results The second represents the corrected continuous machining time, indicating that the original time difference of 1800 seconds has been reduced by 0.029 seconds due to spindle load fluctuations and compensation factors. This value is directly used as input data for "generating continuous machining time" in step S101. The correction term is used to eliminate non-machining interruption time caused by load fluctuations, ensuring that the output time only includes valid machining cycles.

[0085] S102: Call the time period corresponding to the continuous processing time, synchronously collect the temperature sensor values ​​at the front end and the tail end of the spindle, calculate the absolute value of the difference between the front end temperature and the tail end temperature in each time period, and generate the temperature difference result.

[0086] The previously determined 742-second continuous machining time period was used as the reference interval for temperature data acquisition. Starting from the machining start time of 08:03:21 and ending at 08:15:43, real-time sampling values ​​from the temperature sensors at the front and rear ends of the spindle were read once per second within this interval. One pair of temperature data was obtained per second, forming 742 temperature value pairs. For example, at the start, the front end temperature was 45.2°C and the rear end temperature was 44.1°C. Within the 742-second interval, the highest front end temperature was 48.6°C and the lowest was 44.1°C. The temperature ranges from 4.8°C at the front end to a maximum of 46.5°C and a minimum of 43.3°C at the rear end. Then, in each second of sampling, the difference between the front and rear temperatures is calculated and the absolute value is taken. For example, if the front temperature is 47.1°C and the rear temperature is 45.3°C at the 200th second, the difference is 1.8°C. This generates a temperature difference sequence of 742 data points, covering the entire processing section. This temperature difference sequence serves as the raw input data for subsequent thermal stability calculations. All data is retained to one decimal place to ensure that the details of temperature difference changes are fully preserved for further analysis.

[0087] S103: Divide the temperature difference result by the continuous processing time of the corresponding time period to obtain the temperature difference change rate per unit time. Call the temperature fluctuation rate threshold range in the spindle thermal deformation critical threshold table in the equipment manual to determine whether the current temperature difference change rate exceeds the upper or lower limit of the corresponding range and generate a thermal stability mark.

[0088] The 742 temperature difference data points generated above are summed sequentially, assuming a total temperature difference of 1038.3°C. This value is then divided by the corresponding processing time of 742 seconds to calculate the temperature change rate per unit time as 1.4°C / s. This result needs to be compared with the spindle thermal deformation critical threshold range listed in the equipment manual. Here, the spindle model is H75. Based on historical test data and stable operation samples of this model, three reference ranges for temperature change rate are derived, with the stable range set at 0.3°C / s. The temperature range is 1.3°C / s to 1.1°C / s. This range is based on data from 30 spindles operating under stable temperature control for 120 minutes. Below 0.3°C / s is the warning zone for insufficient thermal drift, and above 1.1°C / s is the zone for severe abnormal thermal drift. The current processing segment's temperature change rate is 1.4°C / s, which significantly exceeds the upper limit. Therefore, when performing the judgment action, the current 1.4°C / s is compared with 1.1°C / s. The judgment result is that it exceeds the upper limit, so the thermal stability mark is generated as "0", indicating that the temperature difference fluctuation rate of this processing segment is in an abnormal state, and it is recorded in the thermal stability data field.

[0089] The specific steps of S2 are as follows:

[0090] S201: Based on the time window marked by thermal stability, collect the current waveform data of the spindle drive motor, identify the current peak point of the cutting feed and record the corresponding timestamp, and generate the current peak sequence;

[0091] Based on the time window marked by thermal stability, the start and end times of the machining segment marked as thermally unstable in the spindle thermal stability analysis module are first used as the data extraction interval. Within this interval, the real-time acquisition module of the spindle drive motor current, relying on the equipment control system, collects the current value of the current under the current operating state once per second. Complete current waveform data is obtained through continuous acquisition. Then, the peak points in the current waveform are scanned and judged one by one. By comparing the changing trend of the current value at multiple consecutive moments, the peak point where the current value rises to the highest point and then turns to fall is identified, and the value of this point and the corresponding timestamp are recorded. For example, at 08:05:12, the current value rises from 8.1A to 8.6A and then drops to 8.2A, so 08:05:12 is confirmed as a current peak point. In this way, all data points within 742 seconds are traversed, and finally 47 current peak points are identified, forming a current peak sequence. This sequence contains the current intensity and occurrence time information corresponding to each peak, where the maximum peak is 9.2A and the minimum is 6.4A. The timestamps of all peak points are arranged in sequence to form the current peak sequence of the complete machining segment.

[0092] S202: Call the time interval between adjacent peaks in the current peak sequence, calculate the standard deviation of all interval values, remove interference terms during the idling phase, and generate peak interval fluctuation.

[0093] The formula for calculating the standard deviation of all interval values ​​is as follows:

[0094]

[0095] in, The standard deviation represents the total range of values. Representing the The time interval between adjacent current peak values. The arithmetic mean of all time intervals between current peaks. Representing the The current value at the previous peak point in the group. Representing the The current value at the last peak point in the group. Represents the current value of the previous point among all peak points. The average value, Represents the current value at the last point among all peak points. The average value, This represents the number of available adjacent peak interval groups.

[0096] The time interval data between adjacent peaks in the current peak sequence were collected. The sampling frequency was set to 1000Hz and the sampling time was 10 seconds, resulting in 10 adjacent peak intervals (unit: milliseconds).

[0097] ;

[0098] Calculate the average across all time intervals:

[0099] ;

[0100] Collect the current values ​​(unit: amperes) of each group of adjacent peak points:

[0101] ;

[0102] Calculate the average of the current values ​​at all previous peak points:

[0103] ;

[0104] Calculate the average of all subsequent peak current values:

[0105] ;

[0106] Calculate the weighted difference for each data set and sum them:

[0107] ;

[0108] Sum the above results:

[0109] ;

[0110] Calculate the standard deviation:

[0111] ;

[0112] The calculation results show that by measuring the actual current peak value and time interval, the standard deviation fluctuation of the time interval can be accurately calculated. This result can effectively reflect the fluctuation characteristics and abnormal state of the current peak sequence, and can be used for subsequent current stability analysis and quality control.

[0113] S203: Compare the peak interval fluctuation with the upper and lower limits of the interval range corresponding to the motor's rated power in the equipment manual. If the fluctuation exceeds the range, mark it as abnormal and generate a load balancing flag.

[0114] The peak interval fluctuation of 3.8 seconds is used as the input value and compared with the standard fluctuation range under the rated power condition of the corresponding spindle drive motor in the equipment manual. The current motor model is EM92 with a rated power of 15kW. According to the equipment manual, based on 500 hours of stable operation samples, the acceptable fluctuation range is 1.2 seconds to 2.9 seconds. This range is obtained by selecting the upper and lower 5% confidence intervals after distribution fitting of the measured stability data of the current peak interval under different loads. The specific upper and lower limits are taken from the 25th and 475th ranked values ​​of the historical fluctuation, respectively. The current calculated fluctuation of 3.8 seconds is greater than the upper limit of 2.9 seconds. When performing the judgment action, 3.8 and 2.9 are directly compared. The judgment condition is that if the fluctuation is greater than 2.9 or less than 1.2, it is marked as abnormal. Therefore, the current condition of exceeding the upper limit is met, and the load balance mark is generated as "0", indicating that the motor load fluctuation in the current processing process has exceeded the reasonable range.

[0115] The specific steps for S3 are as follows:

[0116] S301: Based on the timestamp range of the thermal stability marker, synchronously call the temperature monitoring value and humidity monitoring value of the workshop temperature and humidity monitoring point within the time period, calculate the arithmetic mean of the two, and generate the temperature and humidity monitoring results;

[0117] Based on the timestamp range of the thermal stability marker, the start and end times of the processing segment with a thermal stability marker of "0" are first retrieved. Within this time interval, temperature and humidity monitoring values ​​from the environmental monitoring nodes installed in the workshop are simultaneously extracted. Taking a collection frequency of once per minute as an example, a total of 13 sets of temperature and humidity data were collected between 08:03 and 08:15. The temperature data were 26.3℃, 26.5℃, 26.7℃, 27.0℃, 27.1℃, 27.3℃, 27.2℃, 27.0℃, 26.9℃, 26.8℃, 26.6℃, 26.5℃, and 26.4℃, respectively, and the humidity data were... The values ​​were 51.0%, 50.8%, 50.6%, 50.5%, 50.4%, 50.2%, 50.3%, 50.4%, 50.5%, 50.6%, 50.8%, 51.0%, and 51.1%. The arithmetic mean of these two sets of data was then calculated: the average temperature was (26.3 + 26.5 + ... + 26.4) ÷ 13 = 26.82℃, and the average humidity was (51.0 + 50.8 + ... + 51.1) ÷ 13 = 50.59%. These two results were recorded as the environmental temperature and humidity monitoring results for this processing section, serving as the basic input data for subsequent environmental compensation factors.

[0118] S302: Based on the product of the average temperature and average humidity in the temperature and humidity monitoring results, call the real-time air pressure monitoring value of the processing table, subtract the air pressure monitoring value from the product result, and generate the environmental compensation base.

[0119] The average temperature in the temperature and humidity monitoring results was 26.82℃, and the average humidity was 50.59%. First, the two values ​​were multiplied to obtain a product of 1357.35 (in ℃·%). Then, the corresponding air pressure monitoring values ​​for the processing table were extracted within the thermal stability marked interval. A total of 13 sets of air pressure data were obtained within the same time range, in hPa, with values ​​of 101.2, 101.1, 101.1, 101.0, 100.9, 100.8, 100.8, 100.9, and 101... The arithmetic mean of the values ​​0, 101.1, 101.2, 101.2, and 101.3 is calculated to be 101.06 hPa. Then, the average air pressure of 101.06 is subtracted from the product of temperature and humidity (1357.35). The action is to directly subtract the two numbers to obtain the environmental compensation base of 1256.29, with the unit being ℃·%-hPa. This value is used to reflect the intensity of the combined fluctuation of ambient temperature, humidity, and air pressure in the processing section on the operating status of the equipment. This value is recorded as the reference benchmark for the environmental impact of the current processing section.

[0120] S303: Based on the environmental compensation base, obtain the number of thin-walled part processing orders and the total number of orders in the current task queue, calculate the ratio between the two, multiply the environmental compensation base by the ratio, and generate an environmental compensation intensity instruction;

[0121] Based on the generated environmental compensation base of 1256.29, the current task queue retrieval operation stage is entered. This stage retrieves the structural component types and quantities of all pending machining tasks in the current CNC scheduling queue. Among these, there are 18 thin-walled part machining orders, and a total of 45 orders. First, the ratio between the two is calculated as 18 ÷ 45 = 0.4. Then, the environmental compensation base of 1256.29 is multiplied by this ratio of 0.4, and the action is 1256.29 × 0.4 = 502.52. This result of 502.52 is recorded as the environmental compensation intensity command for the corresponding thin-walled part machining task under the current environmental conditions. This command value will be used later to adjust the relevant dynamic compensation parameters during the machining process.

[0122] The specific steps of S4 are as follows:

[0123] S401: Call the start and end timestamps of the thermal stability marker to obtain the start and end times of the spindle running cycle. Compare the start point of the current acquisition time period marked by the load balancing marker with the start point of the spindle running cycle to see if it is earlier than the start point of the spindle running cycle and the end point is later than the end point of the spindle running cycle, and generate a coverage status determination.

[0124] The system retrieves the start and end timestamps of the thermal stability marker. First, it extracts the start time (08:03:21) and end time (08:15:43) recorded in the spindle thermal stability analysis module as the spindle running cycle time range for this machining segment. Then, it retrieves the time segment of current waveform data collected during the load balancing marker process. This data segment starts at 08:03:17 and ends at 08:15:48. During the comparison operation, the start and end times are compared sequentially to determine whether the current data start time (08:03:17) is earlier than the spindle running start time (08:03:21). The result is yes. At the same time, it is determined whether the current data end time (08:15:48) is later than the spindle running end time (08:15:43). The result is also yes. If both comparison judgments are true, it is confirmed that the current acquisition time segment completely covers the spindle running cycle range, and this result is recorded as "complete coverage". If any time item does not meet the corresponding sequential relationship during the judgment process, it is recorded as "insufficient coverage" or "exceeding". The coverage status generated in the current example is determined to be "complete coverage".

[0125] S402: Based on the timestamp range of thermal stability marker and load balancing marker, extract the generation time of environmental compensation intensity command, calculate the absolute value of the difference between the generation time and the starting point of the first two markers, and generate the hysteresis deviation.

[0126] Based on the timestamp range of thermal stability marker (08:03:21 to 08:15:43) and load balancing marker (08:03:17 to 08:15:48), the generation time of the environmental compensation intensity command (08:03:55) is extracted. The absolute value of the time difference between this time and the starting point of thermal stability and the starting point of load balancing is calculated. First, 08:03:55 is subtracted from 08:03:21, and the difference is 34 seconds. Then, 08:03:55 is subtracted from 08:03:17, and the difference is 38 seconds. The absolute values ​​of the two differences are recorded as 34 seconds and 38 seconds respectively. Then, in this step, the definition of the lag deviation is confirmed as the larger of the two differences. Therefore, in this example, the lag deviation is 38 seconds, indicating that there is a 38-second delay between the generation time of the compensation command and the start time of the load data. This value will be used as a reference for subsequent data alignment judgment.

[0127] S403: Based on the coverage status determination and hysteresis deviation, call the single processing cycle value set by the processing table, calculate the absolute value of the time coverage deviation in the coverage status determination, determine whether the absolute value of the time coverage deviation and the hysteresis deviation in the coverage status determination exceed the cycle value, analyze the timestamp range quality assurance of all out-of-limit data segments, and remove them, and generate data alignment marks.

[0128] The specific formula for calculating the absolute value of time coverage deviation in coverage status determination is as follows:

[0129] ;

[0130] in, This represents the absolute value of the time coverage deviation in the coverage status determination. Represents the current system timestamp. The timestamp that triggered the overwrite status determination. This represents the amount of lag bias. This represents the value of a single machining cycle set for the current machining table. This represents a dynamic correction coefficient based on the historical lag deviation mean. Represents the time coverage weighting factor. It represents the smallest positive constant that prevents the denominator from being zero;

[0131] Current system timestamp Overwrite status determination trigger timestamp The absolute value of the time difference is calculated as follows: Hysteresis deviation The lag time data is collected in real time by sensors on the machining table, and after data filtering, the moving average of the three most recent lag times is taken. Machining cycle time value. The parameters are directly read from the preset parameters in the machining table control system. Dynamic correction coefficient. The average value of lag deviation over historical processing cycles The calculation formula is as follows: Substitute have to Time coverage weighting factor The constant is set according to the balanced weight ratio of time coverage and cycle time in the system design requirements. To fix the minimum value and prevent the denominator from being zero, substitute into the formula to calculate:

[0132] ;

[0133] judge and Does it exceed Since neither exceeded the limits, the current data segment does not need to be removed. This result indicates that the absolute value of the time coverage deviation, after correction, is still within the allowable range of the clock cycle, and the data segment has passed verification and generated alignment markers.

[0134] Parameter values ​​are based on:

[0135] and Acquired through system clock synchronization; The hysteresis time is monitored by the sensor and then filtered. These are preset parameters; Dynamically adjusted based on the historical lag deviation mean; The weights are fixed according to the system design. These are engineering constants. Parameter range verification: In industrial processing scenarios, Reasonable range is , Reasonable range is , Usually All settings are consistent with actual configurations.

[0136] The specific steps of S5 are as follows:

[0137] S501: Call the time range of the data alignment mark, extract the thermal deformation level value of the thermal stability mark, the current interval standard deviation exceeding the limit state of the load balancing mark, and the thin-walled component priority coefficient of the environmental compensation intensity command, and generate the task parameter set.

[0138] The data alignment marker is invoked within the time range of 08:03:21 to 08:15:43. The three marker parameters generated within the corresponding time range are extracted sequentially. First, the spindle temperature difference change rate of the corresponding processing section is obtained from the thermal stability marker, which is 1.4°C / s. The corresponding thermal deformation level is set to level 3 (in the 0-3 standard, 0 represents stable and 3 represents severe deformation). Then, the load balancing marker is retrieved. The standard deviation of the current peak interval in this section is 3.8 seconds. The system's qualified standard is a maximum of 2.9 seconds, so it is judged as an "over-limit" state, and the state value is recorded as 1. Next, the environmental compensation intensity command generated for this processing section is extracted from the environmental compensation module as 502. 52. Simultaneously, based on the task structure information, the corresponding processing task for this segment is a thin-walled part. The compensation priority coefficient for thin-walled parts is set to 0.8 in the system. This coefficient is defined through the task attribute manual. For structural tasks with a plate thickness of less than 3mm and sensitive to thermal and load responses, the coefficient range is 0.7 to 1.0. Based on the thin-walled properties of the current task parameters and the previous vibration test records, the coefficient is determined to be 0.8. Finally, the three parameters are uniformly collected using the task number as the index to form a task parameter set consisting of the number, thermal deformation level, load state, and compensation coefficient. For example: Task number #T023, thermal level: 3, load state: 1, priority coefficient: 0.8.

[0139] S502: Based on the numerical values ​​of thermal deformation levels in the task parameter set, sort the task numbers in descending order, adjust the sorting weights in combination with the priority coefficient of thin-walled parts, and generate a task sorting index.

[0140] The task parameters are sorted according to the thermal deformation level of each task. The process involves arranging the thermal level field in all task records in descending order. If multiple tasks have the same thermal level, a secondary sort is performed based on the priority coefficient. Specifically, each task's thermal level value is multiplied by a coefficient value to generate a weighted score. For example, if task A has a thermal level of 3, a coefficient of 0.8, and a score of 2.4, while task B has a thermal level of 3, a coefficient of 0.6, and a score of 1.8, then task A has a higher ranking. If the thermal levels are different, the order is determined directly by the level. After sorting, an index list is generated sequentially. If the sorting results are T023, T019, T008, and T011, then the task sorting index is #T023→#T019→#T008→#T011. The corresponding numbers are mapped to the task node order in the scheduling list, serving as the basis for subsequent compensation insertion and job scheduling.

[0141] S503: Locate the task node requiring compensation in the task sorting index, call the start time and duration of the idle time window after the air pressure adjustment, insert the corresponding task's subsequent node gap, and generate the factory operation management plan.

[0142] Locate the task node requiring compensation in the task sorting index. For example, if task number T023 has a corresponding compensation intensity instruction value of 502.52, which exceeds the system's set basic impact value threshold of 400 (this threshold is defined by the process evaluation team as the critical point where structural components do not require adjustment under normal conditions), then mark this task as requiring compensation. Subsequently, retrieve the available idle time window information after air pressure adjustment from the real-time scheduling system of the machining station. The current idle period for this machining station is from 08:18:00 to 08:23:00, lasting for 5 minutes. The judgment action is as follows: The original scheduled end time of the task T023 requiring compensation is 08:15:43, and the scheduled start time of its subsequent node T024 is 08:25:00. There is a gap of 7 minutes and 17 seconds in between, so the insertion condition is met. The compensated execution node is inserted at 08:18:00, and the start time of T024 is updated to 08:23:00. The new scheduling record formed after the execution node order is adjusted is T023 compensation segment → T024. Finally, the adjusted factory operation management plan is generated, and the new scheduling sequence and compensation operation identifier are recorded.

[0143] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for the operation and management of an intelligent machining factory based on the Internet of Things, characterized in that, Includes the following steps: S1: Collect the continuous running timestamp of the CNC spindle, extract the time length of the continuous machining segment without interruption between tool change operation points, obtain the temperature difference within the time period, and generate a thermal stability mark by comparing it with the critical threshold of thermal deformation in the equipment manual. S2: Based on the time window of the thermal stability mark, collect the current fluctuation data of the spindle motor, calculate the standard deviation of the time interval of the current peak, and generate a load balancing mark by comparing the interval fluctuation range under the rated power. S3: Call the thermal stability marker timestamp, collect the average temperature and humidity values ​​in the workshop, multiply them, subtract the air pressure value of the processing table, and generate an environmental compensation intensity command; S4: Receive the start and end time of the thermal stability mark, verify whether the current acquisition covers the spindle cycle, detect whether the compensation command is delayed, remove data segments whose deviation exceeds the machining cycle, and generate a data alignment mark. S5: Based on the data alignment mark, input the thermal deformation level value of the thermal stability mark, retrieve whether the current interval standard deviation in the load balancing mark exceeds the limit, match the thin-walled part task compensation priority in the environmental compensation intensity instruction, insert air pressure adjustment in the idle window, and generate a factory operation management plan.

2. The method for operation and management of an intelligent machining factory based on the Internet of Things according to claim 1, characterized in that, The thermal stability markers include temperature difference values, continuous processing time, temperature difference fluctuation rate per unit time, and thermal deformation level. The load balancing markers include the standard deviation of current peak interval, fluctuation range comparison results, and load stability level. The environmental compensation intensity instructions include average temperature and humidity values, air pressure correction parameters, thin-walled part order ratio, and compensation intensity level. The data alignment markers include current acquisition time coverage status, instruction generation timing relationship, and processing cycle deviation rejection identifier. The factory operation management scheme includes task number reordering, compensation task identification, and idle time window insertion strategy.

3. The method for intelligent machining factory operation management based on the Internet of Things according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Collect CNC spindle running timestamps, identify tool change operation points, extract the start and end times of uninterrupted machining segments between adjacent tool change points, calculate the corrected duration of the machining segment, and generate continuous machining time. S102: Call the time period corresponding to the continuous processing time, synchronously collect the temperature sensor values ​​of the front end and the tail end of the spindle, calculate the absolute value of the difference between the front end temperature and the tail end temperature in each time period, and generate the temperature difference result. S103: Divide the temperature difference result by the continuous processing time of the corresponding time period to obtain the temperature difference change rate per unit time. Call the temperature fluctuation rate threshold range in the spindle thermal deformation critical threshold table in the equipment manual to determine whether the current temperature difference change rate exceeds the upper or lower limit of the corresponding range and generate a thermal stability mark.

4. The method for operation and management of an intelligent machining factory based on the Internet of Things according to claim 3, characterized in that, The formula for calculating the corrected duration of the processing section is as follows: ； in, Representing the The corrected duration of each processing segment Representing the The end timestamp of each processing segment Representing the The start timestamp of each processing segment Representing the Spindle load fluctuation coefficient between adjacent timestamps This represents the total number of timestamp sampling points within the current processing segment. This represents the load compensation factor generated based on historical downtime data.

5. The method for operation and management of an intelligent machining factory based on the Internet of Things according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the time window of the thermal stability mark, collect the current waveform data of the spindle drive motor, identify the current peak point of the cutting feed and record the corresponding timestamp, and generate the current peak sequence; S202: Call the time interval between adjacent peaks in the current peak sequence, calculate the standard deviation of all interval values, remove interference terms during the idling phase, and generate peak interval fluctuation. S203: Compare the peak interval fluctuation with the upper and lower limits of the interval range corresponding to the rated power of the motor in the equipment manual. If the fluctuation exceeds the range, mark it as abnormal and generate a load balancing mark.

6. The method for operation and management of an intelligent machining factory based on the Internet of Things according to claim 5, characterized in that, The formula for calculating the standard deviation of all interval values ​​is as follows: ； in, The standard deviation represents the total range of values. Representing the The time interval between adjacent current peak values. The arithmetic mean of all time intervals between current peaks. Representing the The current value at the previous peak point in the group. Representing the The current value at the last peak point in the group. Represents the current value of the previous point among all peak points. The average value, Represents the current value at the last point among all peak points. The average value, This represents the number of available adjacent peak interval groups.

7. The method for operation and management of an intelligent machining factory based on the Internet of Things according to claim 1, characterized in that, The specific steps of S3 are as follows: S301: Based on the timestamp range of the thermal stability marker, synchronously call the temperature monitoring value and humidity monitoring value of the workshop temperature and humidity monitoring point within the time period, calculate the arithmetic mean of the two, and generate the temperature and humidity monitoring result; S302: Based on the multiplication of the average temperature and average humidity in the temperature and humidity monitoring results, call the real-time air pressure monitoring value of the processing table, subtract the air pressure monitoring value from the product result, and generate the environmental compensation base. S303: Based on the environmental compensation base, obtain the number of thin-walled part processing orders and the total number of orders in the current task queue, calculate the ratio between the two, multiply the environmental compensation base by the ratio, and generate an environmental compensation intensity instruction.

8. The method for operation and management of an intelligent machining factory based on the Internet of Things according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Call the timestamp start and end points of the thermal stability marker to obtain the start and end times of the spindle running cycle, compare whether the start point of the current acquisition time period marked by the load balancing marker is earlier than the start point of the spindle running cycle and whether the end point is later than the end point of the spindle running cycle, and generate a coverage status determination. S402: Based on the timestamp range of the thermal stability mark and the timestamp range of the load balancing mark, extract the generation time of the environmental compensation intensity command, calculate the absolute value of the difference between the generation time and the starting point of the first two marks, and generate the hysteresis deviation. S403: Based on the coverage status determination and the hysteresis deviation, call the single processing cycle value set by the processing table, determine whether the absolute value of the time coverage deviation and the hysteresis deviation in the coverage status determination exceed the cycle value, calculate the timestamp range quality assurance of all out-of-limit data segments, remove them, and generate data alignment marks.

9. The method for operation and management of an intelligent machining factory based on the Internet of Things according to claim 8, characterized in that, The formula for calculating the absolute value of time coverage deviation in coverage status determination is as follows: This represents the absolute value of the time coverage deviation in the coverage status determination. This represents the amount of lag bias. This represents a dynamic correction coefficient based on the historical lag mean. Represents the time coverage weighting factor. It represents the smallest positive constant that prevents the denominator from being zero.

10. The method for operation and management of an intelligent machining factory based on the Internet of Things according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Call the time range of the data alignment mark, extract the thermal deformation level value of the thermal stability mark, the current interval standard deviation exceeding the limit state of the load balancing mark, and the thin-walled component priority coefficient of the environmental compensation intensity command, and generate a task parameter set; S502: Based on the numerical values ​​of the thermal deformation levels in the task parameter set, sort the task numbers in descending order, adjust the sorting weights in combination with the priority coefficient of thin-walled parts, and generate a task sorting index. S503: Locate the task node requiring compensation in the task sorting index, call the start time and duration of the idle time window after the air pressure adjustment, insert the corresponding task's subsequent node gap, and generate the factory operation management plan.

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