An adaptive energy-saving control and operation data processing method and system for an asynchronous motor

Abstract

The application discloses a kind of asynchronous motor adaptive energy-saving control and operation data processing method and system, it is related to data processing technical field, method includes: obtaining the historical operation data of same type asynchronous motor, maintenance event data and load linkage relationship data, identify effective load carrying operation fragment and generate effective load carrying maintenance interval sequence;Combining standard maintenance interval, design service life and maintenance before observation period operation data, screen fault-driven effective maintenance interval, generate historical interval compression state and main exception type;Accordingly, asynchronous motor maintenance equipment set is divided, maintenance compression state value, pre-maintenance interval sequence and pre-maintenance timing are generated, and pre-maintenance operation constraint data is output.The application solves the problems of maintenance interval distortion, unclear fault-driven maintenance record identification and collaborative maintenance delay first-expiring equipment, can improve maintenance scheduling accuracy and support energy-saving operation management before maintenance.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to an adaptive energy-saving control and operation data processing method and system for asynchronous motors. Background Technology

[0002] In industrial production, asynchronous motors are typically used as continuous power sources. Their maintenance schedule is related to production cycle time, load distribution, downtime, and operating energy consumption. Multiple asynchronous motors of the same type within the same production line or process section may drive adjacent loads in the same conveyor chain, roller conveyor, pump group, fan group, or continuous production unit. Although these asynchronous motors may have similar models and maintenance specifications, their actual operating conditions are not entirely consistent. Different asynchronous motors may differ in terms of load-bearing operating time, no-load operating time, temperature variations, vibration variations, power factor variations, and maintenance causes.

[0003] In current maintenance management, a common approach is to schedule maintenance based on fixed maintenance cycles, alarm thresholds, or current operating parameters. While this method can reflect some operating states, it struggles to distinguish the actual load-bearing operating time of the asynchronous motor between maintenance intervals. In actual production, asynchronous motors experience states such as shutdown, maintenance shutdown, no-load operation, light-load operation, and effective load-bearing operation. If the calendar time between two maintenance sessions is directly used as the maintenance interval, downtime and no-load operation time will be included in the equipment wear and tear. Conversely, if normal operating time is used directly, invalid no-load operation segments with low current and low power factor may be included in the maintenance interval, resulting in the maintenance interval failing to accurately reflect the operating losses of the asynchronous motor under load.

[0004] Meanwhile, maintenance event data typically includes scheduled maintenance, planned shutdown maintenance, post-fault maintenance, abnormal temperature rise maintenance, abnormal vibration maintenance, protection trip maintenance, power factor abnormality maintenance, current abnormality maintenance, and abnormal state triggered maintenance. If the cause of maintenance and the operating status before maintenance are not distinguished, and all records shorter than the maintenance cycle are treated as abnormal maintenance records, planned shutdown maintenance, production plan adjustments, or manual premature maintenance may interfere with the judgment of fault trends. For the coordinated maintenance of multiple asynchronous motors, if the maintenance is merged only based on whether the maintenance time is close, the maintenance time of the asynchronous motor due earlier in the group may be delayed, or asynchronous motors with different abnormality types and unrelated load relationships may be grouped into the same maintenance batch. Summary of the Invention

[0005] In response to the technical problems described in the background art, the present invention provides an adaptive energy-saving control and operation data processing method and system for asynchronous motors, which is used to address the problems of inaccurate calculation of maintenance intervals for asynchronous motors, unclear identification of fault-driven maintenance records, and the possibility of delayed maintenance of equipment due earlier in the collaborative maintenance process. The method combines pre-maintenance timing with pre-maintenance operation constraints to support maintenance scheduling and energy-saving operation management of asynchronous motor groups.

[0006] An adaptive energy-saving control and operation data processing method for asynchronous motors includes: acquiring historical operation data sequences, maintenance event data, and load linkage relationship data of multiple asynchronous motors of the same type; identifying effective load-bearing operation segments based on start-stop status data, operating current data, and power factor data; and generating an effective load-bearing maintenance interval sequence based on the effective load-bearing operation segments between adjacent maintenance events; obtaining the target maintenance interval corresponding to each effective load-bearing maintenance interval based on the standard maintenance interval, design service life, effective load-bearing cumulative operating time, and pre-maintenance observation period operation data; and further determining the target maintenance interval based on the effective load-bearing maintenance interval, target maintenance interval, maintenance reason identifier, and pre-maintenance observation period operation data. The system filters operational data during the observation period to identify fault-driven effective maintenance intervals, generating historical interval compression status and main anomaly types. Based on historical interval compression status, main anomaly types, and load linkage data, it divides asynchronous motor maintenance equipment sets and obtains maintenance compression status values ​​and pre-maintenance interval sequences according to the effective load-bearing maintenance interval compression amount within the group, the number of asynchronous motors with main anomaly types, and the total number of asynchronous motors within the group. Based on the pre-maintenance interval sequence, the last maintenance end time of each asynchronous motor within the group, and the maintainable window, it generates a pre-maintenance timing sequence no later than the maintenance expiration time of the earliest expiring single machine within the group, and outputs pre-maintenance operational constraint data when the operational constraint triggering conditions are met.

[0007] Optionally, valid load-bearing operation segments are identified based on start-stop status data, operating current data, and power factor data, including: acquiring the no-load identification current and no-load identification power factor of the same type of asynchronous motor; wherein, the no-load identification current is determined based on the no-load test operation current of the same type of asynchronous motor, and the no-load identification power factor is determined based on the no-load operation data and light-load operation data of the same type of asynchronous motor; when the start-stop status data corresponding to the target sampling segment indicates that the asynchronous motor is in operation, the operating current data corresponding to the target sampling segment exceeds the no-load identification current, and the power factor data corresponding to the target sampling segment exceeds the no-load identification power factor, the target sampling segment is determined as a valid load-bearing operation segment; when the start-stop status data corresponding to the target sampling segment indicates that the asynchronous motor is in a stopped or maintenance stopped state, or the operating current data corresponding to the target sampling segment does not exceed the no-load identification current, or the power factor data corresponding to the target sampling segment does not exceed the no-load identification power factor, the target sampling segment is not determined as a valid load-bearing operation segment.

[0008] Optionally, generating an effective load-bearing maintenance interval sequence based on effective load-bearing operation segments between adjacent maintenance events includes: sorting maintenance event data for the same asynchronous motor according to the maintenance start time; using the maintenance end time of the previous maintenance event among two adjacent maintenance events as the start time of the effective load-bearing maintenance interval; using the maintenance start time of the next maintenance event among two adjacent maintenance events as the end time of the effective load-bearing maintenance interval; extracting effective load-bearing operation segments between the interval start and end; and accumulating the effective load-bearing maintenance interval based on the sampling time data corresponding to the effective load-bearing operation segments; when maintenance event data lacks a maintenance end time, supplementing the maintenance end time based on the average maintenance duration of similar maintenance events; when maintenance event data lacks both a maintenance start time and a maintenance end time, marking the corresponding maintenance event as a maintenance time missing event and prohibiting the corresponding maintenance event from participating in the generation of effective load-bearing maintenance intervals.

[0009] Optionally, based on the standard maintenance interval, design service life, effective cumulative load running time, and pre-maintenance observation period data, the target maintenance interval corresponding to each effective load maintenance interval is obtained. This includes: obtaining the effective cumulative load running time based on the effective load running segment of the same asynchronous motor from the commissioning time to the start of the effective load maintenance interval; determining the life stage of the asynchronous motor at the start of the corresponding effective load maintenance interval based on the effective cumulative load running time and design service life; wherein, the life stage includes the initial life stage, the middle life stage, the late life stage, and the over-life operation stage; obtaining the corresponding target maintenance interval compression level from the target maintenance interval rule table based on the life stage, and generating the target maintenance interval based on the standard maintenance interval and the target maintenance interval compression level; wherein, the target maintenance interval rule table is determined based on the historical periodic maintenance records of the same type of asynchronous motor in different effective cumulative load running time intervals.

[0010] Optionally, the effective on-load maintenance interval is screened based on the effective on-load maintenance interval, target maintenance interval, maintenance reason identifier, and operating data during the observation period before maintenance. This includes: determining whether the effective on-load maintenance interval to be determined has not reached the corresponding target maintenance interval; when the effective on-load maintenance interval to be determined has not reached the corresponding target maintenance interval, and the maintenance reason identifier is post-fault maintenance, abnormal temperature rise maintenance, abnormal vibration maintenance, protection trip maintenance, power factor abnormal maintenance, current abnormal maintenance, or abnormal state triggered maintenance, the effective on-load maintenance interval to be determined is determined as the fault-driven effective maintenance interval; when the effective on-load maintenance interval to be determined has not reached the target maintenance interval, and the maintenance reason identifier is post-fault maintenance, abnormal temperature rise maintenance, abnormal vibration maintenance, protection trip maintenance, power factor abnormal maintenance, current abnormal maintenance, or abnormal state triggered maintenance, the effective on-load maintenance interval to be determined is determined as the fault-driven effective maintenance interval; when the effective on-load maintenance interval to be determined has not reached the target maintenance interval, the target maintenance interval is determined as the target maintenance interval. When the target maintenance interval is missing and the maintenance reason identifier is missing, the operating data during the pre-maintenance observation period before the maintenance start time is used to determine whether there are abnormal operating current, abnormal power factor, abnormal temperature, or abnormal vibration. If at least one of the abnormal operating current, abnormal power factor, abnormal temperature, or abnormal vibration exists, the valid on-load maintenance interval to be determined is determined as the fault-driven valid maintenance interval. If the maintenance reason identifier is planned shutdown maintenance and the operating data during the pre-maintenance observation period does not show abnormal operating current, abnormal power factor, abnormal temperature, or abnormal vibration, the valid on-load maintenance interval to be determined is not determined as the fault-driven valid maintenance interval.

[0011] Optionally, the historical interval compression status and main anomaly type are generated, including: arranging the effective maintenance intervals of the same asynchronous motor driven by faults in chronological order and comparing the changes of adjacent effective maintenance intervals driven by faults sequentially; recording a single compression status when the effective maintenance intervals driven by faults before and after the current show compression changes but do not form a continuous compression status or an accelerated compression status; recording a continuous compression status when continuous compression changes occur; recording an accelerated compression status when the compression magnitude of adjacent effective maintenance intervals driven by faults continues to increase; recording an interval recovery status when the effective maintenance intervals driven by faults before and after the current show recovery changes; and based on the abnormal operating current and power factor during the observation period before maintenance. Temperature anomalies and vibration anomalies generate pre-maintenance anomaly types; when multiple pre-maintenance anomaly types exist simultaneously, they are first sorted by anomaly duration, then by the magnitude exceeding the corresponding anomaly limit, and the pre-maintenance anomaly type ranked first is determined as the primary anomaly type; when the number of fault-driven effective maintenance intervals is less than two, an alternative historical interval compression state is generated based on the difference between the most recent effective load-bearing maintenance interval and the corresponding target maintenance interval, as well as the pre-maintenance anomaly type; when there are no fault-driven effective maintenance intervals for the same asynchronous motor, and the operating data during the most recent pre-maintenance observation period does not show abnormal operating current, power factor, temperature, or vibration, a stable maintenance state is recorded.

[0012] Optionally, the asynchronous motor maintenance equipment set is divided based on historical interval compression status, main anomaly type, and load linkage relationship data. This includes: sorting each asynchronous motor according to its historical interval compression status, with the sorting order being accelerated compression status, continuous compression status, single compression status, stable maintenance status, and interval recovery status; and sequentially determining whether the asynchronous motor to be grouped meets the grouping conditions with the asynchronous motors already in the current asynchronous motor maintenance equipment set. The grouping conditions include: effective load maintenance intervals being at the same fault interval compression level, the same main anomaly type or belonging to the same anomaly category, the existence of a load linkage relationship, the existence of the same drive object relationship, the existence of the same process section relationship, or the existence of a historical simultaneous operation relationship. The system is as follows: When the asynchronous motor to be grouped meets at least two grouping conditions with any existing asynchronous motor in the current asynchronous motor maintenance equipment set, the asynchronous motor to be grouped is considered as a candidate member of the current asynchronous motor maintenance equipment set; when the asynchronous motor to be grouped matches multiple asynchronous motor maintenance equipment sets simultaneously, it is preferentially grouped into the asynchronous motor maintenance equipment set with the most met grouping conditions; if the number of met grouping conditions is the same, it is preferentially grouped into the asynchronous motor maintenance equipment set ranked earlier in the historical interval compression state; when the asynchronous motor to be grouped does not meet at least two grouping conditions with any existing asynchronous motor in the current asynchronous motor maintenance equipment set, a new asynchronous motor maintenance equipment set is created; each asynchronous motor is only grouped into one asynchronous motor maintenance equipment set.

[0013] Optionally, the maintenance compression status value and pre-maintenance interval sequence are obtained, and pre-maintenance operation constraint data are output, including: determining the degree of maintenance interval compression based on the normalized result of the effective load-bearing maintenance interval compression amount within the asynchronous motor maintenance equipment set relative to the standard maintenance interval; determining the degree of anomaly concentration based on the normalized result of the number of asynchronous motors with major anomaly types within the asynchronous motor maintenance equipment set relative to the total number of asynchronous motors in the group; obtaining the maintenance compression status value based on the maintenance interval compression degree and the degree of anomaly concentration; and obtaining the pre-maintenance interval sequence from the pre-maintenance interval rule table based on the maintenance compression status value, the representative life stage within the group, and the preset plan window; wherein, the pre-maintenance interval rule table is based on asynchronous motors of the same type. Historical maintenance records, maintenance resource constraints, and production downtime window constraints are determined. When the maintenance compression status value reaches the preset risk level, or the main anomaly type in the asynchronous motor maintenance equipment set includes temperature anomaly, vibration anomaly, or power factor anomaly, or there is a fault-driven effective maintenance interval in the asynchronous motor maintenance equipment set with the maintenance reason identified as protection trip maintenance, it is determined that the asynchronous motor maintenance equipment set meets the operation constraint triggering conditions, and pre-maintenance operation constraint data is generated. Among them, the pre-maintenance operation constraint data includes the running time limit for continuously exceeding the high load ratio threshold, the suppression of no-load invalid operation, the priority transfer object of transferable load, the target production window operation indication, and pre-maintenance monitoring data items.

[0014] A system for adaptive energy-saving control and operation data processing of asynchronous motors is also provided, including a management platform. The management platform includes a data acquisition module, an effective load interval generation module, a fault drive interval confirmation module, a maintenance equipment set partitioning module, a pre-maintenance timing generation module, and an operation constraint output module. The data acquisition module is used to acquire historical operation data sequences, maintenance event data, and load linkage relationship data of multiple asynchronous motors of the same type. The effective load interval generation module is used to identify effective load operation segments based on start-stop status data, operating current data, and power factor data, and generate a set of effective load operation segments based on the effective load operation segments between adjacent maintenance events. The system includes: an effective maintenance interval sequence; a fault-driven interval confirmation module to obtain the target maintenance interval, filter fault-driven effective maintenance intervals, and generate historical interval compression status and main anomaly type; a maintenance equipment set partitioning module to partition the asynchronous motor maintenance equipment set, obtain maintenance compression status values, and generate a pre-maintenance interval sequence; a pre-maintenance timing generation module to generate a pre-maintenance timing sequence no later than the maintenance expiration time of the earliest expiring single machine in the group, based on the pre-maintenance interval sequence, the last maintenance end time of each asynchronous motor in the group, and the maintainable window; and a running constraint output module to output pre-maintenance running constraint data when the running constraint triggering conditions are met.

[0015] Optionally, the effective load interval generation module is further used to determine the target sampling segment as an effective load-bearing operating segment when the start / stop status data corresponding to the target sampling segment indicates that the asynchronous motor is in operation, the operating current data corresponding to the target sampling segment exceeds the no-load identification current, and the power factor data corresponding to the target sampling segment exceeds the no-load identification power factor; the fault drive interval confirmation module is further used to determine the effective load-bearing maintenance interval as a fault-driven effective maintenance interval when the effective load-bearing maintenance interval has not reached the corresponding target maintenance interval, and the maintenance reason is identified as post-fault maintenance, abnormal temperature rise maintenance, abnormal vibration maintenance, protection trip maintenance, power factor abnormal maintenance, current abnormal maintenance, or abnormal state triggered maintenance; the maintenance equipment set division module is further used to determine the effective load-bearing maintenance interval as the same as the asynchronous motor to be collected and the existing asynchronous motors in the current asynchronous motor maintenance equipment set. When at least two of the following conditions are met—fault interval compression level, same main anomaly type or belonging to the same anomaly category, existence of load linkage relationship, existence of the same driven object relationship, existence of the same process section relationship, or existence of historical simultaneous operation relationship—the asynchronous motor to be collected is added to the current asynchronous motor maintenance equipment set. The pre-maintenance sequence generation module is also used to obtain the next maintenance due time of each asynchronous motor in the same asynchronous motor maintenance equipment set, and determine the next maintenance due time of the single motor with the earliest due time as the first due time of the single motor in the group. When there is a maintainable window no later than the first due time of the single motor in the group, the maintainable window is determined as the unified pre-maintenance time point in the pre-maintenance sequence. When there is no maintainable window no later than the first due time of the single motor in the group, a separate pre-maintenance sequence is generated for the asynchronous motor with the earliest due time.

[0016] The processing effects of the present invention include:

[0017] In the entire asynchronous motor adaptive energy-saving control and operation data processing method, effective load-bearing operation segments are identified through start-stop status data, operating current data, and power factor data. Segments involving shutdown, maintenance shutdown, no-load invalid operation, and missing sampling are excluded from the maintenance interval calculation, ensuring that the effective load-bearing maintenance interval corresponds to the operating consumption of the asynchronous motor under load conditions. Furthermore, the target maintenance interval is obtained through standard maintenance intervals, design service life, and effective load-bearing cumulative operating time, allowing asynchronous motors at different life stages to be judged according to corresponding maintenance intervals. Moreover, the effective load-bearing maintenance interval, target maintenance interval, maintenance reason identifier, and pre-maintenance observation period operation data are used to filter fault-driven effective maintenance intervals, reducing the impact of planned shutdowns and premature maintenance without abnormalities on fault trend judgment. Furthermore, the asynchronous motor maintenance equipment set is divided through historical interval compression status, main anomaly type, and load linkage relationship data, ensuring that asynchronous motors in the same group meet the aggregation conditions in terms of maintenance interval changes, anomaly types, or load relationships. Furthermore, by maintaining the compression status value, the degree of compression of effective load maintenance intervals and the concentration of major anomaly types within the group are converted into the query basis for the pre-maintenance interval rule table, reducing the occurrence of maintenance batches generated solely based on maintenance date or equipment quantity. Furthermore, by using pre-maintenance sequence generation rules that are no later than the due date of the earliest single-machine maintenance within the group, the occurrence of delayed maintenance for early-due asynchronous motors is reduced. Simultaneously, by outputting pre-maintenance operational constraint data, the duration of continuous operation exceeding the high load ratio threshold is limited before the pre-maintenance sequence arrives, suppressing ineffective no-load operation, and providing data basis for transferable load scheduling and target production window selection. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0019] Figure 1 This is a schematic diagram illustrating the steps of the asynchronous motor adaptive energy-saving control and operation data processing method of the present invention;

[0020] Figure 2 This is a schematic diagram of a portion of steps S1 in the asynchronous motor adaptive energy-saving control and operation data processing method of the present invention;

[0021] Figure 3 This is a schematic diagram of a portion of steps S2 in the asynchronous motor adaptive energy-saving control and operation data processing method of the present invention;

[0022] Figure 4This is a schematic diagram of a portion of steps S3 in the asynchronous motor adaptive energy-saving control and operation data processing method of the present invention;

[0023] Figure 5 This is a schematic diagram of a portion of step S4 in the asynchronous motor adaptive energy-saving control and operation data processing method of the present invention;

[0024] Figure 6 This is a schematic diagram of the module structure and data flow of the asynchronous motor adaptive energy-saving control and operation data processing system of the present invention;

[0025] Figure 7 This is a timing diagram illustrating the pre-maintenance timing generation and operation constraint output in the asynchronous motor adaptive energy-saving control and operation data processing method of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0027] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0028] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] This invention provides an adaptive energy-saving control and operation data processing method for asynchronous motors, such as... Figure 1 As shown, in one specific embodiment, the method includes:

[0030] S1. Obtain historical operating data sequences, maintenance event data, and load linkage relationship data of multiple asynchronous motors of the same type. Identify effective load-carrying operating segments based on start-stop status data, operating current data, and power factor data. Generate an effective load-carrying maintenance interval sequence based on the effective load-carrying operating segments between adjacent maintenance events.

[0031] S2. Based on the standard maintenance interval, design service life, effective load-carrying cumulative running time and pre-maintenance observation period running data, obtain the target maintenance interval corresponding to each effective load-carrying maintenance interval, and filter the fault-driven effective maintenance intervals based on the effective load-carrying maintenance interval, target maintenance interval, maintenance reason identifier and pre-maintenance observation period running data, and generate historical interval compression status and main anomaly type.

[0032] S3. Divide the asynchronous motor maintenance equipment set according to the historical interval compression status, main anomaly type and load linkage relationship data, and obtain the maintenance compression status value and pre-maintenance interval sequence according to the effective load maintenance interval compression amount in the group, the number of asynchronous motors with main anomaly type and the total number of asynchronous motors in the group.

[0033] S4. Based on the pre-maintenance interval sequence, the last maintenance end time of each asynchronous motor in the group, and the maintainable window, generate a pre-maintenance timing sequence no later than the maintenance expiration time of the first single machine in the group that is due. When the operation constraint triggering condition is met, output the operation constraint data before pre-maintenance.

[0034] In this embodiment, asynchronous motors of the same type refer to asynchronous motors with the same rated power range, rated voltage, number of poles, installation purpose, cooling method, maintenance specifications, and production condition category, or belonging to the same maintenance standard category. The load linkage relationship between multiple asynchronous motors can be determined based on the production line equipment relationship table, driven object identifier, process section identifier, historical simultaneous operation records, and load transfer records. If multiple asynchronous motors drive adjacent loads in the same conveyor chain, the same roller conveyor, the same pump group, the same fan group, the same mixing section, or the same continuous production unit, then a load linkage relationship can be identified between the multiple asynchronous motors. The historical operating data sequence includes operating current data, operating voltage data, power factor data, temperature data, vibration data, start / stop status data, sampling time data, and asynchronous motor identifier. Maintenance event data includes maintenance start time, maintenance end time, maintenance reason identifier, maintenance result identifier, and maintenance event type. Maintenance reason identifiers include periodic maintenance, planned shutdown maintenance, post-fault maintenance, abnormal temperature rise maintenance, abnormal vibration maintenance, protection trip maintenance, power factor abnormal maintenance, current abnormal maintenance, and abnormal state triggered maintenance. Pre-maintenance operational constraint data includes runtime limits for consecutively exceeding high load ratio thresholds, suppression of idle and ineffective operation, priority transfer of transferable loads, target production window operation indicators, and pre-maintenance monitoring data items.

[0035] The maintenance waiting time is determined based on both the maintenance work order status and start / stop status data. It represents the time period during which the asynchronous motor has stopped production but maintenance has not yet begun. If the maintenance management system records the maintenance request time, maintenance start time, and maintenance work order waiting status, the maintenance waiting time is the time between the maintenance request time and the maintenance start time, provided the start / stop status is "stopped". If the maintenance management system does not record the maintenance request time, a separate maintenance waiting time is not generated, and the corresponding segment is excluded based solely on the stop status data. For example, if an asynchronous motor stops production at 08:00 on day 12 and begins maintenance at 11:00 on day 12, then the period from 08:00 to 11:00 on day 12 is recorded as the maintenance waiting time.

[0036] Abnormal state triggered maintenance refers to maintenance events where the maintenance management system does not categorize maintenance reasons into abnormal temperature rise maintenance, abnormal vibration maintenance, protection trip maintenance, power factor abnormal maintenance, or current abnormal maintenance, but the maintenance work order is triggered by an abnormal status code in the operation management system. Abnormal status codes can originate from temperature alarms, vibration alarms, current exceeding limits, power factor exceeding limits, or protection action records. For example, if the maintenance reason identifier in the maintenance work order is empty, but a temperature alarm status code exists within 12 hours prior to the maintenance start time, the corresponding maintenance event can be classified as abnormal state triggered maintenance.

[0037] In S1, historical operating data sequences, maintenance event data, and load linkage relationship data of multiple asynchronous motors of the same type are first obtained from the asynchronous motor operation management system, maintenance management system, and production relationship data. Unlike directly obtaining the calendar interval between two maintenance sessions, S1 uses start-stop status data, operating current data, and power factor data as the basis for identifying effective load-bearing operating segments. Start-stop status data is used to exclude shutdown and maintenance shutdown states, operating current data is used to determine whether the asynchronous motor has entered load-bearing operation, and power factor data is used to exclude no-load invalid operation. Subsequently, the start and end points of the interval are determined according to adjacent maintenance events of the same asynchronous motor. Only effective load-bearing operating segments between the start and end points of the interval are accumulated to obtain the effective load-bearing maintenance interval sequence. For example, if the calendar time between two maintenance events of an asynchronous motor is 30 days, including 3 days of shutdown time, 0.5 days of maintenance waiting time, 2 days of no-load invalid operation time, and 1 day of sampling missing time, then only the remaining segments that meet the operating status, have an operating current exceeding the no-load identification current, and a power factor exceeding the no-load identification power factor are included in the effective load-bearing maintenance interval. Therefore, S1 converts the calendar interval between two maintenance operations into an effective load maintenance interval, excluding downtime, maintenance waiting time, no-load invalid operation time, and sampling missing time.

[0038] In S2, the target maintenance interval corresponding to each effective load-bearing maintenance interval is obtained based on the standard maintenance interval, design service life, effective cumulative load running time, and pre-maintenance observation period operation data. The standard maintenance interval represents the basic maintenance cycle that an asynchronous motor of the same type should achieve according to maintenance specifications. The design service life represents the usable life of the asynchronous motor under rated operating conditions; both provide a benchmark for the target maintenance interval. The effective cumulative load running time is used to determine the life stage of the asynchronous motor at the start of the corresponding effective load-bearing maintenance interval. The pre-maintenance observation period operation data is used to determine whether there are abnormal operating currents, power factors, temperatures, or vibrations before maintenance. When the effective load-bearing maintenance interval to be judged has not reached the corresponding target maintenance interval, and the maintenance reason identifier points to post-fault maintenance, abnormal temperature rise maintenance, abnormal vibration maintenance, protection trip maintenance, power factor abnormality maintenance, current abnormality maintenance, or abnormal state triggered maintenance, the system determines the effective load-bearing maintenance interval to be judged as a fault-driven effective maintenance interval. When the maintenance reason identifier is missing, the system further determines whether there is an abnormality based on the pre-maintenance observation period operation data. If the maintenance reason is identified as planned shutdown for maintenance and there are no abnormalities in the operating data during the observation period before maintenance, the corresponding interval will not be considered as a fault-driven effective maintenance interval. Therefore, S2 filters fault-driven effective maintenance intervals from the maintenance event data and generates historical interval compression status and main anomaly type for subsequent grouping and time series generation.

[0039] In S3, asynchronous motor maintenance equipment sets are divided based on historical interval compression status, main anomaly type, and load linkage data. Historical interval compression status characterizes whether the effective maintenance interval driven by a fault in the same asynchronous motor has experienced single compression, continuous compression, accelerated compression, stable maintenance, or interval recovery. Main anomaly type characterizes the anomaly type that ranks first in terms of impact on the maintenance event among abnormal operating current, power factor, temperature, or vibration during the observation period before maintenance. Load linkage data characterizes whether multiple asynchronous motors have a relationship with the same driving object, the same process section, a historical simultaneous operation relationship, or a load transfer relationship. When dividing asynchronous motor maintenance equipment sets, the system requires that the asynchronous motor to be grouped meets at least two grouping conditions with existing asynchronous motors in the current asynchronous motor maintenance equipment set, such as: effective load maintenance intervals at the same fault interval compression level, the same main anomaly type or belonging to the same anomaly category, a load linkage relationship, a relationship with the same driving object, a relationship with the same process section, or a historical simultaneous operation relationship. Subsequently, the maintenance compression status value is obtained based on the effective load maintenance interval compression amount within the group, the number of asynchronous motors with major anomaly types, and the total number of asynchronous motors within the group. A pre-maintenance interval sequence is then generated based on the maintenance compression status value and the representative life stage within the group. Therefore, S3 generates an asynchronous motor maintenance equipment set based on the maintenance interval compression status, major anomaly types, and load linkage relationships, and generates a pre-maintenance interval sequence based on the maintenance compression status value.

[0040] In S4, based on the pre-maintenance interval sequence, the last maintenance end time of each asynchronous motor in the group, and the maintainable window, a pre-maintenance sequence is generated that is no later than the earliest due date of individual maintenance within the group. For the same set of asynchronous motor maintenance equipment, the system first obtains the next maintenance due date of each asynchronous motor based on the last maintenance end time and the pre-maintenance interval sequence, and then determines the next maintenance due date of the motor with the earliest due date as the earliest due date of individual maintenance within the group. If a maintainable window exists that is no later than the earliest due date of individual maintenance within the group, the system determines the maintainable window as the unified pre-maintenance time point in the pre-maintenance sequence; if no maintainable window exists that is no later than the earliest due date of individual maintenance within the group, a separate pre-maintenance sequence is generated for the asynchronous motor with the earliest due date. Simultaneously, when the maintenance compression status value reaches the preset risk level, or when the main anomaly type within the asynchronous motor maintenance equipment set includes temperature anomaly, vibration anomaly, or power factor anomaly, or when there is a fault-driven effective maintenance interval within the asynchronous motor maintenance equipment set with the maintenance cause identified as protection trip maintenance, the system outputs pre-maintenance operational constraint data. For example, it limits the duration of continuous operation exceeding the high load ratio threshold, suppresses ineffective no-load operation, generates priority transfer objects for transferable loads, outputs target production window operation instructions, and specifies pre-maintenance monitoring data items. Thus, S4 outputs pre-maintenance timing and pre-maintenance operational constraint data for use by the maintenance management system and production scheduling system.

[0041] In summary, this invention forms a processing chain from historical operating data of asynchronous motors to maintenance sequences and then to energy-saving operating constraints. This chain involves effective load-carrying operation segment identification, fault-driven effective maintenance interval screening, generation of historical interval compression status and main anomaly types, asynchronous motor maintenance equipment set division, maintenance compression status value acquisition, pre-maintenance sequence generation with a maintenance expiration time no later than the earliest due date of a single machine within the group, and output of pre-maintenance operating constraint data. This processing chain excludes downtime segments and no-load invalid operation segments from maintenance interval accumulation, and uses maintenance reasons, pre-maintenance anomaly types, and load linkage relationships for maintenance equipment set division. When generating collaborative maintenance sequences, the earliest due date of a single machine within the group is used as a constraint.

[0042] like Figure 2 As shown, in one specific embodiment, S1 includes:

[0043] S11. Obtain historical operating data sequences, maintenance event data, and load linkage relationship data of multiple asynchronous motors of the same type;

[0044] S12. Identify valid load-bearing operating segments based on start / stop status data, operating current data, and power factor data;

[0045] S13. Generate an effective load maintenance interval sequence based on the effective load operation segments between adjacent maintenance events.

[0046] In S11, historical operating data sequences, maintenance event data, and load linkage relationship data of multiple asynchronous motors of the same type are acquired. The historical operating data sequences are arranged in chronological order of sampling time and correspond to the asynchronous motor identifiers. The maintenance event data are sorted according to the maintenance start time and correspond to the asynchronous motor identifiers. The load linkage relationship data is used to characterize whether there is a relationship of the same driving object, the same process section, a historical simultaneous operation relationship, or a load transfer relationship among the asynchronous motors. The load linkage relationship data can be obtained from the production line equipment relationship table, equipment driving object table, process section configuration table, production scheduling record, and historical operation synchronization record. In this embodiment, a certain conveying section includes 5 asynchronous motors of the same type, numbered M1, M2, M3, M4, and M5, all with a rated power of 15 kW, a rated voltage of 380 V, a standard maintenance interval of 30 days, and a designed service life of 1800 days. The system acquires the historical operating data sequence of the most recent 180 days, with each sampling segment being 10 minutes long, and acquires the maintenance event data of each asynchronous motor within that time period. The system also reads the production line equipment relationship table to confirm that M1, M2, and M3 drive the same conveyor roller, indicating a shared drive object relationship and a historical simultaneous operation relationship. This step provides a data source for subsequent identification of effective load-carrying operation segments, screening of effective maintenance intervals driven by faults, and division of maintenance equipment sets.

[0047] Historical simultaneous operation relationships are determined based on the overlap of the operating states of two asynchronous motors within the same recording period. The system first reads the start-stop status data and effective load-bearing operation segments of the two asynchronous motors within the same recording period, and then calculates the cumulative duration of their simultaneous effective load-bearing operation. When this cumulative duration reaches the preset simultaneous operation ratio of the total effective load-bearing operation time for the corresponding recording period, a historical simultaneous operation relationship is established. For example, if a recording period is 30 days, and the cumulative duration of the two asynchronous motors simultaneously being in effective load-bearing operation is 18 days, with a preset simultaneous operation ratio of 50%, then the two motors satisfy the historical simultaneous operation relationship. The preset simultaneous operation ratio is determined based on the historical operation records of asynchronous motors with confirmed linkage relationships in the same production line. The system first selects asynchronous motor combinations marked as having the same drive object or the same process section in the equipment ledger, calculates the simultaneous effective load-bearing operation ratio of each combination within the recording period, and then takes the lower limit of the ratio that covers most confirmed linkage combinations as the preset simultaneous operation ratio; for example, if the simultaneous effective load-bearing operation ratio of confirmed linkage combinations is mainly distributed between 55% and 90%, then the preset simultaneous operation ratio is 50%.

[0048] Load transfer relationships are determined based on production scheduling records, load switching records, and operating power change records. The system first reads the load transfer instructions from the production scheduling system, and then compares the changes in operating power or operating current of the relevant asynchronous motors before and after the transfer. When the operating load of one asynchronous motor decreases and the operating load of another candidate asynchronous motor increases during the same scheduling period, and both correspond to the same or adjacent process sections, a load transfer relationship is recorded between them. For example, if the scheduling record shows that the conveying load of M1 is transferred to M4, and after the transfer, the operating power of M1 decreases by 3 kW while the operating power of M4 increases by 3.2 kW, and both belong to adjacent conveying sections, then a load transfer relationship between M1 and M4 is recorded.

[0049] In S12, valid load-bearing operation segments are identified based on start / stop status data, operating current data, and power factor data. The system first acquires the no-load identification current and no-load identification power factor of the same type of asynchronous motor. The no-load identification current is determined based on the stable current value of the same type of asynchronous motor during no-load trial operation. Specifically, it selects stable segments from no less than 5 no-load trial operations with a duration exceeding 10 minutes and current fluctuations not exceeding a preset current fluctuation ratio, takes the maximum value of the stable no-load operating current, and rounds it upward according to the current acquisition accuracy as the no-load identification current; for example, if the stable no-load operating currents are 6.1 A, 6.3 A, 6.2 A, 6.4 A, and 6.0 A, with a maximum value of 6.4 A, after rounding upward with a 0.5 A acquisition margin, the no-load identification current is taken as 7.0 A.

[0050] Current fluctuation is determined based on the ratio of the difference between the maximum and minimum operating current within the same stable segment to the average operating current of that segment. When this ratio does not exceed the preset current fluctuation ratio, the segment is considered stable. For example, if the maximum operating current in a certain no-load test run segment is 6.4 A, the minimum operating current is 6.1 A, and the average operating current is approximately 6.25 A, corresponding to a fluctuation ratio below 5%, then this segment can be considered stable. The preset current fluctuation ratio is determined based on no-load test run data of similar asynchronous motors and the error of the current acquisition device. The system first statistically analyzes the current fluctuation ratio of manually confirmed stable operating segments during multiple no-load test runs, and then determines the upper limit of the stable segment based on the allowable error of the current acquisition device. For example, if the current fluctuation ratio in manually confirmed stable no-load operating segments mainly does not exceed 4.2%, and the allowable error of the current acquisition device is approximately 0.5%, then the preset current fluctuation ratio is 5%.

[0051] The current acquisition margin is determined based on the resolution of the current acquisition device and the current fluctuation during no-load test operation of the same type of asynchronous motor. First, the minimum resolution of the current acquisition device is read, and then the maximum current fluctuation value within the no-load stable segment is calculated. The larger of the two values ​​is taken as the current acquisition margin. For example, if the current acquisition resolution is 0.1 A and the maximum fluctuation value within the no-load stable segment is 0.4 A, then the current acquisition margin is 0.4 A. When rounding upward, it can be processed according to the engineering record granularity of 0.5 A.

[0052] The no-load identification power factor is determined based on the upper limit of the no-load operating power factor and the lower limit of the light-load operating power factor of the same type of asynchronous motor. Specifically, no-load and light-load operating segments are first screened from historical operating data to obtain the concentrated range of no-load and light-load operating power factors, respectively. Then, a separator value is selected between the two concentrated ranges. For example, if the no-load operating power factor is concentrated between 0.18 and 0.26, and the light-load operating power factor is concentrated between 0.32 and 0.45, then 0.30 is selected as the no-load identification power factor between 0.26 and 0.32.

[0053] The no-load operation segment is selected based on start / stop status data, drive object load records, and production task records. When the asynchronous motor is running but the corresponding drive object has no production load task, the corresponding sampling segment is designated as the no-load operation segment. The light-load operation segment is selected based on the task segments in the production task records that are below 30% of the rated load. For example, if an asynchronous motor is running from 10:00 to 10:30, but the conveyor line has no material conveying task, then this period is a candidate no-load operation segment; if the task load from 11:00 to 11:20 is 25% of the rated load, then this period is a light-load operation segment.

[0054] The power factor concentration interval is determined by sorting historical power factor data of the same type of asynchronous motor. The system first removes shutdown segments, maintenance shutdown segments, missing sampling segments, and abnormal alarm segments. Then, it sorts the power factor data in the no-load or light-load operation segments in ascending order of value, removes the first and last discrete values ​​in the sort, and uses the minimum and maximum values ​​of the remaining data as the boundaries of the power factor concentration interval. For example, if the no-load operation power factor is concentrated between 0.18 and 0.26 after processing, and the light-load operation power factor is concentrated between 0.32 and 0.45, then 0.30 is selected as the no-load identification power factor between 0.26 and 0.32.

[0055] When the start / stop status data corresponding to the target sampling segment indicates that the asynchronous motor is in operation, the operating current data exceeds the no-load identification current, and the power factor data exceeds the no-load identification power factor, the target sampling segment is determined as a valid load-bearing operation segment. For example, if the operating current in a sampling segment is 18 A, the power factor is 0.74, and the start / stop status is in operation, then this sampling segment is a valid load-bearing operation segment; if the operating current is 6 A or the power factor is 0.22, it is not included in the valid load-bearing operation segment. This step is used to extract the sampling segments that form the load-bearing operation consumption from the ordinary operation data.

[0056] In S13, an effective load-bearing maintenance interval sequence is generated based on the effective load-bearing operation segments between adjacent maintenance events. The system sorts the maintenance event data of the same asynchronous motor according to the maintenance start time. The end time of the maintenance of the preceding maintenance event between two adjacent maintenance events is used as the starting point of the effective load-bearing maintenance interval, and the start time of the maintenance of the following maintenance event between two adjacent maintenance events is used as the ending point of the effective load-bearing maintenance interval. Effective load-bearing operation segments are extracted between the starting and ending points of the interval, and the effective load-bearing maintenance interval is obtained by accumulating the sampling time data corresponding to the effective load-bearing operation segments. For example, the end time of the previous maintenance for M1 is 10:00 on day 1, and the start time of the next maintenance is 10:00 on day 31, with a calendar interval of 30 days. Within this time period, the downtime is 3 days, the maintenance waiting time is 0.5 days, the no-load invalid operation time is 2 days, the sampling missing time is 1 day, and the accumulated effective load-bearing operation segments are 23.5 days. Therefore, the effective load-bearing maintenance interval is 23.5 days.

[0057] When maintenance event data lacks a maintenance end time, the maintenance end time is supplemented based on the average maintenance duration of similar maintenance events. The average maintenance duration for similar maintenance events is determined based on at least five recent maintenance event records for the same type of asynchronous motor. Records lacking a maintenance start time, a maintenance end time, or a maintenance duration exceeding the range of the same type of record set are first removed. Then, the arithmetic mean of the maintenance durations of the remaining maintenance events is taken. For example, the effective maintenance durations for abnormal temperature rise maintenance are 1.8 h, 2.0 h, 2.2 h, 2.1 h, and 1.9 h, respectively, averaging to 2.0 h. When a similar maintenance event lacks a maintenance end time, the maintenance end time is supplemented by shifting the maintenance start time by 2.0 h. When maintenance event data lacks both a maintenance start time and a maintenance end time, the corresponding maintenance event is marked as a maintenance time missing event, and the corresponding maintenance event is prohibited from participating in the generation of effective load-bearing maintenance intervals.

[0058] The range of similar records is determined based on the distribution of maintenance duration for similar maintenance events. First, the maintenance duration is summarized according to the maintenance event type. Then, the first and last records in the duration ranking are removed. The minimum to maximum value among the remaining records is used as the range of similar records. For example, the maintenance durations for abnormal temperature rise are 1.8 h, 2.0 h, 2.1 h, 2.3 h, and 5.5 h. After removing 1.8 h and 5.5 h, the range of similar records is 2.0 h to 2.3 h.

[0059] In a supplementary implementation, if the target sampling segment has a short-term missing operating current data but complete start-stop status data and power factor data, the system will not directly interpolate from adjacent sampling segments to generate operating current data. Instead, the target sampling segment will be marked as a missing sampling segment, and it will be prohibited from being included in the valid load-bearing operating segments. If the cumulative duration of consecutive missing sampling segments exceeds a preset missing ratio for one recording cycle, the corresponding asynchronous motor will be marked as having insufficient operating data integrity, and the data processing status will be retained when outputting the pre-maintenance sequence to indicate that subsequent data supplementation or manual verification is required.

[0060] The status of insufficient operational data integrity is determined based on the cumulative duration of consecutively missing sampling segments. The system first counts the cumulative duration of sampling segments for which operating current data, power factor data, or start / stop status data cannot be obtained within a recording period. When the cumulative duration exceeds the preset missing proportion for that recording period, the corresponding asynchronous motor is marked as having insufficient operational data integrity. For example, if a recording period is 30 days and the preset missing proportion is 8%, if the cumulative consecutive missing sampling reaches 3 days, it exceeds the 2.4-day limit, and the corresponding asynchronous motor is marked as having insufficient operational data integrity.

[0061] The preset missing ratio is determined based on the impact of sampling missing data within the historical record period on the effective load maintenance interval generation results. First, select no less than 30 record periods for the same type of asynchronous motor, and statistically analyze the sampling missing ratio and the maintenance interval deviation after manual review. When the maintenance interval deviation begins to exceed the allowable error of the enterprise's maintenance records, the corresponding sampling missing ratio is taken as the preset missing ratio. For example, if historical statistics show that the maintenance interval deviation is less than 0.5 days when the sampling missing ratio does not exceed 5%, and the maintenance interval deviation exceeds 1 day when the sampling missing ratio reaches 8%, then the preset missing ratio can be taken as 8%.

[0062] If duplicate maintenance events exist in the maintenance event data, the system uses both the maintenance start time and the maintenance reason identifier to determine duplicate records. If the difference between the maintenance start times of two maintenance events is less than the average maintenance duration of the same type of maintenance event and the maintenance reason identifier is the same, then the maintenance event with a complete maintenance result identifier is retained to reduce the situation where duplicate maintenance events shorten the effective load maintenance interval.

[0063] like Figure 3 As shown, in one specific embodiment, S2 includes:

[0064] S21. Obtain the target maintenance interval corresponding to each effective load maintenance interval based on the standard maintenance interval, design service life and effective load-carrying cumulative running time.

[0065] S22. Filter fault-driven effective maintenance intervals based on effective load maintenance intervals, target maintenance intervals, maintenance reason identifiers, and pre-maintenance observation period operation data.

[0066] S23. Generate historical interval compression status and main anomaly type based on the fault-driven effective maintenance interval.

[0067] In S21, the effective load-carrying cumulative runtime is obtained based on the effective load-carrying operation segments of the same asynchronous motor from the commissioning time to the start of the effective load-carrying maintenance interval. The effective load-carrying cumulative runtime does not include downtime, maintenance downtime, no-load invalid operation time, and sampling missing time. Based on the effective load-carrying cumulative runtime and the design service life, the system determines the life stage of the asynchronous motor at the start of the corresponding effective load-carrying maintenance interval. The life stage includes the initial life stage, the middle life stage, the late life stage, and the over-life operation stage.

[0068] The lifespan stage is determined based on the ratio of the cumulative effective load-bearing time to the design lifespan. The initial lifespan stage corresponds to a ratio of less than 40%, the mid-lifespan stage corresponds to a ratio of greater than or equal to 40% and less than 70%, the late lifespan stage corresponds to a ratio of greater than or equal to 70% and less than 100%, and the over-lifespan stage corresponds to a ratio of greater than or equal to 100%. For example, if the design lifespan is 1800 days and the cumulative effective load-bearing time is 1350 days, the ratio is 75%, which corresponds to the late lifespan stage.

[0069] The lifespan stage proportion boundaries are determined based on the design lifespan of the same type of asynchronous motor, historical periodic maintenance records, and fault-driven maintenance records. First, the frequency of fault-driven maintenance events is statistically analyzed under different percentages of cumulative effective load-bearing operation time. Then, the percentages at which fault-driven maintenance events begin to increase and increase intensively are selected as stage boundaries. For example, if historical records show that fault-driven maintenance is less frequent when the percentage is below 40%, increases after the percentage reaches 70%, and enters over-life operation after the percentage reaches 100%, then 40%, 70%, and 100% are used as stage boundaries.

[0070] Subsequently, the corresponding target maintenance interval compression level is obtained from the target maintenance interval rule table according to the life stage, and the target maintenance interval is generated based on the standard maintenance interval and the target maintenance interval compression level. The target maintenance interval rule table is determined based on the historical periodic maintenance records of the same type of asynchronous motor within different effective load-carrying cumulative operating time intervals. The target maintenance interval rule table is generated based on the historical periodic maintenance records of the same type of asynchronous motor. First, the life stage is divided into the initial life stage, the middle life stage, the late life stage, and the over-life operation stage according to the proportion of effective load-carrying cumulative operating time to the design life. Then, the periodic maintenance intervals that have not been driven by faults within each life stage are counted, and the robust representative value of the periodic maintenance interval for that stage is taken as the target maintenance interval. For example, if the design life is 1800 days, when the effective load-carrying cumulative operating time is 1350 days, the proportion is about 75%, which is classified as the late life stage. The historical periodic maintenance intervals are concentrated between 23 days and 25 days, so the target maintenance interval is 24 days.

[0071] Each record in the target maintenance interval rule table includes at least the lifespan stage, the target maintenance interval compression level, and the target maintenance interval. The target maintenance interval compression level is determined based on the degree to which historical periodic maintenance intervals are shortened relative to the standard maintenance interval within the same lifespan stage. When historical periodic maintenance intervals are close to the standard maintenance interval, the target maintenance interval compression level is uncompressed. When historical periodic maintenance intervals are shorter than the standard maintenance interval and no fault-driven maintenance has yet occurred, the compression level is determined sequentially according to the degree of shortening: the first target maintenance interval compression level, the second target maintenance interval compression level, or the third target maintenance interval compression level. After reading the lifespan stage, the system first retrieves the corresponding target maintenance interval compression level from the target maintenance interval rule table, and then reads the target maintenance interval corresponding to that level. For example, if the standard maintenance interval is 30 days, and historical periodic maintenance intervals without fault-driven maintenance in later lifespan stages are concentrated between 23 and 25 days, then this stage can be recorded as the second target maintenance interval compression level, and 24 days can be used as the corresponding target maintenance interval.

[0072] The robust representative value is determined based on the scheduled maintenance intervals that have not experienced fault-driven maintenance within the corresponding lifespan stage. Abnormal intervals caused by planned production stoppages, spare parts shortages, or premature manual maintenance are first eliminated. Then, the remaining intervals are sorted by value, and the median is taken as the target maintenance interval. For example, if the scheduled maintenance intervals for the later lifespan stage are 22 days, 23 days, 24 days, 25 days, and 36 days, after eliminating 36 days, the median of 24 days is taken. Therefore, the target maintenance interval for the later lifespan stage is 24 days.

[0073] In S22, fault-driven effective maintenance intervals are filtered based on the effective on-load maintenance interval, target maintenance interval, maintenance reason identifier, and pre-maintenance observation period operating data. The system determines whether the effective on-load maintenance interval to be judged has not reached the corresponding target maintenance interval. When the effective on-load maintenance interval to be judged reaches the corresponding target maintenance interval, the effective on-load maintenance interval to be judged is not determined as a fault-driven effective maintenance interval; when the effective on-load maintenance interval to be judged has not reached the corresponding target maintenance interval, the maintenance reason identifier and pre-maintenance observation period operating data are read again. If the maintenance reason identifier is post-fault maintenance, abnormal temperature rise maintenance, abnormal vibration maintenance, protection trip maintenance, power factor abnormal maintenance, current abnormal maintenance, or abnormal state triggered maintenance, then the effective on-load maintenance interval to be judged is determined as a fault-driven effective maintenance interval. For example, if the target maintenance interval for M1 is 24 days, the actual effective on-load maintenance interval is 20 days, and the maintenance reason identifier is abnormal temperature rise maintenance, then this interval is a fault-driven effective maintenance interval. If the maintenance reason identifier is missing, the system reads continuous operating current data, power factor data, temperature data, and vibration data prior to the maintenance start time to determine if there are any abnormalities in operating current, power factor, temperature, or vibration. If the maintenance reason identifier is planned shutdown for maintenance, and the operating data during the observation period before maintenance does not contain any of the above abnormalities, then this interval will not be considered a valid maintenance interval driven by a fault.

[0074] The pre-maintenance observation period is determined based on the early occurrence time of anomalies in historical fault-driven maintenance events of the same type of asynchronous motor. First, the time elapsed before the first occurrence of abnormal operating current, power factor, temperature, or vibration before the maintenance start time is statistically analyzed for each fault-driven maintenance event. Then, these early occurrence times are sorted from shortest to longest, and the early occurrence time near the 80th percentile after sorting is selected as the pre-maintenance observation period. If this early occurrence time is shorter than one production shift, the pre-maintenance observation period is one production shift; if it is longer than the company's maintenance warning cycle, the pre-maintenance observation period is the company's maintenance warning cycle. For example, if the historical anomaly early occurrence times are mainly distributed between 6 hours and 20 hours, and the 80th percentile corresponds to 18 hours, and the company uses a 24-hour maintenance warning cycle, then the pre-maintenance observation period can be the consecutive 18 hours before the maintenance start time.

[0075] The first occurrence of an anomaly refers to the number of consecutive sampling segments that reach the anomaly limit, which is the preset anomaly confirmation number, rather than a single sampling point instantaneously exceeding the limit. For example, if the sampling segment length is 10 minutes and the preset anomaly confirmation number is 3, when three consecutive sampling segments of temperature data exceed the temperature anomaly limit, the time point of the first sampling segment exceeding the temperature anomaly limit will be taken as the time point of the first temperature anomaly. The preset anomaly confirmation number is determined based on the sampling segment length, data fluctuation, and historical false alarm records. The system first counts the number of false alarms in the historical operating data of the same type of asynchronous motor where a single point exceeding the limit did not result in a maintenance event, and then selects the number of consecutive sampling segments that can reduce false alarms without significantly delaying anomaly confirmation; for example, if the sampling segment length is 10 minutes, there are many false alarms for single point exceeding the limit, and most of the three consecutive sampling segments exceeding the limit correspond to real anomalies, then the preset anomaly confirmation number is 3.

[0076] In S23, historical interval compression states and main anomaly types are generated based on the fault-driven effective maintenance intervals. The system arranges the fault-driven effective maintenance intervals of the same asynchronous motor in chronological order and compares the changes of adjacent fault-driven effective maintenance intervals sequentially. When the adjacent fault-driven effective maintenance intervals show compression changes but do not form a continuous compression state or an accelerated compression state, a single compression state is recorded; when continuous compression changes occur, a continuous compression state is recorded; when the compression magnitude of adjacent fault-driven effective maintenance intervals continues to increase, an accelerated compression state is recorded; when the adjacent fault-driven effective maintenance intervals show recovery changes, an interval recovery state is recorded. For example, the fault-driven effective maintenance intervals of M1 are 28 days, 24 days, and 18 days respectively. The adjacent intervals are first compressed by 4 days, then by 6 days. Due to continuous compression and an increasing compression magnitude, the system generates an accelerated compression state.

[0077] Continuous compression refers to a situation where, in a time-sequential sequence of fault-driven effective maintenance intervals, at least two consecutive comparisons show that the later fault-driven effective maintenance interval is shorter than the earlier one. A further increase in compression means that the number of days shortened between subsequent adjacent fault-driven effective maintenance intervals is greater than the number of days shortened between preceding adjacent fault-driven effective maintenance intervals. For example, if the fault-driven effective maintenance intervals are 28 days, 24 days, and 18 days respectively, and the first shortening is 4 days, and the second is 6 days, then this is recorded as an accelerated compression state.

[0078] The system also generates pre-maintenance anomaly types based on abnormal operating current, power factor, temperature, and vibration during the pre-maintenance observation period. The limits for abnormal operating current, power factor, temperature, and vibration are determined based on historical data from the effective load-bearing normal operation segments of the same type of asynchronous motor. First, abnormal segments, shutdown segments, and segments with missing samples are removed. Then, the normal concentration range boundaries of each operating data point are obtained, and these are combined with the equipment's rated parameters or maintenance specifications to form anomaly limits. For example, if the temperature of a similar asynchronous motor during normal effective load operation is concentrated between 54℃ and 68℃, and the maintenance specification requires the temperature not to exceed 75℃, then the temperature anomaly limit is set to 75℃. When the temperature consistently exceeds 75℃ during the pre-maintenance observation period, the temperature anomaly type is recorded.

[0079] The normal concentration range boundary is determined based on historical data from the effective load-carrying normal operation segments. First, abnormal segments and missing sampling segments in the observation period before maintenance are removed. Then, the remaining data are sorted by value, and the boundary values ​​at the beginning and end of the sort are taken as the normal concentration range. Finally, the more conservative side is taken as the abnormal limit value in combination with the equipment rated parameters and maintenance specifications. For example, if the normal temperature concentration range is 54℃ to 68℃, and the maintenance specifications require the temperature not to exceed 75℃, then the abnormal temperature limit value is taken as 75℃.

[0080] When multiple pre-maintenance anomaly types exist simultaneously, they are first sorted by duration, then by the magnitude of exceeding the corresponding anomaly limit, and the anomaly type ranked first is determined as the primary anomaly type. For example, if a temperature anomaly lasts for 4 hours, a vibration anomaly lasts for 2 hours, and a power factor anomaly lasts for 1 hour, then the temperature anomaly type can be determined as the primary anomaly type. When multiple pre-maintenance anomaly types have the same duration and exceed the corresponding anomaly limit by the same magnitude, the anomaly type directly corresponding to the maintenance cause identifier is selected first; if the maintenance cause identifier is missing, then the anomaly type that appears first during the pre-maintenance observation period is selected as the primary anomaly type; for example, if both temperature and vibration anomalies last for 4 hours and exceed the corresponding anomaly limit by the same magnitude, but the maintenance cause identifier is abnormal temperature rise maintenance, then the primary anomaly type is determined to be the temperature anomaly type. This step is used to determine the anomaly type that appears first during the pre-maintenance observation period.

[0081] In a supplementary implementation method, if an asynchronous motor has only one fault-driven valid maintenance interval, the system does not directly exclude it. Instead, it generates a replacement historical interval compression state based on the difference between the most recent valid on-load maintenance interval and the corresponding target maintenance interval, as well as the anomaly type before maintenance. For example, if the target maintenance interval for an asynchronous motor is 24 days, the most recent valid on-load maintenance interval is 20 days, and there is a power factor anomaly during the observation period before maintenance, the system generates a single compression state and uses the power factor anomaly type as the primary anomaly type. If there are no fault-driven valid maintenance intervals, and the operating data during the observation period before maintenance is normal, the system generates a stable maintenance state. Through this supplementary method, newly commissioned asynchronous motors, asynchronous motors with fewer maintenance records, and asynchronous motors with insufficient data can still participate in the subsequent asynchronous motor maintenance equipment set partitioning, and data processing results for subsequent grouping can continue to be generated even when the number of records is insufficient.

[0082] like Figure 4 As shown, in one specific embodiment, S3 includes:

[0083] S31. Divide the asynchronous motor maintenance equipment set according to historical interval compression status, main anomaly type and load linkage relationship data;

[0084] S32. Obtain the maintenance compression status value based on the effective load maintenance interval compression amount within the group, the number of asynchronous motors with main abnormality types, and the total number of asynchronous motors within the group;

[0085] S33. Generate a pre-maintenance interval sequence based on the maintenance compression state value and the representative life stage within the group.

[0086] In S31, the system sorts each asynchronous motor according to its historical interval compression status. The sorting order is as follows: accelerated compression status, continuous compression status, single compression status, stable maintenance status, and interval recovery status. Then, it sequentially determines whether the asynchronous motor to be grouped meets the grouping conditions compared to existing asynchronous motors in the current asynchronous motor maintenance equipment set. Grouping conditions include: effective load maintenance intervals at the same fault interval compression level; the same main anomaly type or belonging to the same anomaly category; a load linkage relationship; a relationship with the same driven object; a relationship within the same process section; or a historical simultaneous operation relationship.

[0087] The fault interval compression level is determined based on the difference between the most recent fault-driven effective maintenance interval and the corresponding target maintenance interval. When the most recent fault-driven effective maintenance interval has not reached the corresponding target maintenance interval, and the difference between this effective on-load maintenance interval and the corresponding target maintenance interval has not reached the median of historical fault-driven maintenance compression differences for the same type of asynchronous motor, and no continuous compression change has occurred, it is determined to be at the first fault interval compression level. When the most recent fault-driven effective maintenance interval has not reached the corresponding target maintenance interval and continuous compression change has occurred, it is determined to be at the second fault interval compression level. When the most recent fault-driven effective maintenance interval has not reached the corresponding target maintenance interval and accelerated compression has occurred, it is determined to be at the third fault interval compression level. When adjacent fault-driven effective maintenance intervals show a recovery change, the interval recovery state is retained and it participates in the aggregation judgment according to the first fault interval compression level.

[0088] The boundary for the fault interval compression level is determined based on the historical fault-driven maintenance compression difference of the same type of asynchronous motor. The system first calculates the difference between the target maintenance interval and the actual effective load-carrying maintenance interval in historical fault-driven maintenance events, then sorts the differences from smallest to largest, and uses the median as the boundary between the first and second fault interval compression levels; for example, if the historical compression difference is concentrated between 2 days and 8 days, and the median is 4 days, and the current compression difference is 3 days and not continuously compressed, then it is determined to be the first fault interval compression level.

[0089] The anomaly categories are determined based on the anomaly source corresponding to the main anomaly type. Operating current anomalies and power factor anomalies are categorized under electrical operating anomalies, temperature anomalies under thermal anomalies, and vibration anomalies under mechanical anomalies. When the maintenance cause is identified as protection trip maintenance, the corresponding maintenance event is categorized under electrical protection anomalies. If the main anomaly types of two asynchronous motors belong to the same anomaly category, they are considered to meet the criterion of "belonging to the same anomaly category."

[0090] When a motor to be grouped meets at least two grouping conditions with any existing asynchronous motor in the current asynchronous motor maintenance equipment set, the motor to be grouped is considered a candidate member of the current asynchronous motor maintenance equipment set. When a motor to be grouped matches multiple asynchronous motor maintenance equipment sets simultaneously, it is preferentially grouped into the asynchronous motor maintenance equipment set with the most met grouping conditions. If the number of met conditions is the same, it is preferentially grouped into the asynchronous motor maintenance equipment set ranked higher in historical interval compression status. When a motor to be grouped does not meet at least two grouping conditions with any existing asynchronous motor in the current asynchronous motor maintenance equipment set, a new asynchronous motor maintenance equipment set is created. Each asynchronous motor is grouped into only one asynchronous motor maintenance equipment set. For example, if M1, M2, and M3 are all in continuous compression or accelerated compression states, and the main anomaly types of M1 and M2 are both temperature anomalies, and they share the same driving object relationship, then all three can be grouped into the same asynchronous motor maintenance equipment set. This step ensures that the formation of maintenance equipment sets is based on changes in maintenance intervals, anomaly types, and load relationships simultaneously.

[0091] In S32, the system determines the degree of maintenance interval compression based on the normalized result of the effective load maintenance interval compression amount within the asynchronous motor maintenance equipment set relative to the standard maintenance interval; it determines the degree of anomaly concentration based on the normalized result of the number of asynchronous motors with major anomaly types within the asynchronous motor maintenance equipment set relative to the total number of asynchronous motors in the group; and it obtains the maintenance compression status value based on the degree of maintenance interval compression and the degree of anomaly concentration.

[0092] The effective load-carrying maintenance interval compression represents the time difference used to evaluate whether the maintenance interval of an asynchronous motor has been shortened. When there are at least two fault-driven effective maintenance intervals for the same asynchronous motor, the effective load-carrying maintenance interval compression is the difference between the last fault-driven effective maintenance interval in time sorting and its preceding fault-driven effective maintenance interval. When there is only one fault-driven effective maintenance interval for the same asynchronous motor, the effective load-carrying maintenance interval compression is the difference between that fault-driven effective maintenance interval and the corresponding target maintenance interval. When there are no fault-driven effective maintenance intervals, the effective load-carrying maintenance interval compression is zero. The effective load-carrying maintenance interval compression within a group represents the set of effective load-carrying maintenance interval compressions for all asynchronous motors within the same asynchronous motor maintenance equipment set. The smallest effective load-carrying maintenance interval compression represents the effective load-carrying maintenance interval compression with the highest compression degree within the same asynchronous motor maintenance equipment set. The number of asynchronous motors with a major anomaly type represents the number of asynchronous motors with a major anomaly type that have been generated in the k-th asynchronous motor maintenance equipment set. The total number of asynchronous motors within a group represents the total number of asynchronous motors in the k-th asynchronous motor maintenance equipment set. The maintenance compression state value represents a discrete state value generated based on the degree of compression of the effective load maintenance interval and the degree of anomaly concentration of the main anomaly type within the group, and is used to match the pre-maintenance interval rule table.

[0093] The compression status value is obtained by maintaining the following expression:

[0094]

[0095] in, This represents the maintenance compression status value of the k-th asynchronous motor maintenance equipment set; k represents the sequence number of the asynchronous motor maintenance equipment set. This represents the minimum effective load maintenance interval compression amount within the k-th asynchronous motor maintenance equipment set; Indicates the standard maintenance interval for asynchronous motors of the same type; This represents the normalized result of the effective on-load maintenance interval compression relative to the standard maintenance interval; This represents the number of asynchronous motors with a major fault type in the k-th asynchronous motor maintenance equipment set; This represents the total number of asynchronous motors in the k-th asynchronous motor maintenance equipment set; This indicates the degree of concentration of the main exception type within the group; This represents the floor function; This represents the function that takes the maximum value. represents the minimum value function; constant 3 is used to classify the effective load maintenance interval compression degree into discrete levels; constant 2 is used to classify the degree of anomaly concentration within the group into discrete levels.

[0096] The constant 1 in the expression ensures that the output still matches the rule table's status value even when there is no interval compression or no master exception type. The expression... Used to convert the degree of time compression to a boundary-constrained result between 0 and 1. In the expression... This step is used to indicate the degree of concentration of major anomaly types within a group. It generates a maintenance compression status value based on the maintenance interval compression rate of a single asynchronous motor and the concentration of major anomaly types within the group.

[0097] For example, a set of asynchronous motor maintenance equipment G1 includes M1, M2, and M3. M1's effective maintenance intervals for the two most recent fault-driven events are 24 days and 18 days, with an effective load-bearing maintenance interval compression of -6 days; M2's effective maintenance intervals for the two most recent fault-driven events are 23 days and 16 days, with an effective load-bearing maintenance interval compression of -7 days; M3's effective maintenance intervals for the two most recent fault-driven events are 21 days and 12 days, with an effective load-bearing maintenance interval compression of -9 days. The smallest effective load-bearing maintenance interval compression within this asynchronous motor maintenance equipment set is -9 days, which is the standard maintenance interval. The timeframe is 30 days. There are 3 asynchronous motors with the main fault type, and the total number of asynchronous motors in the group is 3. The system will... , , , Substitute the expression for maintaining the compression state value; where, , min(1,0.3)=0.3, max(0,0.3)=0.3, 3⋅0.3=0.9, 0.9 + 2 = 2.9, rounding down 2.9 gives 2. Therefore .

[0098] In a supplementary implementation, if a primary anomaly type exists in the asynchronous motor maintenance equipment set but the effective load-bearing maintenance interval compression data is insufficient, the effective load-bearing maintenance interval compression can be treated as zero when obtaining the maintenance compression status value, while retaining the state of insufficient effective maintenance intervals driven by faults. In this case, the time compression part of the expression will not generate an additional level, but the anomaly concentration part can still reflect the degree of anomaly concentration within the group. If there are no asynchronous motors with a primary anomaly type in the asynchronous motor maintenance equipment set, then... The abnormal concentration portion is zero. In this way, the expression can cover states such as accelerated compression, continuous compression, single compression, stable maintenance, interval recovery, and insufficient data.

[0099] In step S33, a pre-maintenance interval sequence is generated based on the maintenance compression status value and the representative life stage within the group. The representative life stage within the group is determined based on the life stage of each asynchronous motor in the asynchronous motor maintenance equipment set. When multiple life stages exist within a group, the life stage with the later ranking is selected as the representative life stage within the group. The life stages are ranked as follows: beyond-life operation stage, late life stage, mid-life stage, and early life stage. For example, if M1 in a certain asynchronous motor maintenance equipment set is in the late life stage, M2 is in the mid-life stage, and M3 is in the late life stage, then the representative life stage within the group is determined to be the late life stage.

[0100] The system retrieves the pre-maintenance interval sequence from the pre-maintenance interval rule table based on the maintenance compression status value, the representative life stage within the group, and the preset plan window. The preset plan window represents the future maintenance plan coverage duration used to generate the pre-maintenance interval sequence. The pre-maintenance interval rule table is jointly determined based on historical maintenance records of the same type of asynchronous motors, maintenance resource constraints, and production downtime window constraints. The system first reads the fault-driven effective maintenance interval, maintenance compression status value, maintenance resource occupancy, and production downtime window from the historical maintenance records of the same type of asynchronous motors. Then, it removes records with missing maintenance time events, insufficient operational data integrity, or missing maintenance result identifiers. Subsequently, it groups the devices according to the maintenance compression status value and life stage, counts the maintenance intervals that can be executed under the maintenance resource constraints and production downtime window constraints in each group, and forms the pre-maintenance interval rule table. For example, if the historical records show that the maintenance compression status value is 3 and the representative life stage within the group is the later life stage, and the maintenance resources allow for the maintenance of 3 asynchronous motors at a time and the production downtime window occurs every 18 to 20 days, subsequent fault downtimes are less frequent. In this case, the corresponding single pre-maintenance interval in the rule table can be 18 days.

[0101] Each record in the pre-maintenance interval rule table includes a maintenance compression status value, a representative life stage within the group, a single pre-maintenance interval, applicable maintenance resource constraints, and applicable production downtime window constraints. When generating the pre-maintenance interval sequence, the system uses the current maintenance compression status value and the representative life stage within the group as query fields to read the corresponding single pre-maintenance interval. If multiple matching records exist, the record with the same applicable maintenance resource constraints and current maintenance resource status is selected first. If multiple records still exist, the record with the shorter single pre-maintenance interval is selected. For example, if the current maintenance compression status value is 3, the representative life stage within the group is a late life stage, and the current maintenance resources allow for the maintenance of 3 asynchronous motors at a time, then the corresponding single pre-maintenance interval is read as 18 days.

[0102] The pre-maintenance interval sequence is generated based on the single pre-maintenance interval and the preset planning window. The system first reads the single pre-maintenance interval from the pre-maintenance interval rule table, and then repeats the single pre-maintenance interval within the preset planning window until adding another single pre-maintenance interval would exceed the preset planning window. If the cumulative duration equals the preset planning window, all generated pre-maintenance intervals are retained; if the cumulative duration is less than the preset planning window, no additional pre-maintenance intervals exceeding the preset planning window are generated. For example, if the single pre-maintenance interval is 18 days and the preset planning window is 90 days, the generated pre-maintenance interval sequence will be 18 days, 36 days, 54 days, 72 days, and 90 days.

[0103] The preset plan window is determined based on the enterprise's maintenance plan cycle and production scheduling cycle. The system reads the monthly maintenance plan cycle and quarterly maintenance plan cycle from the maintenance management system and the plan freeze cycle from the production scheduling system, and selects a time that can cover the current pre-maintenance schedule and will not exceed the confirmed range of the production plan as the preset plan window; for example, if the enterprise uses a 90-day quarterly maintenance plan cycle and the production scheduling system can confirm a downtime window of 100 days in the future, then the preset plan window can be 90 days.

[0104] The preset risk level is determined based on the correspondence between historical maintenance compression status values ​​and fault shutdown events. The system statistically analyzes the proportion of subsequent fault shutdowns, protection trips, or abnormal shutdowns under different maintenance compression status values. When the proportion of fault shutdowns corresponding to a certain maintenance compression status value is significantly higher than that of lower-level status values, that maintenance compression status value is determined as the preset risk level. For example, if there are 20 equipment groups with a maintenance compression status value of 1 and 1 fault shutdown, 18 equipment groups with a maintenance compression status value of 2 and 2 fault shutdowns, and 15 equipment groups with a maintenance compression status value of 3 and 6 fault shutdowns, then the preset risk level is 3.

[0105] like Figure 5 As shown, in one specific embodiment, S4 includes:

[0106] S41. Generate a pre-maintenance sequence based on the pre-maintenance interval sequence, the last maintenance end time of each asynchronous motor in the group, and the maintainable window.

[0107] S42. When the running constraint triggering condition is met, output the running constraint data before pre-maintenance;

[0108] S43. Output pre-maintenance timing and running data processing results.

[0109] In step S41, for the same set of asynchronous motor maintenance equipment, the system obtains the last maintenance end time of each asynchronous motor in the group, and obtains the next maintenance due time for each asynchronous motor based on the last maintenance end time and the first pre-maintenance interval in the pre-maintenance interval sequence. Then, the next maintenance due time of the motor with the earliest due time is determined as the first due time of the single motor in the group. For example, in the asynchronous motor maintenance equipment set G1, the last maintenance end time of M1 is day 0, the last maintenance end time of M2 is day 2, the last maintenance end time of M3 is day 1, and the first pre-maintenance interval in the pre-maintenance interval sequence is day 18. Therefore, the next maintenance due time of M1 is day 18, the next maintenance due time of M2 is day 20, the next maintenance due time of M3 is day 19, and the first due time of the single motor in the group is day 18.

[0110] The production stoppage window constraint is used to determine the feasibility of historical collaborative maintenance intervals when generating the pre-maintenance interval rule table; the maintainable window is used to determine the specific maintenance time point when generating the current pre-maintenance sequence. The system first uses the production stoppage window constraint to form the pre-maintenance interval rule table, and then reads the maintainable window from the current production schedule, without directly using historical production stoppage windows as the current maintainable window.

[0111] A maintainable window represents a time period within the current production schedule that simultaneously meets the requirements of permissible production downtime, available maintenance resources, available spare parts, and safe operating time. The maintainable window is determined based on the downtime plan, maintenance personnel schedule, spare parts availability, and safe operating time in the production scheduling system. Only a time period that simultaneously meets the requirements of permissible production downtime, available maintenance resources, available spare parts, and a window duration not less than the target maintenance duration is designated as a maintainable window. For example, if the production scheduling system specifies a downtime window from 09:00 to 13:00 on day 17, with available maintenance personnel and spare parts, and a target maintenance duration of 3 hours, then this time period can be considered a maintainable window.

[0112] The target maintenance duration is determined based on the average maintenance duration of similar maintenance events, the number of current maintenance items, and the spare parts replacement items. First, the average maintenance duration of similar maintenance events is obtained from historical maintenance events. Then, it is increased based on whether the current maintenance items include bearing inspection, winding insulation testing, wiring inspection, or spare parts replacement. For example, if the average duration of similar maintenance events is 2 hours, and the current maintenance items include winding insulation testing and bearing inspection, and the combined increase of these two items in the historical records is 1 hour, then the target maintenance duration is 3 hours.

[0113] The system determines whether a maintainable window exists that is no later than the due date of the earliest single-machine maintenance within the group. If such a window exists, it is designated as the unified pre-maintenance time point in the pre-maintenance sequence. For example, if the earliest single-machine maintenance due date is day 18, and the maintainable window is from 09:00 to 13:00 on day 17, then the unified pre-maintenance time point is determined to be 09:00 on day 17. If no maintainable window exists that is no later than the earliest single-machine maintenance due date, a separate pre-maintenance sequence is generated for the asynchronous motor with the earliest due date. For example, if the earliest single-machine maintenance due date is day 18, and the nearest maintainable window in the current production schedule is day 21, the system does not uniformly postpone all equipment to day 21, but instead generates a separate pre-maintenance sequence for the asynchronous motor due on day 18.

[0114] In a supplementary implementation method, if a unified pre-maintenance time point within the same asynchronous motor maintenance equipment set conflicts with the production critical task of a particular asynchronous motor, the system does not directly postpone the entire asynchronous motor maintenance equipment set. Instead, it first determines whether the asynchronous motor belongs to the group's first-due-out individual machines. If it does, a separate pre-maintenance sequence is generated; if it does not, a new maintenance time point is selected within the maintainable window no later than the next maintenance due date of the individual machine. The production critical task is determined based on the task priority, process continuity identifier, and downtime impact identifier in the production scheduling system. If an asynchronous motor undertakes a production task with a priority higher than the preset critical task level during the time period corresponding to the unified pre-maintenance time point, or if its downtime would cause an interruption of continuous production units, then the unified pre-maintenance time point is considered to conflict with the production critical task of the asynchronous motor. For example, if the production scheduling system marks M2 as performing a continuous conveying task from 14:00 to 18:00 on day 18, with a task priority of level one, and a downtime would affect subsequent sections, then this time period is considered to conflict with the production critical task of M2.

[0115] The preset critical task level is determined based on the enterprise's production scheduling rules and historical downtime impact records. The system first calculates the production delays, material scrap, or process interruptions caused by different task levels after downtime, and then selects the lowest task level that will cause continuous production unit interruption or exceed the allowable delay time as the preset critical task level; for example, if historical records show that a level 2 task downtime can be recovered within 1 hour, while a level 1 task downtime will cause continuous production interruption, then the preset critical task level is level 1.

[0116] In S42, when the operational constraint triggering conditions are met, pre-maintenance operational constraint data is output. Operational constraint triggering conditions include: the maintenance compression state value reaching a preset risk level; the main anomaly type within the asynchronous motor maintenance equipment set including temperature anomaly, vibration anomaly, or power factor anomaly; or the existence of a fault-driven effective maintenance interval within the asynchronous motor maintenance equipment set with the maintenance cause identified as protection trip maintenance. Pre-maintenance operational constraint data includes runtime limits for continuously exceeding high load ratio thresholds, suppression of no-load ineffective operation, priority transfer targets for transferable loads, target production window operation indicators, and pre-maintenance monitoring data items.

[0117] The runtime limit for continuously exceeding the high load ratio threshold is used to restrict the continuous high load operation of asynchronous motors before pre-maintenance. The high load ratio threshold represents the load ratio boundary used to trigger the continuous high load operation limit before pre-maintenance. The high load ratio threshold is determined based on the abnormal development records of similar asynchronous motors during the pre-maintenance observation period. First, the operating load ratio before the occurrence of temperature anomaly, vibration anomaly, or power factor anomaly is statistically analyzed. Then, the operating load ratio at which the frequency of anomaly occurrence begins to increase is selected as the high load ratio threshold. For example, if historical records show that the frequency of anomaly occurrence is low when the operating load ratio is below 80%, and the frequency of temperature anomaly occurrence increases after the operating load ratio reaches 85%, then the high load ratio threshold is set to 85%. For example, when the maintenance compression status value of a certain asynchronous motor maintenance equipment set reaches a preset risk level, and the main anomaly type is temperature anomaly, the system can output a runtime limit for continuously exceeding the high load ratio threshold, requiring that the asynchronous motor not operate at an operating load ratio exceeding 85% for more than 2 hours before pre-maintenance.

[0118] No-load invalid operation suppression is used to reduce the time that asynchronous motors are running but not carrying production load before pre-maintenance. The system identifies no-load invalid operation segments based on start / stop status data, operating current data, power factor data, and production task records, and outputs suppression instructions to the production scheduling system. For example, when an asynchronous motor is running but the production task record shows no material conveying task, and the operating current is lower than the no-load identified current or the power factor is lower than the no-load identified power factor, the system outputs a no-load invalid operation suppression instruction.

[0119] The priority transfer targets for transferable loads are determined based on load linkage data, the current operating load ratio, and maintenance compression status values. The system first filters candidate equipment sets from the load linkage data that have a load transfer relationship with the current asynchronous motor maintenance equipment set. Then, it excludes asynchronous motor maintenance equipment sets that have missing maintenance time events, have already generated separate pre-maintenance time sequences, or are in a state of insufficient operating data integrity. Subsequently, it sorts the candidate equipment sets from low to high according to the current operating load ratio and reads the rated power, current operating power, available operating time slots, and production scheduling permission for transfer for each candidate equipment set. When the remaining available power of a candidate equipment set can cover the load to be transferred, and the corresponding time slot allows load transfer, the candidate equipment set ranked first is determined as the priority transfer target for transferable loads.

[0120] Load-bearing capacity is determined based on the rated power, current operating power, remaining available power, available operating time, and production scheduling permission for transfer of the candidate equipment set. When the remaining available power of the candidate equipment set is not less than the power of the load to be transferred, and the candidate equipment set is not scheduled for maintenance or shutdown during the target time period, the candidate equipment set is deemed to have load-bearing capacity. For example, if the power of the load to be transferred is 4 kW, the remaining available power of the candidate equipment set is 6 kW, and the production scheduling allows for transfer, then the candidate equipment set has load-bearing capacity.

[0121] The target production window operation instruction is generated based on pre-maintenance timing and production scheduling data. The system selects production windows from the production plan prior to the pre-maintenance timing that have load demand changes below a preset load fluctuation limit and do not affect the unified pre-maintenance time. These production windows are designated as target production windows, and operation instructions are output. The preset load fluctuation limit is determined based on historical fluctuation data of the load demand curve in the production plan. First, the load demand change rate within the same type of production window is statistically analyzed, and then the upper limit representing a stable production state is selected as the preset load fluctuation limit. For example, if the load demand change rate within historically stable production windows is mostly between 5% and 8%, while the load demand change rate in frequently scheduled windows exceeds 15%, then the preset load fluctuation limit is set to 8%.

[0122] Pre-maintenance monitoring data items include operating current data, power factor data, temperature data, vibration data, start / stop status data, and protection action records. If the main anomaly type is temperature anomaly, temperature data and operating current data are prioritized for monitoring; if the main anomaly type is vibration anomaly, vibration data and operating current data are prioritized for monitoring; if the main anomaly type is power factor anomaly, power factor data and operating current data are prioritized for monitoring.

[0123] In S43, the system outputs pre-maintenance timing and operational data processing results. The pre-maintenance timing includes the asynchronous motor maintenance equipment set identifier, unified pre-maintenance time point, individual pre-maintenance timing, the due date of the first-to-expire single-machine maintenance within the group, and the source of the maintainable window. The operational data processing results include the effective load-carrying maintenance interval sequence, fault-driven effective maintenance interval, historical interval compression status, main anomaly type, maintenance compression status value, pre-maintenance interval sequence, and pre-maintenance operational constraint data. This output is used by the maintenance management system to generate maintenance work orders and by the production scheduling system to allocate load.

[0124] like Figure 7As shown, during the pre-maintenance timing generation and operational constraint output process, the system first reads the asynchronous motor maintenance equipment set identifier, the last maintenance end time of each asynchronous motor, the pre-maintenance interval sequence, and the maintainable window. Then, it generates the next maintenance due time for each asynchronous motor and uses the next maintenance due time of the motor with the earliest due time as the first due time of the single motor in the group. When there is a maintainable window that is no later than the first due time of the single motor in the group, a unified pre-maintenance timing point is output. When there is no maintainable window that is no later than the first due time of the single motor in the group, a separate pre-maintenance timing is output for the asynchronous motor with the earliest due time, and it continues to determine whether the operational constraint triggering condition is met. If it is met, the pre-maintenance operational constraint data is output synchronously.

[0125] like Figure 6 As shown, the present invention also provides an adaptive energy-saving control and operation data processing system for asynchronous motors, including a management platform. The management platform includes a data acquisition module, an effective load interval generation module, a fault drive interval confirmation module, a maintenance equipment set partitioning module, a pre-maintenance timing generation module, and an operation constraint output module.

[0126] The data acquisition module is used to acquire historical operating data sequences, maintenance event data, and load linkage relationship data for multiple asynchronous motors of the same type. Historical operating data sequences include operating current data, operating voltage data, power factor data, temperature data, vibration data, start / stop status data, sampling time data, and asynchronous motor identification. Maintenance event data includes maintenance start time, maintenance end time, maintenance reason identifier, maintenance result identifier, and maintenance event type. Load linkage relationship data includes relationships within the same driven object, relationships within the same process section, historical simultaneous operation relationships, and load transfer relationships.

[0127] The effective load interval generation module is used to identify effective load-bearing operation segments based on start / stop status data, operating current data, and power factor data, and to generate an effective load-bearing maintenance interval sequence based on the effective load-bearing operation segments between adjacent maintenance events. The effective load interval generation module is also used to obtain the no-load identification current and no-load identification power factor. When the start / stop status data corresponding to the target sampling segment indicates that the asynchronous motor is in operation, the operating current data exceeds the no-load identification current, and the power factor data exceeds the no-load identification power factor, the target sampling segment is determined as an effective load-bearing operation segment.

[0128] The fault-driven interval confirmation module is used to obtain the target maintenance interval, filter the fault-driven valid maintenance intervals, and generate historical interval compression status and main anomaly types. The fault-driven interval confirmation module is also used to determine the lifespan stage based on the effective cumulative load-bearing time and design service life, and to obtain the target maintenance interval from the target maintenance interval rule table according to the lifespan stage. When the effective load-bearing maintenance interval has not reached the corresponding target maintenance interval, and the maintenance reason is identified as post-fault maintenance, abnormal temperature rise maintenance, abnormal vibration maintenance, protection trip maintenance, power factor abnormal maintenance, current abnormal maintenance, or abnormal state triggered maintenance, the effective load-bearing maintenance interval is determined as the fault-driven valid maintenance interval.

[0129] The maintenance equipment set partitioning module is used to partition asynchronous motor maintenance equipment sets based on historical interval compression status, main anomaly type, and load linkage relationship data, obtain maintenance compression status values, and generate a pre-maintenance interval sequence. The module also adds the asynchronous motor to be partitioned to the current asynchronous motor maintenance equipment set if it meets at least two of the following conditions: the asynchronous motor to be partitioned is at the same fault interval compression level, the main anomaly type is the same or belongs to the same anomaly category, there is a load linkage relationship, there is a relationship with the same drive object, there is a relationship with the same process section, or there is a historical simultaneous operation relationship.

[0130] The pre-maintenance sequence generation module is used to generate a pre-maintenance sequence no later than the earliest due date of individual maintenance for each asynchronous motor in the group, based on the pre-maintenance interval sequence, the last maintenance end time of each asynchronous motor in the group, and the maintainable window. The pre-maintenance sequence generation module is also used to obtain the next maintenance due date of each asynchronous motor in the same asynchronous motor maintenance equipment set, and determine the next maintenance due date of the motor with the earliest due date as the earliest due date of individual maintenance in the group; when there is a maintainable window no later than the earliest due date of individual maintenance in the group, the maintainable window is determined as the unified pre-maintenance time point in the pre-maintenance sequence; when there is no maintainable window no later than the earliest due date of individual maintenance in the group, a separate pre-maintenance sequence is generated for the asynchronous motor with the earliest due date.

[0131] The operation constraint output module is used to output pre-maintenance operation constraint data when the operation constraint triggering conditions are met. The operation constraint output module is also used to output runtime limits for continuous exceeding of high load ratio thresholds, suppression of ineffective idle operation, priority transfer objects for transferable loads, target production window operation indicators, and pre-maintenance monitoring data items.

[0132] In a specific application scenario, a conveyor section includes five identical asynchronous motors, numbered M1, M2, M3, M4, and M5. Each motor has a rated power of 15 kW, a rated voltage of 380 V, a rated current of 30 A, a standard maintenance interval (Ds) of 30 days, and a designed service life of 1800 days. The system acquires historical operating data sequences from the most recent 180 days, with a sampling segment length of 10 minutes, and also obtains maintenance event data and load linkage data.

[0133] The system first determines the no-load identification current as 7.0 A based on the no-load test run records. Based on the no-load operating power factor range of 0.18 to 0.26 and the light-load operating power factor range of 0.32 to 0.45, the no-load identification power factor is determined to be 0.30. When the start / stop state corresponding to the sampling segment is running, the operating current is greater than 7.0 A, and the power factor is greater than 0.30, the sampling segment is counted as a valid load-bearing operating segment. For M1, the calendar interval between two maintenance operations is 30 days, including 3 days of downtime, 0.5 days of maintenance waiting time, 2 days of no-load invalid operating time, 1 day of sampling missing time, and an effective load-bearing maintenance interval of 23.5 days.

[0134] Based on M1's effective load-bearing cumulative runtime of 1350 days and designed service life of 1800 days, the system determines that M1 is in the late stage of its service life and reads the corresponding target maintenance interval of 24 days from the target maintenance interval rule table. Since M1's effective load-bearing maintenance interval of 23.5 days does not reach the target maintenance interval of 24 days, and the maintenance reason is identified as abnormal temperature rise maintenance, the system determines this effective load-bearing maintenance interval as a fault-driven effective maintenance interval. If the maintenance reason identifier for M2 is missing, but three consecutive temperature data sampling segments exceed the abnormal temperature limit during the observation period before maintenance, the system also determines M2's corresponding effective load-bearing maintenance interval as a fault-driven effective maintenance interval.

[0135] The system compares the effective maintenance intervals of fault drives for M1, M2, and M3 in chronological order, determining that M1 is in continuous compression, M2 is in accelerated compression, and M3 is in continuous compression. After sorting the anomaly types before maintenance, the primary anomaly type for M1, M2, and M3 is temperature anomaly. Because the three motors share the same driving object, have a history of simultaneous operation, and have the same primary anomaly type, the system assigns M1, M2, and M3 to the asynchronous motor maintenance equipment set G1.

[0136] For asynchronous motor maintenance equipment set G1, the effective load-bearing maintenance interval reduction for M1 is -6 days, for M2 it is -7 days, and for M3 it is -9 days. Therefore... ; , , The system obtains the value based on the maintenance compression status expression. Subsequently, the system maintains the compression status value. The representative life stage within the group is the later life stage. The single pre-maintenance interval of 18 days is read from the pre-maintenance interval rule table, and the pre-maintenance interval sequence of 18 days, 36 days, 54 days, 72 days and 90 days is generated within the preset planning window of 90 days.

[0137] If the last maintenance end times for M1, M2, and M3 are day 0, day 2, and day 1, respectively, then the next maintenance due dates for each machine are day 18, day 20, and day 19, respectively. The maintenance due date for the first machine in the group is day 18. If a maintainable window exists in the current production schedule from 09:00 to 13:00 on day 17, the system will determine 09:00 on day 17 as the unified pre-maintenance time point. If no maintainable window exists later than day 18, the system will generate a separate pre-maintenance sequence for M1, without postponing the maintenance of M1, M2, and M3 all to after day 18.

[0138] Due to maintaining compression status values If the preset risk level is reached and the main anomaly type is temperature anomaly, the system outputs pre-maintenance operation constraint data, including: the duration of continuous operation exceeding the 85% high load ratio threshold shall not exceed 2 hours; identify and suppress no-load invalid operation segments; prioritize the transfer of transferable loads to a set of candidate equipment with load-bearing capacity that are not in a pre-maintenance state; take the production window with load fluctuation not exceeding 8% as the target production window; and take temperature data and operating current data as priority monitoring data items.

[0139] In this specific application scenario, the system first filters valid load-bearing operation segments from historical operational data sequences, then filters fault-driven valid maintenance intervals from maintenance event data. Next, it constructs an asynchronous motor maintenance equipment set based on historical interval compression status, main anomaly types, and load linkage data, and generates a pre-maintenance interval sequence based on the maintenance compression status value. When generating the pre-maintenance sequence, the system uses the maintenance expiration time of the earliest expiring single machine within the group as a constraint, preventing the earliest expiring asynchronous motors within the group from being postponed to maintenance windows that do not meet their individual expiration requirements. Pre-maintenance operational constraint data is used to constrain high-load continuous operation, no-load invalid operation, transferable loads, and monitoring data items before maintenance execution.

[0140] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0141] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0142] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for adaptive energy-saving control and operation data processing of asynchronous motors, characterized in that, The methods include: Acquire historical operating data sequences, maintenance event data, and load linkage relationship data of multiple asynchronous motors of the same type. Identify effective load-bearing operating segments based on start-stop status data, operating current data, and power factor data. Generate an effective load-bearing maintenance interval sequence based on the effective load-bearing operating segments between adjacent maintenance events. Based on the standard maintenance interval, design service life, effective load-carrying cumulative running time, and pre-maintenance observation period running data, obtain the target maintenance interval corresponding to each effective load-carrying maintenance interval, and filter the fault-driven effective maintenance intervals based on the effective load-carrying maintenance interval, target maintenance interval, maintenance reason identifier, and pre-maintenance observation period running data, and generate historical interval compression status and main anomaly type; The asynchronous motor maintenance equipment set is divided according to the historical interval compression status, main anomaly type and load linkage relationship data. The maintenance compression status value and pre-maintenance interval sequence are obtained according to the effective load maintenance interval compression amount in the group, the number of asynchronous motors with main anomaly type and the total number of asynchronous motors in the group. Based on the pre-maintenance interval sequence, the last maintenance end time of each asynchronous motor in the group, and the maintainable window, a pre-maintenance timing sequence is generated that is no later than the maintenance expiration time of the first single machine in the group. When the operation constraint triggering condition is met, the pre-maintenance operation constraint data is output. The pre-maintenance operation constraint data includes the running time limit for continuously exceeding the high load ratio threshold, the suppression of no-load invalid operation, the priority transfer object of transferable load, the target production window operation indication, and the pre-maintenance monitoring data items.

2. The asynchronous motor adaptive energy-saving control and operation data processing method according to claim 1, characterized in that, Based on start / stop status data, operating current data, and power factor data, valid load-carrying operating segments are identified, including: Obtain the no-load identification current and no-load identification power factor of the same type of asynchronous motor; wherein, the no-load identification current is determined based on the no-load test run current of the same type of asynchronous motor, and the no-load identification power factor is determined based on the no-load operation data and light-load operation data of the same type of asynchronous motor. When the start / stop status data corresponding to the target sampling segment indicates that the asynchronous motor is in operation, the operating current data corresponding to the target sampling segment exceeds the no-load identification current, and the power factor data corresponding to the target sampling segment exceeds the no-load identification power factor, the target sampling segment is determined as a valid load-carrying operation segment. If the start / stop status data corresponding to the target sampling segment indicates that the asynchronous motor is in a stopped or maintenance stopped state, or if the operating current data corresponding to the target sampling segment does not exceed the no-load identification current, or if the power factor data corresponding to the target sampling segment does not exceed the no-load identification power factor, the target sampling segment will not be determined as a valid load-carrying operating segment.

3. The asynchronous motor adaptive energy-saving control and operation data processing method according to claim 1, characterized in that, A sequence of effective load-carrying maintenance intervals is generated based on the effective load-carrying operation segments between adjacent maintenance events, including: Maintenance event data for the same asynchronous motor are sorted according to the maintenance start time. The maintenance end time of the first maintenance event in two adjacent maintenance events is taken as the start time of the effective load maintenance interval, and the maintenance start time of the second maintenance event in two adjacent maintenance events is taken as the end time of the effective load maintenance interval. Effective load-bearing operation segments are extracted between the start and end points of the interval, and the effective load-bearing maintenance interval is obtained by accumulating the sampling time data corresponding to the effective load-bearing operation segments. When maintenance event data lacks a maintenance end time, the maintenance end time is supplemented based on the average maintenance duration of similar maintenance events. When maintenance event data lacks both a maintenance start time and a maintenance end time, the corresponding maintenance event is marked as a maintenance time missing event, and the corresponding maintenance event is prohibited from participating in the generation of effective load maintenance intervals.

4. The asynchronous motor adaptive energy-saving control and operation data processing method according to claim 1, characterized in that, Based on standard maintenance intervals, design service life, effective cumulative load running time, and pre-maintenance observation data, obtain the target maintenance intervals corresponding to each effective load maintenance interval, including: The effective load-carrying cumulative running time is obtained based on the effective load-carrying running segments of the same asynchronous motor from the commissioning time to the start of the effective load-carrying maintenance interval; Based on the effective cumulative running time under load and the design service life, the life stage of the asynchronous motor at the starting point of the corresponding effective maintenance interval is determined; the life stage includes the initial life stage, the middle life stage, the late life stage, and the over-life operation stage. The target maintenance interval compression level is obtained from the target maintenance interval rule table based on the life stage, and the target maintenance interval is generated based on the standard maintenance interval and the target maintenance interval compression level. The target maintenance interval rule table is determined based on the historical periodic maintenance records of the same type of asynchronous motor in different effective load cumulative running time intervals. Each record in the target maintenance interval rule table includes at least the life stage, the target maintenance interval compression level, and the target maintenance interval.

5. The asynchronous motor adaptive energy-saving control and operation data processing method according to claim 1, characterized in that, Fault-driven effective maintenance intervals are selected based on effective load maintenance intervals, target maintenance intervals, maintenance reason identifiers, and operational data during the pre-maintenance observation period, including: Determine whether the effective on-load maintenance interval to be judged has not reached the corresponding target maintenance interval; When the effective on-load maintenance interval to be judged has not reached the corresponding target maintenance interval, and the maintenance reason is identified as post-fault maintenance, abnormal temperature rise maintenance, abnormal vibration maintenance, protection trip maintenance, power factor abnormal maintenance, current abnormal maintenance, or abnormal state triggered maintenance, the effective on-load maintenance interval to be judged will be determined as the fault-driven effective maintenance interval. When the effective on-load maintenance interval to be determined has not reached the corresponding target maintenance interval and the maintenance reason identifier is missing, the operating data of the observation period before the maintenance start time is used to determine whether there is abnormal operating current, abnormal power factor, abnormal temperature, or abnormal vibration. If at least one of the abnormal operating current, abnormal power factor, abnormal temperature, or abnormal vibration exists, the effective on-load maintenance interval to be determined is determined as a fault-driven effective maintenance interval. If the maintenance reason identifier is planned shutdown maintenance and the operating data of the observation period before maintenance does not show abnormal operating current, abnormal power factor, abnormal temperature, or abnormal vibration, the effective on-load maintenance interval to be determined is not determined as a fault-driven effective maintenance interval.

6. The method for adaptive energy-saving control and operation data processing of asynchronous motors according to claim 1, characterized in that, Generate historical interval compressed status and major exception type, including: Arrange the effective maintenance intervals of the same asynchronous motor driven by faults in chronological order, and compare the changes of adjacent effective maintenance intervals driven by faults in turn. When the effective maintenance interval driven by the current and adjacent faults shows a compression change, but does not form a continuous compression state or an accelerated compression state, record the single compression state; when the compression change occurs continuously, record the continuous compression state; when the compression magnitude of the effective maintenance interval driven by the adjacent faults continues to increase, record the accelerated compression state; when the effective maintenance interval driven by the current and adjacent faults shows a recovery change, record the interval recovery state. Anomaly types before maintenance are generated based on abnormal operating current, power factor, temperature, and vibration during the observation period before maintenance. When multiple anomaly types exist simultaneously, they are first sorted by the duration of the anomaly, then by the magnitude of the anomaly exceeding the corresponding anomaly limit, and the anomaly type ranked first is determined as the main anomaly type. When there are fewer than two valid maintenance intervals driven by faults, a replacement historical interval compression state is generated based on the difference between the most recent valid on-load maintenance interval and the corresponding target maintenance interval, as well as the type of abnormality before maintenance. When there are no valid maintenance intervals driven by faults for the same asynchronous motor, and the operating data during the observation period before the most recent maintenance does not show abnormal operating current, abnormal power factor, abnormal temperature, or abnormal vibration, a stable maintenance state is recorded.

7. The asynchronous motor adaptive energy-saving control and operation data processing method according to claim 1, characterized in that, The asynchronous motor maintenance equipment set is divided based on historical interval compression status, main anomaly type, and load linkage data, including: The asynchronous motors are sorted according to their historical interval compression states, in the following order: accelerated compression state, continuous compression state, single compression state, stable maintenance state, and interval recovery state. The system sequentially determines whether the asynchronous motor to be aggregated meets the aggregation conditions of the asynchronous motors already in the current asynchronous motor maintenance equipment set. The aggregation conditions include: the effective load maintenance interval is at the same fault interval compression level, the main anomaly type is the same or belongs to the same anomaly category, there is a load linkage relationship, there is a relationship with the same driving object, there is a relationship with the same process section, or there is a historical simultaneous operation relationship. When the asynchronous motor to be grouped meets at least two grouping conditions with any existing asynchronous motor in the current asynchronous motor maintenance equipment set, the asynchronous motor to be grouped is considered a candidate member of the current asynchronous motor maintenance equipment set. When the asynchronous motor to be grouped matches multiple asynchronous motor maintenance equipment sets simultaneously, it is preferentially grouped into the asynchronous motor maintenance equipment set with the most met grouping conditions. If the number of met grouping conditions is the same, it is preferentially grouped into the asynchronous motor maintenance equipment set ranked earlier in the historical interval compression state. When the asynchronous motor to be grouped does not meet at least two grouping conditions with any existing asynchronous motor in the current asynchronous motor maintenance equipment set, a new asynchronous motor maintenance equipment set is created. Each asynchronous motor is only grouped into one asynchronous motor maintenance equipment set.

8. The asynchronous motor adaptive energy-saving control and operation data processing method according to claim 1, characterized in that, Obtain maintenance compression status values ​​and pre-maintenance interval sequences, and output pre-maintenance running constraint data, including: The degree of maintenance interval compression is determined based on the normalized result of the effective load-bearing maintenance interval compression within the asynchronous motor maintenance equipment set relative to the standard maintenance interval; the degree of anomaly concentration is determined based on the normalized result of the number of asynchronous motors with major anomaly types within the asynchronous motor maintenance equipment set relative to the total number of asynchronous motors in the group; and the maintenance compression status value is obtained based on the degree of maintenance interval compression and the degree of anomaly concentration. Based on the maintenance compression status value, the representative life stage within the group, and the preset planning window, the pre-maintenance interval sequence is obtained from the pre-maintenance interval rule table. The pre-maintenance interval rule table is determined based on the historical maintenance records of asynchronous motors of the same type, maintenance resource constraints, and production shutdown window constraints. Each record in the pre-maintenance interval rule table includes at least the maintenance compression status value, the representative life stage within the group, the single pre-maintenance interval, the applicable maintenance resource constraints, and the applicable production shutdown window constraints. When the maintenance compression status value reaches the preset risk level, or the main anomaly type in the asynchronous motor maintenance equipment set includes temperature anomaly, vibration anomaly, or power factor anomaly, or there is a fault-driven effective maintenance interval in the asynchronous motor maintenance equipment set with the maintenance reason identified as protection trip maintenance, it is determined that the asynchronous motor maintenance equipment set meets the operation constraint triggering condition, and pre-maintenance operation constraint data is generated; wherein, the pre-maintenance operation constraint data includes the running time limit for continuously exceeding the high load ratio threshold, the suppression of no-load invalid operation, the priority transfer object of transferable load, the target production window operation indication, and pre-maintenance monitoring data items.

9. An adaptive energy-saving control and operation data processing system for asynchronous motors, characterized in that, The management platform includes a data acquisition module, an effective load interval generation module, a fault-driven interval confirmation module, a maintenance equipment set partitioning module, a pre-maintenance timing generation module, and an operation constraint output module. The data acquisition module is used to acquire historical operating data sequences, maintenance event data, and load linkage relationship data of multiple asynchronous motors of the same type; The effective load interval generation module is used to identify effective load operation segments based on start-stop status data, operating current data, and power factor data, and to generate an effective load maintenance interval sequence based on the effective load operation segments between adjacent maintenance events. The fault-driven interval confirmation module is used to obtain the target maintenance interval, filter the valid fault-driven maintenance intervals, and generate the historical interval compression status and main anomaly type. The maintenance equipment set partitioning module is used to partition the asynchronous motor maintenance equipment set, obtain the maintenance compression status value, and generate a pre-maintenance interval sequence. The pre-maintenance sequence generation module is used to generate a pre-maintenance sequence no later than the maintenance expiration time of the first machine in the group, based on the pre-maintenance interval sequence, the last maintenance end time of each asynchronous motor in the group, and the maintainable window. The runtime constraint output module is used to output runtime constraint data before pre-maintenance when the runtime constraint triggering conditions are met.

10. The asynchronous motor adaptive energy-saving control and operation data processing system according to claim 9, characterized in that: The effective load interval generation module is also used to determine the target sampling segment as an effective load running segment when the start-stop status data corresponding to the target sampling segment indicates that the asynchronous motor is in the running state, the running current data corresponding to the target sampling segment exceeds the no-load identification current, and the power factor data corresponding to the target sampling segment exceeds the no-load identification power factor. The fault-driven interval confirmation module is also used to determine the effective on-load maintenance interval as the fault-driven effective maintenance interval when the effective on-load maintenance interval has not reached the corresponding target maintenance interval and the maintenance reason is identified as post-fault maintenance, abnormal temperature rise maintenance, abnormal vibration maintenance, protection trip maintenance, power factor abnormal maintenance, current abnormal maintenance or abnormal state triggered maintenance. The maintenance equipment set division module is also used to add the asynchronous motor to be collected to the current asynchronous motor maintenance equipment set when the asynchronous motor to be collected meets at least two of the following conditions: the effective load maintenance interval is at the same fault interval compression level, the main anomaly type is the same or belongs to the same anomaly category, there is a load linkage relationship, there is a relationship with the same driving object, there is a relationship with the same process section, or there is a historical simultaneous operation relationship. The pre-maintenance sequence generation module is also used to obtain the next maintenance due time of each asynchronous motor in the same asynchronous motor maintenance equipment set, and determine the next maintenance due time of the single motor with the earliest due time as the first maintenance due time of the single motor in the group; when there is a maintainable window no later than the first maintenance due time of the single motor in the group, the maintainable window is determined as the unified pre-maintenance time point in the pre-maintenance sequence; when there is no maintainable window no later than the first maintenance due time of the single motor in the group, a separate pre-maintenance sequence is generated for the asynchronous motor with the earliest due time. The runtime constraint output module is also used to output runtime limits for continuous high load ratio thresholds, suppression of idle and invalid operation, priority transfer objects for transferable loads, target production window operation indicators, and pre-maintenance monitoring data items.

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