An intelligent management method for the production of high-speed rotating transmission devices

Abstract

The present invention relates to the field of intelligent management technology, specifically to an intelligent management method for the production of high-speed rotating transmission devices, comprising the following steps: extracting operation data to calculate deviations and mark abnormalities, adjusting load generation configuration in combination with task information, judging beat overlap and splitting priority recording paths, screening equipment to generate control sequences, and extracting temperature and pressure trends that exceed limits and reset beat generation schemes. In the present invention, by combining task numbers and operation status to build a periodic alternation model to dynamically generate load adjustment methods, task adaptability is improved, and a continuous scheduling channel is constructed using output time period difference judgment and beat conflict splitting to improve task connection smoothness. Equipment number deduplication and conflict weighting sorting optimize resource allocation structure, enhance control efficiency, and achieve real-time prediction and cooling adjustment based on temperature and pressure change trends, strengthen operational safety, and overall improve the accuracy, responsiveness, and stability of intelligent management and control.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management technology, and in particular to an intelligent management method for the production of high-speed rotating transmission devices. Background Art

[0002] The field of intelligent management technology encompasses automated control and optimization management systems used in manufacturing and industrial sectors. The core of this technology lies in the intelligent monitoring, management, and optimization of production processes through the use of modern information technology, data acquisition, and analysis methods. Intelligent management systems typically include real-time data acquisition, condition monitoring, production scheduling, fault prediction, process optimization, and decision support. This field is widely used in industrial production, equipment maintenance, logistics management, and other areas, aiming to improve production efficiency, reduce human error, and ensure system stability and efficiency.

[0003] Among them, the intelligent management method for the production of high-speed rotating transmission devices refers to an intelligent management method for the production process of high-speed rotating transmission devices. This method collects data during the operation of the device, conducts real-time monitoring and analysis, and then optimizes the operating efficiency of the transmission device. In this patent, transmission device status monitoring, real-time data analysis, and adaptive adjustment technology are mainly used to obtain and analyze various parameters in the rotating transmission system in real time, and then perform regulation. These technical means involve dynamic monitoring and data acquisition of the transmission device, use information processing methods and analysis models to evaluate the operating status of the device, and make reasonable adjustments based on the evaluation results to ensure that the transmission device operates in an efficient and safe state.

[0004] Existing technologies for transmission status management primarily focus on the acquisition and evaluation of static indicators, lacking a proactive identification mechanism based on fluctuation trends. This results in abnormal components being identified only after significant operational deviations, leading to delayed response times. Task load regulation relies on static load models and lacks correlation analysis at the cycle-alternation level. This makes it difficult to accurately match the varying load characteristics of different tasks during execution, leading to ineffective or over-adjusted regulation. The scheduling process lacks structured differential analysis between the time periods of upstream and downstream tasks, making it difficult to effectively identify and resolve inter-task timing conflicts, resulting in scheduling interruptions and equipment waiting. Equipment resource allocation typically utilizes a unified priority mapping rule, failing to consider the number of path conflicts and the frequency of equipment crossovers during concurrent execution. Tasks are prone to path conflicts and overlap during peak execution periods, leading to frequent resource contention. While temperature and pressure data are collected during high-frequency operation, continuous trend analysis and proactive intervention logic are not implemented. Threshold-based triggering strategies alone are unable to address dynamic operational changes, resulting in unresponsive regulation strategies and increased equipment wear and tear and safety risks. For example, during the switching of continuous processes, the lack of coordinated response to load changes and cooling status can easily cause equipment thermal fatigue or operational interruptions. In summary, existing technologies have significant deficiencies in the timeliness, pertinence, and synergy of abnormal response, task matching, scheduling coordination, and status adjustment. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent management method for the production of high-speed rotating transmission devices.

[0006] In order to achieve the above-mentioned object, the present invention adopts the following technical solution: an intelligent management method for the production of high-speed rotating transmission devices, comprising the following steps:

[0007] S1: Obtain operating data for motors, gears, and bearings in high-speed rotating transmissions, extract frequency, power consumption, and speed per unit time, calculate fluctuation deviations, mark components that exceed the fluctuation range as abnormal nodes, and generate a list of abnormal components to be managed.

[0008] S2: extracting the cycle frequency and load of the motor's current task based on the component operating status and task number in the list of abnormal components to be managed, calculating the load adjustment method and generating a task control state configuration group;

[0009] S3: Calculate the difference between the output time periods of the upstream and downstream task channels through the task control state configuration group to determine whether there is beat overlap. If there is overlap, split the task priorities and record the paths to generate a dispatch path list.

[0010] S4: Filter concurrent task execution segments according to the dispatch path list, remove duplicate device numbers in cross-running segments, sort by the number of path conflicts to generate a dispatch priority, and output a path priority table;

[0011] S5: Extract the path priority table and determine whether it exceeds the defined threshold value. If it exceeds the threshold, call the control parameters of the equipment cooling component and reset the beat to generate an intelligent management plan.

[0012] As a further solution of the present invention, the list of abnormal components to be managed includes the motor operating status, gear operating status, bearing operating status, abnormal node number, fluctuation deviation value, standard operating upper and lower limits, unit time frequency, power consumption, and speed; the task control status configuration group includes motor cycle frequency, operating load, load adjustment method, task node interruption option, frequency and load adjustment combination item, and adjustment method type; the allocation path list includes task segment priority splitting results, channel path information, beat change data, upstream and downstream task channel output time periods, and beat overlap conflict judgment results; the path priority sorting table includes task number, equipment number, cross-operation time period equipment number, allocation index, path conflict number, path operation weight value, control order, and number mapping structure; the intelligent management solution includes continuous temperature change, operating pressure change trend, threshold group definition boundary, cooling component control parameters, and beat reset operation.

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

[0014] S101: Acquire the operating data of the motor, gears, and bearings, extract the frequency, power consumption, and speed parameter values, calculate the frequency fluctuation value per unit time, analyze the power consumption change rate and speed deviation, and generate the operating characteristic fluctuation value;

[0015] S102: Based on the operating characteristic fluctuation, the frequency fluctuation upper and lower limit interval values, the power consumption change rate limit value, and the speed deviation tolerance value are called, and the fluctuation value of the component is compared with the corresponding reference value in sequence to determine whether it exceeds the interval range. The component number that does not meet the reference is marked as an abnormal node, and a set of abnormal node numbers is obtained;

[0016] S103: Based on the abnormal node number set, extract the frequency, power consumption, and speed operating status parameters of the corresponding numbered node in the current time period, combine the number and status to construct a structure, arrange the structure data in the order of the number, and generate a list of abnormal components to be managed.

[0017] As a further solution of the present invention, the specific steps of S2 are:

[0018] S201: extracting the cycle frequency and operating load of the motor in the time period corresponding to the current task number based on the operating status and task number of the components in the list of abnormal components to be managed, calculating the frequency-load alternation degree index of the cycle segment based on the pairing value of the task execution cycle and the load peak interval, and generating a frequency-load alternation intensity value;

[0019] S202: Based on the frequency-load alternation intensity value, call the task node interruption attribute identifier corresponding to the task number, filter the frequency-load alternation intensity value, determine whether the task node has an interruption attribute, and set the load adjustment mode to switching type or slow-release type based on the interruption attribute, and establish an adjustment mode type identifier group;

[0020] S203: Based on the adjustment mode type identification group, the frequency value, the load value and the corresponding adjustment mode type identification under the task node are combined to form a ternary combination item in the order of the task numbers, and the missing item combinations are filtered out to generate a task control state configuration group.

[0021] As a further solution of the present invention, the frequency load alternation degree index calculation formula of the period segment is specifically:

[0022] ;

[0023] in, Represents the frequency load alternation degree index of the cycle segment, Representative The frequency value of the period segment, Representative The frequency value of the period segment, Representative The load value of a cycle segment, Representative The load value of a cycle segment, Representative The duration of a period, Representative The duration of the period, Represents the total number of cycle segments.

[0024] As a further solution of the present invention, the specific steps of S3 are:

[0025] S301: Calling the beat change data of the adjustment combination item in the task control state configuration group, performing synchronous comparison with the output time periods set in the corresponding upstream and downstream task channels, calculating the time period data difference based on the time nodes of the task segments as the benchmark, extracting all task segments with time differences less than or equal to zero, and obtaining the beat overlap conflict comparison value;

[0026] S302: Based on the beat overlap conflict comparison value, a priority splitting operation is performed on the task segments with time overlap, priority parameters defined in the task number sequence are called, and the execution order of the channel nodes with the same task path identifier is readjusted according to the priority sorting result. At the same time, the adjusted task number and channel path identifier combination is recorded to obtain a task segment channel path information set;

[0027] S303: Based on the task segment channel path information set, filter all the reachable paths in the task number and channel path combination, remove the path branches including the conflict identifier, retain the execution channel combination items without beat conflict, and establish a dispatch path list.

[0028] As a further solution of the present invention, the time period data difference calculation formula is specifically:

[0029] ;

[0030] in, Represents the data difference of the time period, Representative The end time of the output time period of each task channel, Representative The time node of each task segment, Represents the total number of cycle segments.

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

[0032] S401: Based on the correspondence between task numbers and equipment numbers in the dispatch path list, concurrent tasks in the same time period in the current batch are screened, all equipment numbers in the cross-running time period are extracted, duplicate numbers are removed, and the equipment numbers are reorganized according to the task numbers. A bidirectional mapping index structure between equipment numbers and task numbers is constructed to generate an equipment scheduling index value.

[0033] S402: Based on the device scheduling index value, count the number of repetitions of the cross-running device number under each path, aggregate them by path number, use the number of device number repetitions as the number of conflicts within the path, sort the path numbers from high to low by the number of conflicts, and generate a path operation weight value;

[0034] S403: According to the path operation weight value, combined with the index mapping relationship between the device number and the task number, the control items in the path number sequence are extracted and output in sequence, a sequential combination mapping between the task number and the path number is established, and a path priority table is generated.

[0035] As a further solution of the present invention, the specific steps of S5 are:

[0036] S501: Based on the task number currently ranked first in the path priority table, the bound device number is extracted, and the temperature change and pressure change trend data continuously recorded by the sensor attached to the device number within the current operating cycle are retrieved. Based on the temperature change slope and the pressure change difference, a corresponding numerical interval index is calculated, and the corresponding numerical interval index is compared with the upper and lower boundaries of the temperature threshold value and pressure threshold value bound to the device to generate a state threshold offset value.

[0037] S502: Based on the state threshold offset value, the task numbers in which any temperature or pressure parameter exceeds the corresponding threshold boundary are screened, the current control parameter set of the cooling component attached to the device corresponding to the task number is extracted, the cooling frequency value, cooling duration, and pressure response time recorded in the current operation cycle are called, a beat correction combination item is constructed, and a beat overlap control amount is generated;

[0038] S503: Based on the beat overlap control amount, the corresponding equipment number, cooling component parameters and beat control amount are combined to construct a structure to form mapping data of task number and reset parameter, and the mapping structure is output according to the task number sequence to generate an intelligent management solution.

[0039] As a further solution of the present invention, the corresponding numerical interval index calculation formula is specifically:

[0040] ;

[0041] in, Represents the corresponding numerical interval indicator, Representative Temperature value for each time period, Representative Temperature value for each time period, Representative The pressure value of each time period, Representative The pressure value of each time period, Representative The length of time for temperature change in each time period, Representative The length of time the pressure changes in each time period, Represents the number of temperature and pressure data points.

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

[0043] In the present invention, a load adjustment method is dynamically generated by combining the task number and the operating status to build a periodic alternation model, thereby improving task adaptability. The output time period difference judgment and beat conflict splitting are used to build a continuous scheduling channel to improve the smoothness of task connection. The equipment number deduplication and conflict weighting sorting are used to optimize the resource allocation structure, enhance the control efficiency, realize real-time prediction and cooling adjustment based on the temperature and pressure change trends, strengthen the operation safety guarantee, and improve the accuracy, responsiveness and stability of intelligent management and control as a whole. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0046] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0047] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0048] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0049] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0050] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0051] See also Figure 1, an intelligent management method for the production of high-speed rotating transmission devices, comprising the following steps:

[0052] S1: Obtain operating data for motors, gears, and bearings in high-speed rotating transmission devices, extract frequency, power consumption, and speed per unit time, and calculate fluctuation deviations. Compare these deviations with upper and lower limits of standard operation, mark components that exceed the fluctuation range as abnormal nodes, combine the abnormal node number with the operating status to form a structure, and generate a list of abnormal components to be managed.

[0053] S2: Based on the component operating status and task number in the list of abnormal components to be managed, the motor's cycle frequency and operating load under the current task are extracted. The load adjustment method is calculated based on the periodic alternation relationship between frequency and load. The adjustment method type is selected based on whether the task node is interruptible. A frequency and load adjustment combination item is established to generate a task control state configuration group.

[0054] S3: Call the beat change data of the adjustment combination item in the task control status configuration group, calculate the difference between the output time periods corresponding to the upstream and downstream task channels, and use the result to determine whether beat overlap conflicts occur. If overlap occurs, the task segments are prioritized and the channel path information is recorded to generate a dispatch path list.

[0055] S4: Based on the correspondence between task numbers and device numbers in the dispatch path list, the concurrent task execution segments in the current batch are screened, the device numbers in the cross-running segments are deduplicated and an allocation index is constructed. Path operation weight values are generated by sorting by the number of path conflicts. The control order is output based on the number mapping structure to generate a path priority table.

[0056] S5: Based on the device bound to the current priority task number in the path priority table, extract the continuous temperature change and operating pressure change trend collected by the sensor to determine whether they exceed the threshold group definition boundary. If so, call the current control parameters of the cooling component mounted on the device and perform a beat reset operation to generate an intelligent management plan.

[0057] The list of abnormal components to be managed includes the motor operating status, gear operating status, bearing operating status, abnormal node number, fluctuation deviation value, standard operating upper and lower limits, unit time frequency, power consumption, and speed. The task control status configuration group includes motor cycle frequency, operating load, load adjustment method, task node interruption option, frequency and load adjustment combination item, and adjustment method type. The allocation path list includes task segment priority splitting results, channel path information, beat change data, upstream and downstream task channel output time periods, and beat overlap conflict judgment results. The path priority sorting table includes task number, equipment number, cross-operation time period equipment number, allocation index, path conflict number, path operation weight value, control order, and number mapping structure. The intelligent management solution includes continuous temperature change, operating pressure change trend, threshold group definition boundary, cooling component control parameters, and beat reset operation.

[0058] The specific steps of S1 are:

[0059] S101: Acquire the operating data of the motor, gears, and bearings, extract the frequency, power consumption, and speed parameter values, calculate the frequency fluctuation value per unit time, analyze the power consumption change rate and speed deviation, and generate the operating characteristic fluctuation value;

[0060] First, key parameters such as frequency, power consumption, and speed are monitored and recorded in real time using device sensors. The motor's operating frequency is typically measured in Hertz (Hz) and is collected regularly by a frequency sensor. Power consumption data is typically obtained using current and voltage sensors, while speed data is obtained using a tachometer, typically measured in revolutions per minute (RPM). For example, a motor might have a frequency of 50 Hz, power consumption of 120 W, and a speed of 1500 RPM. Next, based on this data, the frequency fluctuation per unit time is calculated. To determine the fluctuation, the maximum and minimum frequency values within a given time period are calculated. For example, if the motor frequency varies from 45 Hz to 55 Hz during the sampling period, the fluctuation is 10 Hz. The power consumption change rate is calculated using the ratio method: the change in power consumption over a given time period is divided by the initial power consumption. For example, if power consumption increases from 120 W to 130 W, the change rate is (130 W - 120 W) / 120 W = 8.33%. Speed deviation is calculated by comparing it with a set standard speed value (e.g., 1500 RPM). If the speed deviates by 10 RPM from the standard, the deviation is calculated as 10 RPM. Through these steps, the extracted frequency fluctuation, power consumption change rate, and speed deviation can be further used to generate operating characteristic fluctuations, providing a basis for further anomaly detection.

[0061] S102: Based on the operating characteristic fluctuation, the frequency fluctuation upper and lower limit interval values, the power consumption change rate limit value, and the speed deviation tolerance value are called. The fluctuation value of the component is compared with the corresponding reference value in sequence to determine whether it exceeds the interval range. The component number that does not meet the reference is marked as an abnormal node, and the abnormal node number set is obtained;

[0062] First, the upper and lower limits for frequency fluctuation, the power consumption change rate threshold, and the speed deviation tolerance must be set based on the device type and actual operating conditions. The frequency fluctuation limit is typically set to ±5 Hz, the power consumption change rate to ±10%, and the speed deviation to ±50 RPM. For example, if the device's frequency fluctuation range is 45 to 55 Hz, any fluctuation outside this range is considered abnormal. A frequency fluctuation of 12 Hz exceeds the predefined range. A power consumption change rate exceeding ±10% (e.g., a 12% change rate) is considered abnormal. A speed deviation of 1500 RPM ±50 RPM (i.e., a deviation of more than 200 RPM) is also considered abnormal. These data are compared with preset baseline values to determine whether they exceed the set range. If so, the node is marked as abnormal. This allows for rapid location of the problematic device component and generates a set of abnormal node numbers for subsequent processing.

[0063] S103: Based on the abnormal node number set, extract the frequency, power consumption, and speed operating status parameters of the corresponding numbered node in the current period, combine the number and status to construct a structure, arrange the structure data in order of number, and generate a list of abnormal components to be managed;

[0064] The frequency, power consumption, and speed status parameters of the corresponding components in the current time period are further extracted. These data are integrated and paired with the corresponding component numbers to generate structured data. Each structure includes a component number and corresponding operating status parameters. For example, the motor with component number 001 has a frequency of 53Hz, a power consumption of 128W, and a speed of 1520RPM. These data will be arranged in order of component numbers and form a list of abnormal components to be managed. Through the organization of the structure, the specific operating status of each component in the current time period can be clearly identified, helping subsequent maintenance personnel to quickly and accurately identify and handle abnormal components, thereby optimizing the operation and maintenance strategy of the equipment.

[0065] The specific steps of S2 are:

[0066] S201: Based on the operating status and task number of the components in the list of abnormal components to be managed, the cycle frequency and operating load of the motor in the time period corresponding to the current task number are extracted. Based on the pairing value of the task execution cycle and the load peak interval, the frequency-load alternation degree index of the cycle segment is calculated to generate a frequency-load alternation intensity value;

[0067] The calculation formula for the frequency load alternation degree index of the period segment is as follows:

[0068] ;

[0069] in, Represents the frequency load alternation degree index of the cycle segment, Representative The frequency value of the period segment, Representative The frequency value of the period segment, Representative The load value of a cycle segment, Representative The load value of a cycle segment, Representative The duration of the period, Representative The duration of the period, Represents the total number of cycle segments;

[0070] This formula is used to calculate the frequency-load alternation index of the cycle segment. Its main purpose is to measure the alternating fluctuation of frequency and load during the operation of the motor. The acquisition and quantification methods of each parameter are as follows:

[0071] Representative The frequency value of a cycle segment is collected in real time by the frequency sensor during the operation of the motor. For example, the frequency of a device is 50Hz in a certain cycle segment, and the monitoring device provides this value through the frequency sensor. Hz.

[0072] Representative The frequency value of each cycle segment is determined by the frequency data collected by the frequency sensor in the previous cycle segment. cycle segments, the frequency is 48Hz, so Hz.

[0073] Representative The load value of each cycle segment is measured in real time by the current or power sensor. The load of each cycle segment is 70%, which is obtained through the power sensor.

[0074] Representative The load value of the period. The load of each cycle segment is 65%.

[0075] Representative The duration of a cycle segment is in seconds. This value is calculated by the motor control system or monitoring system. The duration of each cycle segment is 10 seconds.

[0076] Representative The duration of each period is in seconds. The duration of each cycle segment is 8 seconds.

[0077] Represents the total number of cycle segments. For example, in a monitoring cycle, there are 5 cycle segments, so .

[0078] For practical calculations, a specific cycle segment is selected for derivation. Assume that the frequency of the second cycle segment is 50 Hz and the load is 70%, while the frequency of the first cycle segment is 48 Hz and the load is 65%, and the duration of the cycle segments is 10 seconds and 8 seconds respectively.

[0079] Calculate the difference between the frequencies of the second and first period segments:

[0080] ;

[0081] Calculate the difference between the load of the second cycle segment and the load of the first cycle segment:

[0082] ;

[0083] Compute the sum of the time differences:

[0084] ;

[0085] Substitute these values into the formula for calculation:

[0086] ;

[0087] ;

[0088] ;

[0089] ;

[0090] The result shows that the frequency-load alternation degree is 0.747, indicating that the alternating fluctuations between the motor frequency and load were moderate during the monitoring period. This value reflects the frequency of fluctuations between the frequency and load in the system; larger values indicate more severe alternations between the frequency and load.

[0091] S202: Based on the frequency-load alternation intensity value, call the task node interruption attribute identifier corresponding to the task number, filter the frequency-load alternation intensity value, determine whether the task node has the interruption attribute, and set the load adjustment mode to switching type or slow-release type based on the interruption attribute, and establish an adjustment mode type identifier group;

[0092] Next, the task node's interruption attribute flag corresponding to the task number is called to determine whether the task possesses the interruption attribute. This flag is used to determine whether an interruption is likely to occur during task execution or whether special control measures are required. For example, a task node might indicate that an interruption strategy is required if the alternation intensity exceeds a certain threshold (e.g., 80%). By comparing this flag with the interruption attribute, the frequency-load alternation intensity values exceeding the threshold are screened. After determining whether the interruption attribute is present, the load adjustment method is set based on this attribute. If the task node possesses the interruption attribute, the load adjustment method is switching, meaning it directly switches to the new load state. If it does not possess the interruption attribute, a gradual release adjustment method is used, meaning it gradually adjusts the load. For example, if the frequency-load alternation intensity value is 85% and the task node is marked as having the interruption attribute, the load adjustment method is switching. If the alternation intensity is 70% and the task node does not possess the interruption attribute, the gradual release adjustment method is used. This process establishes an adjustment method type flag group containing the corresponding adjustment method type for each task node, facilitating subsequent control decisions.

[0093] S203: Based on the adjustment mode type identifier group, the frequency value and the load value under the task node are combined with the corresponding adjustment mode type identifier to form a ternary combination item in the order of the task numbers, and missing combinations are filtered out to generate a task control state configuration group;

[0094] The frequency and load values for a task node are combined with the corresponding adjustment mode type identifier to form a ternary combination item. Each ternary combination item includes frequency, load, and adjustment mode type, which together constitute a complete adjustment strategy. For example, if task number 001 is assigned a 50Hz frequency, 75% load, and switching adjustment mode, the combination item for this node is (50Hz, 75%, switching). By performing similar combinations of the frequency, load, and adjustment mode types for all task nodes, a task control state configuration group is generated. This combination process ensures that the frequency and load data for each task node are complete. If the data for a task node is incomplete, the combination item is excluded. For example, if a task node lacks load data, the ternary combination item for that node is eliminated. Through these operations, a task control state configuration group is ultimately generated, containing all operational task node combinations for further task scheduling and execution management.

[0095] The specific steps of S3 are:

[0096] S301: Call the beat change data of the adjustment combination item in the task control status configuration group, perform synchronization comparison on the output time periods set in the upstream and downstream task channels, calculate the time period data difference based on the time nodes of the task segments, extract all task segments with time differences less than or equal to zero, and obtain the beat overlap conflict comparison value;

[0097] The specific formula for calculating the difference in time period data is:

[0098] ;

[0099] in, Represents the data difference of the time period, Representative The end time of the output time period of each task channel, Representative The time node of each task segment, Represents the total number of cycle segments;

[0100] This formula is used to calculate the time period data difference, reflecting the difference between the task segment time node and the task channel output time period. The acquisition and quantification methods of each parameter are as follows:

[0101] Representative The end time of the output time period of each task channel is usually obtained in real time by the task management system or control system. For example, the output time period of the first task channel is from 10 seconds to 20 seconds, so Second.

[0102] Representative The time node of each task segment refers to the time point at which the task is actually executed, which is determined by the system according to the task schedule. For example, the time node of the first task segment is 15 seconds, that is, Second.

[0103] Represents the total number of task segment pairs. For example, in a task plan, there are 3 task segment pairs that need to be processed. .

[0104] For actual calculation, we select a specific task segment for derivation. Assuming there are three task segments, the output time period of the task channel and the task segment time nodes are as follows:

[0105] The first task segment: the output time segment of the task channel Seconds, task segment time node Second;

[0106] The second task segment: the output time segment of the task channel Seconds, task segment time node Second;

[0107] The third task segment: the output time segment of the task channel Seconds, task segment time node Second.

[0108] Step 1: Calculate the time difference of each task segment

[0109] For the first task segment, calculate the time difference:

[0110] ;

[0111] For the second task segment, calculate the time difference:

[0112] ;

[0113] For the third task segment, calculate the time difference:

[0114] ;

[0115] Step 2: Calculate the weighted time difference

[0116] For the first task segment, calculate the weighted time difference:

[0117] ;

[0118] For the second task segment, calculate the weighted time difference:

[0119] ;

[0120] For the third task segment, calculate the weighted time difference:

[0121] ;

[0122] Step 3: Calculate the sum

[0123] ;

[0124] Step 4: Calculate the difference in time period data

[0125] ;

[0126] The result shows that the overlap between the task segments and the task channel output time periods is 5.67, indicating a moderate time difference between the task segments and the output time periods. This value can be used to further determine the overlap and take appropriate scheduling or optimization measures.

[0127] S302: Based on the beat overlap conflict comparison value, a priority splitting operation is performed on the task segments with time overlap, the priority parameters defined in the task number sequence are called, and the execution order of the channel nodes with the same task path identifier is readjusted according to the priority sorting result. At the same time, the adjusted task number and channel path identifier combination is recorded to obtain a task segment channel path information set;

[0128] Based on the beat overlap conflict comparison value, priority splitting is performed, particularly for task segments with temporal overlap. First, the temporal overlap between the task segments is determined through comparative calculation. If the time periods of two task segments overlap, a priority split is performed. During the splitting process, the system uses the priority parameters defined in the task number sequence. These priority parameters represent the execution priority of the tasks. Priority values can be numerical (for example, Task 1 has a high priority of 10, while Task 2 has a medium priority of 5). For channel nodes with the same task path identifier, the system re-adjusts the execution order based on the priority sorting results. For example, if Task 1 has a higher priority than Task 2, Task 1 will be executed first, and the adjusted execution order will be Task 1 → Task 2. The system then records the adjusted task number and channel path identifier combination to generate a task segment channel path information set. This information set is used for subsequent path scheduling and task management. Assuming that Task 1 and Task 2 have temporal overlap and Task 1 has a higher priority, Task 1 will be executed earlier and Task 2 later, ultimately forming a task segment channel path set containing the adjusted information.

[0129] S303: Based on the task segment channel path information set, filter all the reachable paths in the task number and channel path combination, remove the path branches containing conflict identifiers, retain the execution channel combination items without beat conflicts, and establish a dispatch path list;

[0130] Based on the task segment channel path information set, the next step is to filter out all reachable paths among all combinations of task numbers and channel paths. This process relies on task path feasibility and conflict detection. The system first traverses all combinations of task numbers and channel paths to determine which paths are reachable. For example, Task 3 may have two paths: Path A and Path B. Path A is not subject to takt conflicts, while Path B may conflict with other task segments. The system removes path branches with conflict indicators and retains execution channel combinations without takt conflicts. Conflict indicators are generated by the takt overlap conflict comparison value, and any paths involving overlap or conflict are excluded. For example, if Path A and Path B overlap, Path B is eliminated, leaving only Path A. Through this screening process, the system ultimately generates a dispatch path list containing all execution channel combinations without takt conflicts. These combinations can be used in subsequent task scheduling to ensure that tasks are executed in the correct order and along the correct paths, thereby optimizing overall production or operational efficiency.

[0131] The specific steps of S4 are:

[0132] S401: Based on the correspondence between task numbers and equipment numbers in the dispatch path list, the concurrent tasks in the current batch that are in the same time period are screened, all equipment numbers in the cross-running time period are extracted, duplicate numbers are removed, and the equipment numbers are reorganized according to the task numbers. A bidirectional mapping index structure between equipment numbers and task numbers is constructed to generate an equipment scheduling index value.

[0133] First, the system filters out concurrent tasks within the current batch that fall within the same time period, identifies their task numbers, and extracts the corresponding device numbers. For example, suppose Task A uses Devices 1 and 2 during Time Period 1, while Task B uses Devices 3 and 4 during the same time period. The system then extracts all device numbers from the overlapping time periods, treating Devices 1, 2, 3, and 4 as the device number set. To avoid duplicate counting, duplicate device numbers are removed to produce a list of unique device numbers. If Devices 2 and 3 appear repeatedly during this process, the system removes them. The system then reorganizes tasks by task number, reassigning devices to Task A and Task B according to their respective task numbers to ensure device independence and clear task execution. Finally, the system constructs a bidirectional mapping index structure between device and task numbers, associating each task number with its corresponding device number. For example, Task A corresponds to Devices 1 and 2, and Task B corresponds to Devices 3 and 4. This operation generates a device scheduling index value, providing a clear scheduling management system for tasks and devices.

[0134] S402: Based on the device scheduling index value, count the number of repeated cross-operation device numbers on each path, aggregate them by path number, use the number of repeated device numbers as the number of conflicts within the path, sort the path numbers from high to low by the number of conflicts, and generate a path operation weight value.

[0135] Based on the device scheduling index value, the next step is to count the number of duplicate device numbers across each path. This is done by traversing each path and, within each path, counting the number of duplicate device numbers. For example, suppose path 1 contains devices 1, 2, 3, and 4. Devices 1 and 2 appear twice in the path, while devices 3 and 4 each appear once. Therefore, the number of duplicates between devices 1 and 2 is 2, and the number of duplicates between devices 3 and 4 is 1. Next, the system aggregates the number of duplicate device numbers within each path to calculate the number of conflicts for each path. For example, the number of conflicts for path 1 is the sum of the number of duplicates for devices 1 and 2, resulting in a conflict count of 2. The conflict counts for all paths are sorted from highest to lowest, with the path with the highest conflict count considered the most influential and prioritized. This sorting allows the system to generate a path weight, assigning each path a weight based on its conflict count. Paths with higher conflict counts receive higher weights and are prioritized for execution. Ultimately, this process generates a path operation weight value to help subsequent path scheduling decisions.

[0136] S403: Based on the path operation weight value and the index mapping relationship between the device number and the task number, the control items in the path number sequence are extracted and output in sequence, and a sequential combination mapping between the task number and the path number is established to generate a path priority table;

[0137] Based on the path execution weights, the next step is to combine the index mapping between device numbers and task numbers to extract the control items in the path number sequence and output them sequentially. First, the system sorts the paths according to their execution weights, selecting the paths with higher weights for scheduling control. For example, if path 1 has a weight of 5 and path 2 has a weight of 3, path 1 will be prioritized. Based on the order of the paths and the control items, the system extracts the control items for each path. For example, the control items for path 1 may include adjustments for both device 1 and device 2. The system then establishes a sequential mapping between task numbers and path numbers, recording the order of these two numbers to form an executable sequence list. For example, task A executes path 1 first, followed by task B and path 2. This generates a path priority table, ensuring that tasks are executed in a reasonable order based on path priority and task execution order, thereby optimizing the overall scheduling strategy.

[0138] The specific steps of S5 are:

[0139] S501: Based on the task number currently ranked first in the path priority table, the bound device number is extracted. The temperature change and pressure change trend data continuously recorded by the sensor attached to the device number within the current operating cycle are called. Based on the temperature change slope and the pressure change difference, the corresponding numerical interval index is calculated. The index is compared with the upper and lower bounds of the temperature and pressure threshold values bound to the device to generate a state threshold offset value.

[0140] The calculation formula for the corresponding numerical interval indicator is as follows:

[0141] ;

[0142] in, Represents the corresponding numerical interval indicator, Representative Temperature value for each time period, Representative Temperature value for each time period, Representative The pressure value of each time period, Representative The pressure value of each time period, Representative The length of time for temperature change in each time period, Representative The length of time the pressure changes in each time period, Represents the number of temperature and pressure data points;

[0143] This formula is used to calculate the combined changes in temperature and pressure to derive the device's state threshold offset value. The specific methods for obtaining and calculating each parameter are as follows:

[0144] Representative The temperature value of each time period is recorded and provided by the temperature sensor in real time. For example, the temperature recorded by the temperature sensor of the device in the second period is 80°C, which means °C.

[0145] Representative The temperature value of the previous period is provided by the same sensor. For example, the temperature of the first period is 75°C, which means °C.

[0146] Representative The pressure data is recorded and provided by the pressure sensor in real time. For example, the pressure recorded in the second period is 1.5MPa, which means MPa.

[0147] Representative For example, the pressure recorded in the first period is 1.4 MPa, which means MPa.

[0148] Representative The length of time the temperature changes in a time period is in seconds. For example, the temperature increases from 75°C to 80°C in 10 seconds, so Second.

[0149] Representative The length of time the pressure changes in a time period is in seconds. For example, the process of the pressure increasing from 1.4MPa to 1.5MPa also occurs within 10 seconds, so Second.

[0150] Represents the total number of data points. For this example, the number of data points is 2, which means .

[0151] Calculation process:

[0152] For the second cycle, calculate the temperature change rate:

[0153] ;

[0154] For the second cycle, calculate the pressure change rate:

[0155] ;

[0156] Multiply the two together to get the weighted change:

[0157] ;

[0158] Repeat this process for the second period to get the change. Since there are only two data points, the sum of this calculation is:

[0159] ;

[0160] Result analysis:

[0161] The results show that the temperature and pressure trend values are 0.005. This value represents the combined offset of temperature and pressure changes in the device's status during the current operating cycle. Using this indicator, the system can further assess whether the device meets the set thresholds and make adjustments or optimization decisions based on the device's status.

[0162] S502: Based on the state threshold offset value, the task numbers where either the temperature or pressure parameter exceeds the corresponding threshold boundary are screened, the current control parameter set of the cooling component attached to the device corresponding to the task number is extracted, the cooling frequency value, cooling duration, and pressure response time recorded in the current operation cycle are called, a beat correction combination item is constructed, and a beat overlap control quantity is generated;

[0163] Based on the calculated state threshold offset, the system selects task numbers where either temperature or pressure exceeds the corresponding threshold. For example, in this monitoring scenario, the device's temperature change is 28°C and its pressure is 1.6 MPa. These pressures exceed the set thresholds. Therefore, the system selects the corresponding task number, Task A, for subsequent processing. Next, the system extracts the current control parameter set for the cooling component attached to the device corresponding to the task number. Cooling components may include cooling pumps and fans, and their control parameter sets may include cooling frequency, cooling duration, and pressure response time. Assuming a cooling frequency of 50 Hz, a cooling duration of 10 minutes, and a pressure response time of 3 minutes, the system constructs a beat correction combination based on these control parameters. The beat correction combination takes into account the actual operating conditions of the device. For example, if the pressure fluctuates significantly, the system may need to increase the cooling intensity, and thus adjust the cooling frequency. Finally, the system generates a beat overlap control variable, indicating that the operating state of the cooling device needs to be adjusted in a timely manner based on pressure fluctuations during task execution.

[0164] S503: Based on the beat overlap control quantity, the corresponding equipment number, cooling component parameters, and beat control quantity are combined to construct a structure to form mapping data between the task number and the reset parameter. The mapping structure is output according to the task number sequence to generate an intelligent management solution.

[0165] Based on the generated beat overlap control, the system combines the corresponding device numbers, cooling component parameters, and beat control variables to construct a structure. For example, the device numbers for Task A are 1 and 2, the cooling component control parameters include a cooling frequency of 50 Hz, a cooling duration of 10 minutes, and a pressure response time of 3 minutes, and the beat control variable is to increase the cooling frequency to 60 Hz. After encapsulating these parameters into a structure, the system forms a mapping data between the task number and the reset parameter, indicating the parameter adjustment requirements for each task. The mapping structure is then output in sequence by task number to form an adjustment table. Ultimately, this table is provided to the control system for real-time management and scheduling as part of the intelligent management solution. This ensures that the cooling system's operating mode is adjusted according to the device status during task execution, thereby optimizing task execution stability and equipment protection.

[0166] 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 modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An intelligent management method for the production of high-speed rotating transmission devices, characterized in that: The following steps are involved: S1: Obtain operating data for motors, gears, and bearings in high-speed rotating transmissions, extract frequency, power consumption, and speed per unit time, calculate fluctuation deviations, mark components that exceed the fluctuation range as abnormal nodes, and generate a list of abnormal components to be managed. S2: extracting the cycle frequency and load of the motor's current task based on the component operating status and task number in the list of abnormal components to be managed, calculating the load adjustment method and generating a task control state configuration group; S3: Calculate the difference between the output time periods of the upstream and downstream task channels through the task control state configuration group to determine whether there is beat overlap. If there is overlap, split the task priorities and record the paths to generate a dispatch path list. S4: Filter concurrent task execution segments according to the dispatch path list, remove duplicate device numbers in cross-running segments, sort by the number of path conflicts to generate a dispatch priority, and output a path priority table; S5: extracting the path priority table and determining whether a defined threshold value is exceeded. If so, calling the control parameters of the equipment cooling component and resetting the cycle time to generate an intelligent management solution; The specific steps of S3 are: S301: Calling the beat change data of the adjustment combination item in the task control state configuration group, performing synchronous comparison with the output time periods set in the corresponding upstream and downstream task channels, calculating the time period data difference based on the time nodes of the task segments as the benchmark, extracting all task segments with time differences less than or equal to zero, and obtaining the beat overlap conflict comparison value; S302: Based on the beat overlap conflict comparison value, a priority splitting operation is performed on the task segments with time overlap, priority parameters defined in the task number sequence are called, and the execution order of the channel nodes with the same task path identifier is readjusted according to the priority sorting result. At the same time, the adjusted task number and channel path identifier combination is recorded to obtain a task segment channel path information set; S303: Based on the task segment channel path information set, filter all the reachable paths in the task number and channel path combination, remove the path branches containing conflict identifiers, retain the execution channel combination items without beat conflicts, and establish a dispatch path list; The specific steps of S5 are: S501: Based on the task number currently ranked first in the path priority table, the bound device number is extracted, and the temperature change and pressure change trend data continuously recorded by the sensor attached to the device number within the current operating cycle are retrieved. Based on the temperature change slope and the pressure change difference, a corresponding numerical interval index is calculated, and the corresponding numerical interval index is compared with the upper and lower boundaries of the temperature threshold value and pressure threshold value bound to the device to generate a state threshold offset value. S502: Based on the state threshold offset value, the task numbers in which any temperature or pressure parameter exceeds the corresponding threshold boundary are screened, the current control parameter set of the cooling component attached to the device corresponding to the task number is extracted, the cooling frequency value, cooling duration, and pressure response time recorded in the current operation cycle are called, a beat correction combination item is constructed, and a beat overlap control amount is generated; S503: Based on the beat overlap control amount, the corresponding equipment number, cooling component parameters and beat control amount are combined to construct a structure to form mapping data of task number and reset parameter, and the mapping structure is output according to the task number sequence to generate an intelligent management solution.

2. The intelligent management method for the production of high-speed rotating transmission devices according to claim 1, characterized in that: The list of abnormal components to be managed includes the motor operating status, gear operating status, bearing operating status, abnormal node number, fluctuation deviation value, standard operating upper and lower limits, unit time frequency, power consumption, and speed. The task control status configuration group includes motor cycle frequency, operating load, load adjustment method, task node interruption option, frequency and load adjustment combination item, and adjustment method type. The allocation path list includes task segment priority splitting results, channel path information, beat change data, upstream and downstream task channel output time periods, and beat overlap conflict judgment results. The path priority sorting table includes task number, equipment number, cross-operation time period equipment number, allocation index, path conflict number, path operation weight value, control order, and number mapping structure. The intelligent management solution includes continuous temperature change, operating pressure change trend, threshold group definition boundary, cooling component control parameters, and beat reset operation.

3. The intelligent management method for the production of high-speed rotating transmission devices according to claim 1, characterized in that: The specific steps of S1 are: S101: Acquire the operating data of the motor, gears, and bearings, extract the frequency, power consumption, and speed parameter values, calculate the frequency fluctuation value per unit time, analyze the power consumption change rate and speed deviation, and generate the operating characteristic fluctuation value; S102: Based on the operating characteristic fluctuation, the frequency fluctuation upper and lower limit interval values, the power consumption change rate limit value, and the speed deviation tolerance value are called, and the fluctuation value of the component is compared with the corresponding reference value in sequence to determine whether it exceeds the interval range. The component number that does not meet the reference is marked as an abnormal node, and a set of abnormal node numbers is obtained; S103: Based on the abnormal node number set, extract the frequency, power consumption, and speed operating status parameters of the corresponding numbered node in the current time period, combine the number and status to construct a structure, arrange the structure data in the order of the number, and generate a list of abnormal components to be managed.

4. The intelligent management method for the production of high-speed rotating transmission devices according to claim 1, characterized in that: The specific steps of S2 are: S201: extracting the cycle frequency and operating load of the motor in the time period corresponding to the current task number based on the operating status and task number of the components in the list of abnormal components to be managed, calculating the frequency-load alternation degree index of the cycle segment based on the pairing value of the task execution cycle and the load peak interval, and generating a frequency-load alternation intensity value; S202: Based on the frequency-load alternation intensity value, the task node interruption attribute identifier corresponding to the task number is called to screen the frequency-load alternation intensity value to determine whether the task node has the interruption attribute. If the task node has the interruption attribute, the load adjustment method is switching type, that is, directly switching to a new load state. If the task node does not have the interruption attribute, a slow-release adjustment method is adopted, that is, gradually adjusting the load, and establishing an adjustment method type identifier group; S203: Based on the adjustment mode type identification group, the frequency value, the load value and the corresponding adjustment mode type identification under the task node are combined to form a ternary combination item in the order of the task numbers, and the missing item combinations are filtered out to generate a task control state configuration group.

5. The intelligent management method for the production of high-speed rotating transmission devices according to claim 4, characterized in that: The specific calculation formula for the frequency load alternation degree index of the period segment is: ； in, Represents the frequency load alternation degree index of the cycle segment, Representative The frequency value of the period segment, Representative The frequency value of the period segment, Representative The load value of a cycle segment, Representative The load value of a cycle segment, Representative The duration of the period, Representative The duration of the period, Represents the total number of cycle segments.

6. The intelligent management method for the production of high-speed rotating transmission devices according to claim 1, characterized in that: The specific calculation formula for the data difference of the time period is: ； in, Represents the data difference of the time period, Representative The end time of the output time period of each task channel, Representative The time node of each task segment, Represents the total number of cycle segments.

7. The intelligent management method for the production of high-speed rotating transmission devices according to claim 1, characterized in that: The specific steps of S4 are: S401: Based on the correspondence between task numbers and equipment numbers in the dispatch path list, concurrent tasks in the same time period in the current batch are screened, all equipment numbers in the cross-running time period are extracted, duplicate numbers are removed, and the equipment numbers are reorganized according to the task numbers. A bidirectional mapping index structure between equipment numbers and task numbers is constructed to generate an equipment scheduling index value. S402: Based on the device scheduling index value, count the number of repetitions of the cross-running device number under each path, aggregate them by path number, use the number of device number repetitions as the number of conflicts within the path, sort the path numbers from high to low by the number of conflicts, and generate a path operation weight value; S403: According to the path operation weight value, combined with the index mapping relationship between the device number and the task number, the control items in the path number sequence are extracted and output in sequence, a sequential combination mapping between the task number and the path number is established, and a path priority table is generated.

8. The intelligent management method for the production of high-speed rotating transmission devices according to claim 1, characterized in that: The calculation formula of the corresponding numerical interval index is specifically as follows: ； in, Represents the corresponding numerical interval indicator, Representative Temperature value for each time period, Representative Temperature value for each time period, Representative The pressure value of each time period, Representative The pressure value of each time period, Representative The length of time for temperature change in each time period, Representative The length of time the pressure changes in each time period, Represents the number of temperature and pressure data points.

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