Intelligent machine processing factory operation management method based on Internet of Things

Abstract

The invention relates to the technical field of intelligent factory management, in particular to an intelligent machine processing factory operation management method based on the Internet of Things, which comprises the following steps: acquiring main shaft operation time and temperature difference to generate a thermal stability mark, extracting a current interval standard deviation to generate a load balancing mark, and generating an environment compensation intensity instruction in combination with temperature, humidity and air pressure. And verifying data time consistency to generate a data alignment mark, and matching task priorities to generate a factory operation management scheme. According to the method, the load disturbance identification precision is improved through the current peak interval standard deviation, the environmental parameter expression ability is enhanced by combining temperature and humidity and air pressure composite indexes with the thin-wall part proportion, the multi-source data processing consistency is ensured through timestamp verification, and dynamic scheduling optimization is achieved based on multi-factor task rearrangement and matching of an air pressure adjustment window. The thermal stability, the load balance and the environmental adaptability in the machining process are improved, and the task execution stability and the precision control capability are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent factory management, and particularly relates to an intelligent machining factory operation management method based on Internet of Things. BACKGROUND

[0002] The technical field of intelligent factory management includes manufacturing system control and optimization technology combining industrial automation, informatization and intelligentization. The core content of this technical field is to realize real-time monitoring, coordination scheduling and optimization control of the whole production process of the factory through the integration of sensors, controllers, execution devices and network communication platforms, so as to realize efficient use of resources and improvement of operation management ability of the manufacturing system. This field usually covers production scheduling optimization, equipment state monitoring, energy consumption management, inventory control and personnel collaboration, etc. Through the construction of a multi-level information processing architecture, collaborative management from the workshop level to the enterprise level is realized, and complex information interaction and control logic design requirements are needed.

[0003] Among them, the intelligent machining factory operation management method based on Internet of Things refers to the matters related to production planning, equipment usage scheduling, processing task execution state monitoring, environmental data acquisition and analysis, operation safety control and energy consumption index acquisition in the machining workshop. The sensing and control system covering the processing flow is constructed by using Internet of Things sensing terminals and communication networks, and the factory operation is organized and managed through device data uploading, edge side data aggregation and platform side rule control mode. Its mode usually includes deploying embedded temperature and humidity collection devices to realize environmental monitoring, using processing equipment operation data collectors to monitor load and start-stop state, uploading to edge servers for real-time aggregation processing through wireless communication mode, and judging processing state and operation abnormality according to set rules. At the same time, according to the task priority rules set in advance, the dynamic allocation and adjustment of processing tasks are carried out.

[0004] The prior art has the problem of fragmented processing of multiple source parameters in process management. Data such as thermal state, current fluctuation, and environmental disturbance are often analyzed independently in the form of single factors, lacking a dynamic fusion mechanism based on task levels, and unable to construct the correlation path between complex variables, resulting in some state abnormalities not being identified or accurately traced in the early stage. In terms of load state identification, the processing method relies on motor start-stop or average current value, and fails to refine the time domain characteristics of cutting fluctuation behavior, resulting in rough evaluation of task execution stability and difficulty in reflecting real load dynamics. Environmental parameter collection is mostly used for independent monitoring or early warning triggering, and its results are not cross-analyzed with task structure, especially in sensitive processes such as thin-walled parts, the potential impact of environmental disturbance on machining precision lacks a systematic feedback mechanism, which easily causes inconsistent quality of batch products. In terms of coordinated sensing data, the data synchronization control mechanism lacks a complete integrity verification process, and there are problems such as non-overlapping time range and disorderly instruction response timing, affecting the accuracy and execution effectiveness of the scheduling strategy. The task scheduling method is based on fixed priority rules and lacks the ability to dynamically adjust the task structure based on device state and environment, limiting the response efficiency of task coordination and resource matching in complex production scenarios. SUMMARY

[0005] The purpose of the present application is to solve the problems existing in the prior art, and to propose an intelligent machine tool factory operation management method based on Internet of Things.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme: an intelligent machine tool factory operation management method based on Internet of Things, comprising the following steps: S1: Collecting the continuous running time stamp of the CNC main shaft, extracting the time length of the uninterrupted continuous machining section between the tool replacement operation points, obtaining the temperature difference in the time period, and generating a thermal stability mark by comparing the thermal deformation critical threshold in the equipment manual; S2: According to the time window of the thermal stability mark, collecting the spindle motor current fluctuation data, calculating the time interval standard deviation of the current peak value, and generating a load balance mark by comparing the interval fluctuation range under the rated power; S3: Calling the thermal stability mark time stamp, collecting the workshop temperature and humidity average, multiplying and subtracting the machining table air pressure value to generate an environmental compensation intensity instruction; S4: Receiving the start and end time of the thermal stability mark, verifying whether the current collection covers the main shaft cycle, detecting whether the compensation instruction is lagging, removing the data segment with deviation exceeding the machining beat, and generating a data alignment mark; S5: Based on the data alignment mark, inputting the thermal deformation level value of the thermal stability mark, calling whether the current interval standard deviation in the load balance mark is out of limit, matching the thin-walled part task compensation priority in the environmental compensation intensity instruction, inserting air pressure adjustment in the idle window, and generating a factory operation management scheme.

[0007] 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.

[0008] As a further aspect of the present invention, 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.

[0009] As a further aspect of the present invention, the formula for calculating the corrected duration of the processing segment 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.

[0010] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Collect spindle drive motor current waveform data based on the time window of the thermal stability mark, identify the current peak value point of the cutting feed and record the corresponding timestamp, and generate a current peak value sequence; S202: Call the time interval of adjacent peaks in the current peak value sequence, calculate the standard deviation of all interval values, eliminate the interference terms in the idle stage, and generate a peak interval fluctuation value; S203: Compare the peak interval fluctuation value 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 value exceeds the range, mark it as abnormal, and generate a load balancing mark.

[0011] As a further scheme of the present application, the standard deviation calculation formula of all interval values is specifically:

[0012] Among them, represents the standard deviation of all interval values, represents the time interval of the first group of adjacent current peak values, represents the arithmetic mean of all current peak time intervals, represents the current value of the first peak point in the first group, represents the current value of the second peak point in the first group, represents the average of the current value of the first peak point in all peak points, represents the average of the current value of the second peak point in all peak points, represents the number of available adjacent peak interval groups.

[0013] As a further scheme of the present application, the specific steps of S3 are: S301: Based on the timestamp range of the thermal stability mark, synchronize the temperature monitoring value and humidity monitoring value of the workshop temperature and humidity monitoring point in the time period, calculate the arithmetic mean of the two, and generate a temperature and humidity monitoring result; S302: Based on the product of the temperature mean and humidity mean in the temperature and humidity monitoring result, call the real-time air pressure monitoring value of the machining table, and subtract the air pressure monitoring value from the product result to generate an environment compensation base; S303: Based on the environment compensation base, obtain the number of thin-walled part processing orders in the current task queue and the total order quantity, calculate the proportion value of the two, multiply the environment compensation base by the proportion value, and generate an environment compensation intensity instruction.

[0014] As a further scheme of the present application, the specific steps of S4 are: S401: call the time stamp start and end of the thermal stability mark, obtain the start time and end time of the spindle running period, compare whether the current collection time range start point of the load balancing mark is earlier than the spindle running start point and the end point is later than the spindle running end point, and generate an overlap state judgment; S402: based on the time stamp range of the thermal stability mark and the time stamp range of the load balancing mark, extract the generation time of the environmental compensation intensity instruction, respectively calculate the absolute value of the difference between the generation time and the start point of the previous two marks, and generate a lag deviation amount; S403: based on the overlap state judgment and the lag deviation amount, call the single processing beat cycle value set in the processing table, judge whether the time overlap deviation absolute value in the overlap state judgment and the lag deviation amount exceed the beat cycle value, calculate the time stamp range quality protection of all over-limit data segments, and perform rejection, and generate a data alignment mark.

[0015] As a further scheme of the present application, the time overlap deviation absolute value calculation formula in the overlap state judgment is:

[0016] representing the time overlap deviation absolute value in the overlap state judgment, representing the lag deviation amount, representing a dynamic correction coefficient based on the historical lag deviation mean, representing a time overlap weight factor, representing a minimum positive constant to prevent the denominator from being zero.

[0017] As a further scheme of the present application, the specific steps of S5 are: 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 over-limit state of the load balancing mark, and the thin-walled part priority coefficient of the environmental compensation intensity instruction, and generate a task parameter set; S502: according to the thermal deformation level value in the task parameter set, perform descending arrangement on the task number, combine the thin-walled part priority coefficient to adjust the sorting weight, and generate a task sorting index; S503: locate the compensation task node in the task sorting index, call the idle time window start time and duration after air pressure adjustment, insert the post-node gap of the corresponding task, and generate a factory operation management scheme.

[0018] Compared with the prior art, the present application has the advantages and positive effects that: In the present application, the load disturbance recognition accuracy is improved by the standard deviation of current peak interval, the environmental parameter expression capability is enhanced by combining the temperature and humidity and air pressure composite index with the thin-walled part proportion, the timestamp verification ensures the consistency of multi-source data processing, the dynamic scheduling optimization is realized based on the multi-factor task rearrangement and the air pressure adjustment window, the processing process thermal stability, load balance and environmental adaptability are improved, and the stability and precision control capability of task execution are enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Figure 1 The step flowchart of the present application. DETAILED DESCRIPTION

[0021] The technical solutions in the present application will be described below in conjunction with the drawings.

[0022] In the embodiments of the present application, the words such as "example", "for example" are used to represent as an example, illustration or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0023] In the embodiments of the present application, "image" and "picture" can be used interchangeably at times. It should be pointed out that when the distinction is not emphasized, the meanings expressed are consistent. "Of", "corresponding" and "corresponding" can be used interchangeably at times. It should be pointed out that when the distinction is not emphasized, the meanings expressed are consistent.

[0024] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1. When the distinction is not emphasized, the meanings expressed are consistent.

[0025] In order to make the technical problems, technical solutions and advantages of the present application clearer, the following will be described in detail in conjunction with the drawings and specific embodiments.

[0026] Please refer to Figure 1 , the intelligent machine processing plant operation management method based on the Internet of Things, comprising the following steps: S1: Collect the CNC spindle continuous running timestamp, extract the time length of the uninterrupted continuous machining section between tool replacement operation points, obtain the temperature difference value of the front end temperature sensor and the tail end temperature sensor in the time period, divide the temperature difference value by the continuous machining section time to obtain the unit time temperature difference fluctuation rate, compare the spindle thermal deformation critical threshold table in the equipment manual, and generate a thermal stability mark; S2: According to 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 time interval standard deviation of the adjacent two cutting feed current peaks, compare the interval fluctuation range under the rated power of the motor, and generate a load balance mark; S3: Call the timestamp range of the thermal stability mark, synchronously obtain the average value of the workshop temperature and humidity monitoring points in the time period, multiply the temperature value by the humidity value, subtract the real-time air pressure monitoring value of the machining table, combine the proportion value of the number of thin-walled part machining orders in the current task queue and the total order quantity, and generate an environment compensation intensity instruction; S4: Receive the start and end points of the thermal stability mark, verify whether the current collection time period of the load balance mark completely covers the spindle running cycle, detect whether the generation time of the environment compensation intensity instruction lags behind the previous two marks, remove the data segment with a timestamp coverage deviation exceeding a single machining beat 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, call whether the current interval standard deviation in the load balance mark is out of limit, match the thin-walled part task compensation priority in the environment compensation intensity instruction, rearrange the task number according to the thermal deformation level, and insert the idle time window after the air pressure adjustment takes effect for the tasks that need to be compensated, and generate a factory operation management scheme.

[0027] The thermal stability mark includes the temperature difference value, the continuous machining time, the unit time temperature difference fluctuation rate, and the thermal deformation level. The load balance mark includes the current peak interval standard deviation, the fluctuation range comparison result, and the load stability level. The environment compensation intensity instruction includes the average temperature and humidity value, the air pressure correction parameter, the thin-walled part order proportion, and the compensation intensity level. The data alignment mark includes the current collection time coverage state, the instruction generation time sequence relationship, and the machining beat deviation removal identification. The factory operation management scheme includes the task number rearrangement, the compensation task identification, and the idle time window insertion strategy.

[0028] The specific steps of S1 are: S101: Collect the CNC spindle running timestamp, identify the tool replacement operation point, extract the start time and end time of the uninterrupted machining section between adjacent tool replacement points, calculate the corrected duration of the machining section, and generate the continuous machining time; The corrected duration of the machining section is calculated according to the following formula: ; wherein, represents the corrected duration of the th processing section, represents the end timestamp of the th processing section, represents the start timestamp of the th processing section, represents the number of spindle load fluctuation coefficients between the th adjacent timestamps, represents the total number of timestamp sampling points within the current processing section, represents the load compensation factor generated based on historical downtime data; Parameter assignment and calculation process: Basic time difference calculation: The start timestamp of the th processing section is 2025-04-21-08:15:30 (corresponding to the second-level timestamp 1713672930), and the end timestamp is 2025-04-21-08:45:30 (corresponding to the second-level timestamp 1713674730).

[0029] The initial time difference is 30 minutes (seconds).

[0030] The spindle load fluctuation coefficient : is the standard deviation of the spindle load fluctuation between the th adjacent timestamps, calculated by real-time monitoring of the spindle load current data.

[0031] Data source: sampling interval is 0.5 seconds, there are a total of 60 timestamps within the current processing section , the standard deviation of the load current between every two adjacent timestamps is calculated as follows: (unit: A), , , …, (the remaining values are calculated by actual monitoring data, and meet the standard deviation range of 0.5~2.0A in industrial scenarios).

[0032] Sum of squares calculation: .

[0033] The load compensation factor : Generated from historical downtime data, fitted according to the correlation between downtime interval and load fluctuation of the same model CNC machine tool in the past month, take (rational interval 0.1~0.3).

[0034] Correction term calculation: seconds.

[0035] Final duration calculation: seconds (about 29.9995 minutes).

[0036] Parameter setting according to data quantification: and Timestamp from CNC system log record, precision is second level; Through the spindle current sensor to collect the load current in real time, the standard deviation of current value between adjacent timestamps is used to quantify the fluctuation intensity; Determined by the timestamp sampling frequency (0.5 seconds) and the total duration of the processing section (30 minutes), but the actual example is simplified to To shorten the calculation period; Based on historical data statistics, set through linear regression analysis of downtime interval and load fluctuation correlation, the larger the value, the higher the impact weight of load fluctuation on time correction.

[0037] Numerical results and step correlation: Calculation results seconds for the corrected continuous processing time, indicating that the original time difference of 1800 seconds is reduced by 0.029 seconds due to spindle load fluctuation and compensation factor. This value is directly used as the input data of "generate continuous processing time" in step S101, and the correction term is used to eliminate the non-processing interruption time caused by load fluctuation, ensuring that the output time length only contains the effective processing period.

[0038] S102: Call the time period corresponding to the continuous processing time, synchronously collect the front end temperature sensor value and the tail end temperature sensor value, 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; The determined 742-second continuous processing time period is called as a temperature data acquisition reference interval, starting from the processing start time 08:03:21 and ending at 08:15:43. The real-time sampling values of the temperature sensors at the front end and the tail end of the main shaft are read at a frequency of once per second in this interval. One pair of temperature data is obtained every second, forming 742 pairs of temperature values. For example, at the start time, the front end temperature is 45.2°C and the tail end temperature is 44.1°C. The highest front end temperature in the 742-second interval is 48.6°C and the lowest is 44.8°C. The highest tail end temperature is 46.5°C and the lowest is 43.3°C. Then, in each second's sampling value, the difference between the front end and tail end temperatures is taken and the absolute value is taken. For example, at the 200th second, the front end temperature is 47.1°C and the tail end temperature is 45.3°C, so the difference is 1.8°C. A temperature difference sequence of 742 data is generated, covering the entire processing segment. This temperature difference sequence is the original input data for subsequent thermal stability calculation. All data are kept to one decimal place to ensure that the details of the temperature difference change are completely retained for further analysis.

[0039] 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 interval in the main shaft thermal deformation critical threshold table in the equipment manual to determine whether the current temperature difference change rate exceeds the upper limit or lower limit of the corresponding interval, and generate a thermal stability marker; The 742 temperature difference data generated above are summed up in sequence. Suppose the total temperature difference sum is 1038.3°C. Then, divide this value by the time length of the corresponding processing segment, 742 seconds, to obtain the temperature difference change rate per unit time, which is 1.4°C / s. This result needs to be compared and judged with the main shaft thermal deformation critical threshold interval listed in the equipment manual. The main shaft equipment model is H75. According to the historical test data and stable operation sample statistics of this model, three temperature difference change rate reference intervals are obtained. The stable interval is set to 0.3°C / s to 1.1°C / s. This interval is based on the data statistics of 30 main shafts running for 120 minutes in a stable temperature control environment. Below 0.3°C / s is the insufficient thermal drift warning zone, and above 1.1°C / s is the severe thermal drift abnormal zone. The current processing segment change rate is 1.4°C / s, which obviously exceeds the upper limit value. Therefore, when performing the judgment action, 1.4 is compared with 1.1. The judgment result is that the upper limit is exceeded, i.e., the thermal stability marker is "0", indicating that the temperature difference fluctuation rate of this processing segment is in an abnormal state, and is recorded in the thermal stability data field.

[0040] The specific steps of S2 are: S201: Based on the time window of the thermal stability marker, collect the main shaft drive motor current waveform data, identify the current peak points of the cutting feed and record the corresponding time stamps, and generate a current peak sequence. Based on the time window of thermal stability marker, firstly, the start and end time points of the processing section marked as thermally unstable in the main shaft thermal stability analysis module are called as the data extraction interval, and in this interval, the current value of the current under the current running state is collected once a second by relying on the real-time collection module of the main shaft driving motor current of the equipment control system, the complete current waveform data is obtained through continuous collection, then the peak points in the current waveform are scanned item by item, the peak points are identified by comparing the change trend of the current values at continuous multiple time points, the point value and the corresponding time stamp are recorded, for example, at 08:05:12, the current value rises from 8.1A to 8.6A and then falls to 8.2A, so 08:05:12 is confirmed as a current peak point, and in this way, all data points within 742 seconds are traversed, and finally, 47 current peak points are identified, forming a current peak sequence, which contains the current intensity and occurrence time information of each peak value, and the maximum peak value is 9.2A and the minimum peak value is 6.4A, and the time stamps of all peak points are arranged in sequence to form the current peak sequence of the complete processing section.

[0041] S202: The time interval of adjacent peak values in the current peak sequence is called, the standard deviation of all interval values is calculated, the idle stage interference items are removed, and the peak interval fluctuation is generated. The standard deviation calculation formula of all interval values is as follows:

[0042] Among them, represents the standard deviation of all interval values, represents the time interval of the first group of adjacent current peak values, represents the arithmetic mean of all current peak time intervals, represents the current value of the first peak point in the first group, represents the current value of the second peak point in the first group, represents the average value of the current value of the previous point in all peak points, represents the average value of the current value of the next point in all peak points, represents the average value of the current value of the previous point in all peak points, represents the average value of the current value of the next point in all peak points, represents the number of available adjacent peak interval groups.

[0043] The time interval data of adjacent peak values in the current peak sequence is collected, the sampling frequency is set to 1000Hz, the sampling time is 10 seconds, and 10 adjacent peak intervals (unit: milliseconds) are obtained: ; The average value of all time intervals is calculated: Collect the current value (unit: ampere) of each group of adjacent peak points: Calculate the average value of all previous peak point current values: Calculate the average value of all next peak point current values: Calculate the weighted difference value of each group of data and sum them up: Sum the above results: Calculate the standard deviation: This calculation result shows that by measuring the actual current peak value and time interval, the standard deviation fluctuation of the time interval can be accurately calculated, which can effectively reflect the fluctuation characteristics and abnormal state of the current peak value sequence, and be used for subsequent current stability analysis and quality control.

[0044] 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; Take the peak interval fluctuation of 3.8 seconds as the input value, compare it with the standard fluctuation range under the rated power condition of the corresponding main shaft driving motor in the equipment manual, and judge. The current motor model is EM92, and the rated power is 15kW. According to the statistical data of 500 hours of stable operation samples in the equipment manual, the qualified range of fluctuation is 1.2 seconds to 2.9 seconds. This range is obtained by selecting the upper and lower 5% confidence intervals after fitting the stability measurement data of current peak interval under different loads. The specific upper and lower limit values are the 25th and 475th sorted values of historical fluctuation. The current calculated fluctuation of 3.8 seconds is greater than the upper limit of 2.9 seconds. When performing the judgment action, directly compare 3.8 and 2.9. 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 satisfies the over-limit condition, and the load balancing mark is "0", indicating that the motor load fluctuation in the current processing process has exceeded the reasonable interval.

[0045] The specific steps of S3 are: S301: Based on the timestamp range of thermal stability mark, synchronously call the temperature monitoring value and humidity monitoring value of workshop temperature and humidity monitoring points in the time period, calculate the arithmetic mean of the two, and generate the temperature and humidity monitoring result; ​​​​​​​Based on the time stamp range of the thermal stability mark, the processing segment starting and ending time points with the thermal stability mark of "0" are called first, and the temperature and humidity monitoring values of the environmental monitoring nodes installed in the workshop are synchronously extracted within the time interval. For example, 13 sets of temperature and humidity data are collected every minute during 08:03-08:15, the temperature data are 26.3℃, 26.5℃, 26.7℃, 27.0℃, 27.1℃, 27.3℃, 27.2℃, 27.0℃, 26.9℃, 26.8℃, 26.6℃, 26.5℃, 26.4℃, and the humidity data are 51.0%, 50.8%, 50.6%, 50.5%, 50.4%, 50.2%, 50.3%, 50.4%, 50.5%, 50.6%, 50.8%, 51.0%, 51.1%, respectively. Then the arithmetic mean of the above two groups of data is calculated, the temperature mean is (26.3+26.5+…+26.4) ÷13=26.82℃, and the humidity mean is (51.0+50.8+…+51.1) ÷13=50.59%, which are recorded as the corresponding environmental temperature and humidity monitoring results of the processing segment, as the basic input data of the subsequent environmental compensation factor.

[0046] S302: Based on the multiplication of the temperature mean and the humidity mean in the temperature and humidity monitoring results, the real-time air pressure monitoring value of the processing table is called, and the product result is subtracted from the air pressure monitoring value to generate the environmental compensation base; The temperature mean in the temperature and humidity monitoring results is 26.82℃, and the humidity mean is 50.59%. First, the multiplication of the two is calculated to get the product value of 1357.35 (unit: ℃·%). Then the corresponding processing table air pressure monitoring value is extracted within the thermal stability mark interval, and 13 sets of air pressure data are obtained within the same time range, with the values being 101.2, 101.1, 101.1, 101.0, 100.9, 100.8, 100.8, 100.9, 101.0, 101.1, 101.2, 101.2, 101.3, and the arithmetic mean is 101.06 hPa. Then the product of temperature and humidity 1357.35 is subtracted from the air pressure mean 101.06, and the result is 1256.29, which is used to reflect the influence of the comprehensive fluctuation of environmental temperature and humidity and air pressure in the processing segment on the equipment running state, and is recorded as the environmental influence reference benchmark of the current processing segment.

[0047] S303: Based on the environmental compensation base, the number of thin-walled part processing orders in the current task queue and the total order quantity are obtained, the proportion value of the two is calculated, and the environmental compensation base is multiplied by the proportion value to generate the environmental compensation intensity instruction; Based on the generated environmental compensation base 1256.29, the current task queue operation stage is entered to obtain the structure type and quantity information of all machining tasks in the current CNC scheduling queue. The number of thin-walled part machining orders is 18, and the total number of orders is 45. First, the ratio of the two is calculated as 18÷45=0.4. Then, the environmental compensation base 1256.29 is multiplied by the ratio value 0.4, and the action is 1256.29×0.4=502.52. The result 502.52 is recorded as the environmental compensation intensity instruction corresponding to the thin-walled part machining task under the current environmental condition. This instruction value will be used to adjust the related dynamic compensation parameters in the subsequent processing.

[0048] The specific steps of S4 are: S401: Call the time stamp start and end of the thermal stability mark to obtain the start and end times of the spindle running cycle. Compare whether the current collection time range of the load balancing mark is earlier than the start point of the spindle running and whether the end point is later than the end point of the spindle running to generate an overlap state judgment. Call the time stamp start and end of the thermal stability mark. First, extract 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 of this processing section. Then, call the time range of the current collection current waveform data in the load balancing mark process. The start time of this data is 08:03:17, and the end time is 08:15:48. Perform a comparison operation by comparing the start and end times in sequence. 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, determine 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. Both comparison judgments are true. Therefore, it is confirmed that the current collection time range completely covers the spindle running cycle range. Record this result as "complete coverage". If any time item in the judgment process does not meet the corresponding sequence relationship, record it as "insufficient coverage" or "exceeding". The generated coverage state judgment in the current example is "complete coverage".

[0049] S402: Based on the time stamp range of the thermal stability mark and the time stamp range of the load balancing mark, extract the generation time of the environmental compensation intensity instruction, and calculate the absolute value of the difference between the generation time and the start time of the previous two marks to generate a lag bias. Based on the time stamp range of thermal stability mark 08:03:21 to 08:15:43 and the time stamp range of load balancing mark 08:03:17 to 08:15:48, the generation time 08:03:55 of the environmental compensation intensity instruction is extracted, and the absolute value of the time difference is calculated with the thermal stability starting point and the load balancing starting point respectively. First, 08:03:55 is subtracted from 08:03:21, the difference is 34 seconds, and then 08:03:55 is subtracted from 08:03:17, the difference is 38 seconds. The absolute values of the two differences are recorded as 34 seconds and 38 seconds respectively. Then it is confirmed in this step that the lag bias is defined as the larger value of the above two differences, so the lag bias in this example is 38 seconds, indicating that the compensation instruction generation time is delayed by 38 seconds from the load data starting time. This value will be used as a reference basis for subsequent data alignment judgment.

[0050] S403: Based on the coverage state determination and the lag bias, the single processing cycle value of the processing station setting is called to calculate the time coverage bias absolute value in the coverage state determination, and whether the time coverage bias absolute value in the coverage state determination and the lag bias exceed the cycle value is judged. The time stamp range of all out-of-limit data segments is analyzed and removed to generate a data alignment mark. The time coverage bias absolute value calculation formula in the coverage state determination is as follows: ; Among them, represents the time coverage bias absolute value in the coverage state determination, represents the current system time stamp, represents the coverage state determination trigger time stamp, represents the lag bias, represents the current single processing cycle value of the processing station setting, represents the dynamic correction coefficient based on the historical lag bias mean, represents the time coverage weight factor, represents the minimum positive constant to prevent the denominator from being zero; The current system time stamp , the coverage state determination trigger time stamp , and the time difference absolute value is calculated as The lag bias is calculated by taking the sliding average of the last three lag time data collected by the real-time monitoring sensor of the processing station after data filtering processing. The processing cycle value is directly read from the preset parameter in the processing station control system. The dynamic correction coefficient is calculated by the mean of the lag bias in the historical processing cycle, and the formula is , substitute , get . Time coverage weight factor , according to the system design requirements of time coverage and the balance weight proportion of the beat cycle. Constant , is a fixed minimum value to prevent the denominator from being zero. Substitute the formula to calculate: ; determine and whether it exceeds , because both are not over limit, the current data segment does not need to be rejected. The result indicates that the absolute value of the time coverage deviation after correction is still within the allowed range of the beat cycle, the data segment passes the verification and generates an alignment marker.

[0051] Parameter values are obtained according to: and through system clock synchronization; filtered by the sensor monitoring lag time; is a preset parameter; adjusted dynamically by the historical lag deviation mean; according to the system design weight; is an engineering constant. Parameter range verification: in the industrial processing scene, the reasonable interval is , the reasonable interval is , usually , all meet the actual setting.

[0052] The specific steps of S5 are: S501: Call the time range of the data alignment marker, extract the thermal deformation level value of the thermal stability marker, the current interval standard deviation over-limit state of the load balancing marker, and the thin-walled part priority coefficient of the environmental compensation intensity instruction, and generate a task parameter set; The time range of calling data alignment mark is 08:03:21-08:15:43, three generated mark parameters in the corresponding time range are extracted in turn, first, the corresponding machining section spindle temperature difference rate in the thermal stability mark is 1.4°C / s, the corresponding thermal deformation level is set to level 3 (in the 0-3 level standard, 0 represents stable, and 3 represents serious deformation), then the load balancing mark is called, the standard deviation of current peak interval of this section is 3.8 seconds, and the qualified standard set by the system is 2.9 seconds at most, so it is judged as “over limit” state, and the state value is recorded as 1, then the environmental compensation intensity instruction generated in this machining section is extracted from the environmental compensation module, which is 502.52, and combined with the task structure information, the machining task corresponding to this section is a thin-walled part, and the compensation priority coefficient of the thin-walled part is set to 0.8 in the system, which is defined through the task attribute manual, and the coefficient range of the structure task with a plate thickness less than 3mm and sensitive to thermal and load response is 0.7-1.0, according to the thin-walled property of the current task parameter and the previous vibration test record, the coefficient is determined as 0.8, finally, the three parameters are uniformly collected with task number as index to form a task parameter set composed of task number, thermal deformation level, load state and compensation coefficient, for example: task number #T023, thermal level: 3, load state: 1, priority coefficient: 0.8.

[0053] S502: According to the size of the thermal deformation level value in the task parameter set, the task number is sorted in descending order, and the sorting weight is adjusted combined with the priority coefficient of the thin-walled part to generate a task sorting index; According to the size of the thermal deformation level of each task in the task parameter set, the sorting is performed, and the action is to arrange the thermal level field in all task records in descending order, if there are multiple tasks with the same thermal level, the secondary sorting is performed according to the priority coefficient, the specific processing mode is: the thermal level value of each task is multiplied by the coefficient value to generate a weight score, for example: task A thermal level 3, coefficient 0.8, score 2.4; task B thermal level 3, coefficient 0.6, score 1.8, then task A ranks first, if the thermal levels are different, the order is directly determined according to the level, after sorting, the index list is generated in turn, for example, the sorting result is T023, T019, T008, T011 in turn, then the task sorting index is generated as #T023→#T019→#T008→#T011, which corresponds to the task node order in the scheduling list, and is used as the basis for subsequent compensation insertion and job scheduling.

[0054] S503: Locate the task node that needs compensation in the task sorting index, call the idle time window start time and duration after air pressure adjustment, insert the post-node gap of the corresponding task, and generate a factory operation management scheme; Locate the task node that needs to be compensated in the task scheduling index, for example, the task number T023, which corresponds to the compensation intensity instruction value 502.52 and has exceeded the system set basic influence value threshold 400 (which is set according to the process evaluation group and is defined as the critical point at which the structure does not need to be adjusted under normal circumstances), and mark the task as needing compensation, then call the available idle time window information after the air pressure adjustment in the processing table real-time scheduling system, the processing table 08:18:00-08:23:00 is the current idle segment, the duration is 5 minutes, judge the action as: the original scheduling end time of the task T023 that needs to be compensated is 08:15:43, and the subsequent node T024 scheduling start time is 08:25:00, there is a 7 minute and 17 second gap in between, therefore, it meets the insertable condition, insert the execution node after compensation at 08:18:00, update the T024 start time to 08:23:00, and the new scheduling record formed after the execution node order adjustment is T023 compensation segment→T024, finally generate the adjusted factory operation management scheme, record the new scheduling time sequence and compensation operation identification.

[0055] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection 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 hysteresis 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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