A real-time calculation and analysis method and system for overall equipment effectiveness (OEE)

Abstract

The application relates to the technical field of control systems and discloses a real-time calculation and analysis method and system for equipment comprehensive efficiency OEE, the method comprising the following steps: collecting the running data of equipment in real time, including equipment running time, planned running time and theoretical cycle time; performing exponential weighted fusion on the multiple-source data of the equipment running time, the planned running time and the theoretical cycle time to obtain a time opening rate; based on the time opening rate and the real-time performance opening rate of the equipment, performing dynamic coupling analysis on the running efficiency index of the equipment to obtain a comprehensive efficiency OEE value; performing real-time state analysis on the comprehensive efficiency OEE value to obtain efficiency analysis results of the equipment; based on the efficiency analysis results, optimizing efficiency parameters of the equipment to obtain optimized running configurations; based on the optimized running configurations, evaluating the effect of the running parameters of the equipment to obtain a final efficiency optimization report; and the application can improve the accuracy of real-time calculation and analysis of the equipment comprehensive efficiency OEE.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of control system, and in particular to a real-time calculation and analysis method and system for overall equipment effectiveness OEE. BACKGROUND

[0002] The prior art lacks systematic optimization in the collection and processing of device operation data in the calculation of overall equipment effectiveness OEE (global equipment efficiency). It only simply obtains raw data such as device operation time, planned operation time, and theoretical cycle time, does not perform noise identification filtering on the effective production period, boundary calibration correction on the planned production period, and does not perform normalization benchmarking on the standard single-piece processing time, resulting in mixed interference information in the raw data, boundary deviation, and dimension inconsistency problems, which cannot provide accurate and standardized basic data for subsequent OEE calculation, affecting the accuracy of the time utilization rate calculation.

[0003] The prior art has obvious deficiencies in OEE value calculation and subsequent analysis optimization. It does not establish a dynamic coupling mechanism for time utilization rate and real-time performance utilization rate, only simply superimposes or synthesizes the overall equipment effectiveness OEE value by a fixed proportion, cannot reflect the correlation of the dynamic changes of the two, and leads to a large deviation between the OEE value and the actual operation efficiency of the device. At the same time, it lacks a systematic state analysis and parameter optimization process based on the OEE value, cannot accurately identify the key influencing factors of efficiency anomalies, cannot develop targeted multi-level adjustment strategies, and cannot simulate and evaluate the optimization configuration, making it difficult to form a complete efficiency improvement closed loop and unable to meet the needs of efficient operation and continuous improvement of the device. SUMMARY

[0004] The present application provides a real-time calculation and analysis method and system for overall equipment effectiveness OEE to solve the problems raised in the background art.

[0005] To achieve the above-mentioned purpose, the present application provides a real-time calculation and analysis method for overall equipment effectiveness OEE, comprising:

[0006] S1. Real-time collection of device operation data, wherein the operation data includes device operation time, planned operation time, and theoretical cycle time;

[0007] S2. Exponential weighted fusion of multi-source data for the device operation time, the planned operation time, and the theoretical cycle time to obtain the time utilization rate of the operation data;

[0008] S3. Dynamic coupling analysis of the operation efficiency index of the device based on the time utilization rate and the real-time performance utilization rate of the device to obtain the overall equipment effectiveness OEE value of the device;

[0009] S4. performing real-time state analysis on the comprehensive efficiency OEE value to obtain an efficiency analysis result of the equipment;

[0010] S5. performing efficiency parameter optimization on the equipment based on the efficiency analysis result to obtain an optimized operation configuration of the equipment;

[0011] S6. performing effect evaluation on the operation parameters of the equipment based on the optimized operation configuration to obtain a final efficiency optimization report of the equipment.

[0012] In a preferred embodiment, the equipment running time, the planned running time and the theoretical cycle time in the real-time collected equipment running data comprise:

[0013] collecting original time series data representing the equipment running state in the equipment control system;

[0014] performing noise identification filtering on the effective production period of the equipment running time in the original time series data to obtain the effective production period of the original time series data;

[0015] performing boundary calibration correction on the planned production period of the planned running time in the original time series data to obtain the planned production period of the original time series data;

[0016] performing normalization benchmarking on the standard single-piece processing time of the theoretical cycle time in the original time series data to obtain the standard single-piece processing time of the original time series data.

[0017] In a preferred embodiment, the exponential weighted fusion of the equipment running time, the planned running time and the theoretical cycle time comprises:

[0018] performing corresponding dynamic weight distribution on the equipment running time, the planned running time and the theoretical cycle time to obtain a running time influence factor of the equipment running time, a planned execution weight coefficient of the planned running time and a theoretical benchmark parameter proportion of the theoretical cycle time;

[0019] performing exponential weighted fusion on the equipment running time, the planned running time and the theoretical cycle time according to the running time influence factor, the planned execution weight coefficient and the theoretical benchmark parameter proportion to obtain a preliminary open rate value of the equipment, wherein the calculation formula of the preliminary open rate value is as follows:

[0020]

[0021] wherein, is the preliminary open rate value, , , is the adjustment coefficient, is the device running time, is the planned running time, is the theoretical cycle time, is the running time influence factor, is the planned execution weight coefficient, is the theoretical reference parameter proportion, is the device time utilization rate, is the cycle time efficiency ratio, is the planned capacity coverage rate;

[0022] The preliminary initial rate value is normalized to obtain the time initial rate of the running data.

[0023] In a preferred embodiment, based on the time initial rate and the real-time performance initial rate of the device, the running efficiency index of the device is dynamically coupled and analyzed to obtain the comprehensive efficiency OEE value of the device, including:

[0024] According to the dynamic change trend of the time initial rate and the real-time performance initial rate, the coupling relationship coefficient between the two is determined;

[0025] Based on the coupling relationship coefficient, a dynamic compensation mechanism of the time initial rate and the real-time performance initial rate is constructed;

[0026] Based on the dynamic compensation mechanism, the time initial rate and the real-time performance initial rate are optimized to obtain the optimized time initial rate and performance initial rate;

[0027] The optimized time initial rate and performance initial rate are dynamically balanced and synthesized to obtain the comprehensive efficiency OEE value of the device.

[0028] In a preferred embodiment, the optimized time initial rate and performance initial rate are dynamically balanced and synthesized to obtain the comprehensive efficiency OEE value of the device, including:

[0029] According to the real-time change characteristics of the optimized time initial rate and performance initial rate, the dynamic weight of the optimized time initial rate and performance initial rate in the comprehensive efficiency evaluation is determined;

[0030] The dynamic weight of the optimized time initial rate and performance initial rate is weighted and fused to obtain the weighted time initial rate and weighted performance initial rate;

[0031] linearly fusing the weighted time opening rate and the weighted performance opening rate to obtain an overall efficiency OEE value of the device, wherein a calculation formula of the overall efficiency OEE value is as follows:

[0032]

[0033] In the formula, the overall efficiency OEE value, a synthesis coefficient of the weighted time opening rate, a synthesis coefficient of the weighted performance opening rate, the weighted time opening rate, the weighted performance opening rate.

[0034] In a preferred embodiment, the real-time state analysis is performed on the overall efficiency OEE value to obtain an efficiency analysis result of the device, including:

[0035] comparing the overall efficiency OEE value with a preset efficiency threshold interval to obtain an efficiency deviation state of the overall efficiency OEE value;

[0036] based on the efficiency deviation state, performing abnormal category identification on real-time running parameters of the device to obtain a parameter abnormal category of the device;

[0037] performing extraction analysis on the parameter abnormal category to obtain a key efficiency influencing factor of the parameter abnormal category;

[0038] performing efficiency rationality analysis on the key efficiency influencing factor to obtain the efficiency analysis result of the device.

[0039] In a preferred embodiment, based on the efficiency analysis result, the efficiency parameter optimization is performed on the device to obtain an optimized running configuration of the device, including:

[0040] based on the efficiency analysis result, identifying an efficiency optimization parameter of the device to be optimized to obtain the efficiency optimization parameter of the device;

[0041] performing parameter adjustment strategy on the efficiency optimization parameter to obtain a multi-level adjustment strategy of the device;

[0042] based on the multi-level adjustment strategy, performing configuration optimization on controllable running parameters of the device to obtain the optimized running configuration of the device.

[0043] In a preferred embodiment, based on the optimized running configuration, the running parameter effect evaluation is performed on the device to obtain a final efficiency optimization report of the device, including:

[0044] Based on the optimized operation configuration, the running parameters of the device are simulated and adjusted to obtain simulation running data of the device;

[0045] The simulation running data is compared and analyzed with the historical running data to obtain an optimization effect evaluation result of the device;

[0046] According to the optimization effect evaluation result, the final efficiency optimization report is determined.

[0047] In a preferred embodiment, the simulation running data is compared and analyzed with the historical running data to obtain an optimization effect evaluation result of the device, comprising:

[0048] Based on a preset comparison rule, corresponding key indicators are extracted from the simulation running data and the historical running data respectively to obtain simulation key indicators of the simulation running data and historical key indicators of the historical running data;

[0049] The simulation key indicators are simulated and production characteristic quantities are constructed to obtain production characteristic quantities of the simulation key indicators;

[0050] The historical key indicators are analyzed to obtain historical running benchmark quantities of the historical key indicators;

[0051] According to the degree of coincidence between the production characteristic quantities and the historical running benchmark quantities, the optimization effect of the optimized operation configuration on the efficiency of the device is evaluated to obtain the optimization effect evaluation result.

[0052] In order to solve the above problems, the present application also provides a real-time calculation and analysis system for comprehensive efficiency OEE of a device, comprising:

[0053] A data acquisition module is configured to acquire running data of a device in real time, wherein the running data comprises device running time, planned running time and theoretical cycle time;

[0054] A data fusion module is configured to perform exponential weighted fusion of multi-source data on the device running time, the planned running time and the theoretical cycle time to obtain a time opening rate of the running data;

[0055] A dynamic coupling module is configured to perform dynamic coupling analysis on the running efficiency indicators of the device based on the time opening rate and the real-time performance opening rate of the device to obtain a comprehensive efficiency OEE value of the device;

[0056] A state analysis module is configured to perform real-time state analysis on the comprehensive efficiency OEE value to obtain an efficiency analysis result of the device;

[0057] The running optimization module is used for performing efficiency parameter optimization on the equipment based on the efficiency analysis result, so as to obtain an optimized running configuration of the equipment.

[0058] The effect evaluation module is used for performing effect evaluation on the running parameter of the equipment based on the optimized running configuration, so as to obtain a final efficiency optimization report of the equipment.

[0059] Compared with the prior art, the present application has the following beneficial effects:

[0060] 1. The present application can accurately calculate the comprehensive efficiency OEE value by collecting the equipment running time, the planned running time and the theoretical cycle time in real time, ensuring the data accuracy through noise filtering, boundary calibration and normalization processing, and then obtaining the time opening rate through dynamic weight distribution and exponential weighted fusion, combining the real-time performance opening rate to construct a dynamic coupling mechanism and a compensation mechanism, realizing the collaborative optimization and dynamic balance synthesis of the two, and significantly improving the real-time performance and accuracy of OEE calculation, providing a reliable core index for equipment efficiency analysis.

[0061] 2. The present application can accurately identify the efficiency deviation state, parameter abnormality category and key influencing factors through real-time state analysis of the comprehensive efficiency OEE value, formulate a multi-level parameter adjustment strategy based on the analysis result to obtain an optimized running configuration, and generate a final report by comparing and evaluating the optimization effect through simulation running data and historical data, so as to not only optimize the equipment running parameter, but also clearly present the efficiency improvement effect, and provide comprehensive technical support for efficient running and continuous improvement of the equipment. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 A flowchart of a real-time calculation and analysis method of equipment comprehensive efficiency OEE provided by an embodiment of the present application is shown in the figure.

[0063] Figure 2 A functional module diagram of a real-time calculation and analysis system of equipment comprehensive efficiency OEE provided by an embodiment of the present application is shown in the figure.

[0064] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0065] It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0066] The embodiment of the present application provides a kind of equipment comprehensive efficiency OEE real-time calculation and analysis method.The execution subject of the equipment comprehensive efficiency OEE real-time calculation and analysis method includes but is not limited to at least one of the electronic device that can be configured to execute the method provided by the embodiment of the present application, such as server, terminal etc.In other words, the equipment comprehensive efficiency OEE real-time calculation and analysis method can be executed by the software or hardware installed in terminal device or server device.The server includes but is not limited to: single server, server cluster, cloud server or cloud server cluster etc.The server can be independent server, can also be cloud server that provides cloud service, cloud database, cloud computing, cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, content distribution network (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platform.

[0067] Referring to Figure 1 As shown in the figure, a kind of equipment comprehensive efficiency OEE real-time calculation and analysis method provided by the embodiment of the present application is flow chart diagram.In the embodiment, the equipment comprehensive efficiency OEE real-time calculation and analysis method includes:

[0068] S1. real-time acquisition equipment running time, planned running time and theoretical cycle time in equipment running data;

[0069] In the embodiment of the present application, the real-time acquisition equipment running time, planned running time and theoretical cycle time in equipment running data includes:

[0070] Collect original time series data representing equipment running state in equipment control system;

[0071] The effective production period of equipment running time in the original time series data is filtered by noise identification, and the effective production period of the original time series data is obtained;

[0072] The plan production period of planned running time in the original time series data is boundary calibration correction, and the plan production period of the original time series data is obtained;

[0073] The standard single-piece processing time of theoretical cycle time in the original time series data is normalized reference processing, and the standard single-piece processing time of the original time series data is obtained.

[0074] Specifically, by connecting the special data interface of the equipment control system, which is a standard communication interface between the equipment control system and the external data acquisition module, the original time series data recorded in the equipment control system and capable of reflecting the running state of the equipment are continuously obtained according to the natural time interval of the equipment operation, which records the running time, the planned running time and the theoretical cycle time related information of the equipment at different time points in sequence with time as the axis. The continuity of data transmission is maintained during the acquisition process to ensure that the record of any time point is not missed, and finally the original time series data representing the running state of the equipment in the equipment control system are obtained.

[0075] Further, when the effective production period of the equipment running time in the original time series data is identified and filtered, the normal fluctuation range and the minimum continuous stable running time of the equipment running time in the effective production period are determined according to the design running parameters of the equipment and the historical normal production records. The normal fluctuation range refers to the common variation interval of the equipment running time value during normal production, and the minimum continuous stable running time refers to the duration that must be reached by the equipment in one effective production process.

[0076] Further, when a certain period of data of the equipment running time in the original time series data exceeds the normal fluctuation range or the length of continuous time without change exceeds the minimum continuous stable running time, it is determined that the data is noise, and these noise data are removed from the original time series data. The data segment that meets the normal fluctuation range and the continuous stable running time standard after removal is the effective production period of the original time series data.

[0077] Further, when the boundary of the planned production period of the planned running time in the original time series data is calibrated and corrected, the planned production schedule prepared in advance by the equipment is obtained, which specifies the start time and end time of the planned production period.

[0078] Further, the start time of the planned production period in the original time series data is compared with the start time in the planned production schedule. If the start time in the original data is later than the planned time, but the time difference between the two is within the standard time required for equipment startup preparation, which is determined according to the fixed steps of the equipment startup process and the specified duration of each step, then the start time in the original data is corrected to the start time in the planned production schedule.

[0079] Further, similarly, the end time of the planned production period in the original data is compared with the end time in the planned production schedule. If the end time in the original data is earlier than the planned time, and the time difference is within the standard time for normal ending work of the equipment, then the end time in the original data is corrected to the end time in the planned production schedule. After such calibration and correction, the planned production period of the original time series data is obtained.

[0080] Further, when the standard single-piece processing time of the theoretical cycle time in the original timing data is normalized, a unified reference time is first determined, which is the time that should be spent according to the design requirements and process standards when a single standard product is processed by the equipment, and then each standard single-piece processing time in the original timing data is compared with the reference time to calculate the proportional relationship of each standard single-piece processing time relative to the reference time.

[0081] Further, if a certain standard single-piece processing time is one and a half times the reference time, the proportion is one and a half, and then all the proportions are converted into standardized values with the reference time as the reference, so that each standard single-piece processing time can clearly reflect the relative relationship with the reference time, and finally the standard single-piece processing time of the original timing data is obtained.

[0082] In summary, the original timing data of the equipment control system is collected, and three types of time data are continuously recorded directly to avoid data loss or distortion and provide a reliable initial source for subsequent processing.

[0083] In summary, the effective production period of the equipment running time is denoised and filtered to eliminate noise data and obtain the effective production period, thereby ensuring that the subsequent efficiency indicators reflect the actual time utilization.

[0084] In summary, the boundaries of the planned running time and the planned production period are calibrated and corrected to the standard planned time to obtain the planned production period and provide an accurate planned time reference.

[0085] In summary, the standard single-piece processing time of the theoretical cycle time is normalized to eliminate dimensional differences and obtain a standardized time, thereby laying a foundation for multi-source data fusion.

[0086] S2. Exponentially weighted fusion of the device running time, the planned running time and the theoretical cycle time to obtain the time opening rate of the running data;

[0087] In the embodiment of the present application, the exponentially weighted fusion of the device running time, the planned running time and the theoretical cycle time to obtain the time opening rate of the running data comprises:

[0088] The device running time, the planned running time and the theoretical cycle time are assigned corresponding dynamic weights to obtain the running time influence factor of the device running time, the planned execution weight coefficient of the planned running time and the theoretical reference proportion of the theoretical cycle time;

[0089] According to the running time influence factor, the plan execution weight coefficient and the theoretical benchmark proportion, the equipment running time, the plan running time and the theoretical cycle time are exponentially weighted and fused to obtain a preliminary start-up rate value of the equipment, wherein a calculation formula of the preliminary start-up rate value is as follows:

[0090]

[0091] In the formula, is the preliminary start-up rate value, , , is an adjustment coefficient, is the equipment running time, is the plan running time, is the theoretical cycle time, is the running time influence factor, is the plan execution weight coefficient, is the theoretical benchmark proportion, is the equipment time utilization rate, is the cycle time efficiency ratio, is the plan capacity coverage rate;

[0092] The preliminary start-up rate value is normalized to obtain a time start-up rate of the running data.

[0093] Specifically, a basis for dynamic weight distribution is determined, which is historical running data of the equipment in a recent period, specifically including a coincidence degree of historical equipment running time and plan running time, and a deviation rate of historical theoretical cycle time and actual processing time. For the equipment running time, if the coincidence degree of the equipment running time and the plan running time in a recent period is in a higher interval, the interval is determined based on a common range of the coincidence degree of the equipment running time and the plan running time in long-term normal operation, the running time influence factor is set to a value corresponding to the high coincidence degree.

[0094] Further, if the coincidence degree is in a medium interval, the value corresponding to the medium coincidence degree is set; if the coincidence degree is in a lower interval, the value corresponding to the low coincidence degree is set, so as to determine the running time influence factor of the equipment running time.

[0095] Further, for the plan running time, if a proportion of days in which the equipment is operated according to the plan to total running days in a recent period is in a higher interval, the interval is determined based on a conventional standard reaching proportion of the equipment according to the plan, the plan execution weight coefficient is set to a value corresponding to the high execution proportion; if the proportion is in a medium interval, the value corresponding to the medium execution proportion is set; if the proportion is in a lower interval, the value corresponding to the low execution proportion is set, so as to determine the plan execution weight coefficient of the plan running time.

[0096] Further, for the theoretical cycle time, if the deviation rate of the theoretical cycle time from the actual processing time in the recent period is in a lower interval, the interval is determined based on the standard allowable deviation range of the equipment processing accuracy, then the theoretical reference parameter proportion is set to the value corresponding to the low deviation rate; if the deviation rate is in a medium interval, then the value corresponding to the medium deviation rate is set.

[0097] Further, if the deviation rate is in a higher interval, then the value corresponding to the high deviation rate is set, and then the theoretical reference parameter proportion of the theoretical cycle time is obtained, and the dynamic weight distribution corresponding to the equipment running time, the planned running time and the theoretical cycle time is completed.

[0098] Further, the determined running time influence factor, the planned execution weight coefficient and the theoretical reference parameter proportion are obtained first, and then the specific values of the equipment running time, the planned running time and the theoretical cycle time in the current period of the equipment are obtained. The specific value of the equipment running time is multiplied by the running time influence factor to obtain the weighted value of the equipment running time; the specific value of the planned running time is multiplied by the planned execution weight coefficient to obtain the weighted value of the planned running time; the specific value of the theoretical cycle time is multiplied by the theoretical reference parameter proportion to obtain the weighted value of the theoretical cycle time.

[0099] Further, the three weighted values are then added to obtain a total sum, which is the preliminary operating rate value of the equipment, and the exponential weighted fusion of the equipment running time, the planned running time and the theoretical cycle time according to the running time influence factor, the planned execution weight coefficient and the theoretical reference parameter proportion is completed.

[0100] Further, the value range of the normalization processing is determined first, which corresponds to the conventional percentage expression requirement of the time operating rate, and is usually set to an interval from a minimum percentage to a maximum percentage. The maximum possible value and the minimum possible value of the preliminary operating rate value are calculated, the maximum possible value is the value of the equipment in the ideal running state, that is, the equipment running time is equal to the planned running time, the theoretical cycle time and the actual processing time without deviation, and the preliminary operating rate value obtained by the exponential weighted fusion at this time is the maximum possible value; the minimum possible value is the preliminary operating rate value when the equipment is completely not running, which is usually the minimum percentage.

[0101] Further, if the preliminary operating rate value is higher than the maximum possible value, the normalization result is calculated according to the maximum possible value; if it is lower than the minimum possible value, the normalization result is calculated according to the minimum possible value, and the time operating rate of the running data is obtained through the operation.

[0102] Specifically, the adjustment coefficient is preset according to the production standard of the industry to which the reference device belongs, the historical operation performance of the device, and the fault diagnosis data of the same type of device, and is determined by referring to the performance index range of the device during normal operation, combining the parameter data of the device without fault in the past year, and comparing the adjustment coefficient value of the same type of device under the same production environment. The device operation time is obtained by filtering the effective production period of the device operation time in the original time series data representing the device operation state in the device control system, and filtering the noise of the effective production period of the original time series data.

[0103] Further, the planned operation time is the planned production period of the planned operation time in the original time series data after boundary calibration and correction.

[0104] Further, the operation time influence factor is determined according to the degree of coincidence between the historical device operation time and the planned operation time of the device in the recent period. When the degree of coincidence is in a high range, a value corresponding to the high degree of coincidence is set; when the degree of coincidence is in a medium range, a value corresponding to the medium degree of coincidence is set; and when the degree of coincidence is in a low range, a value corresponding to the low degree of coincidence is set.

[0105] Further, the planned execution weight coefficient is determined according to the proportion of the number of days that the device is operated according to the plan to the total number of operation days in the recent period. When the proportion is in a high range, a value corresponding to the high execution proportion is set; when the proportion is in a medium range, a value corresponding to the medium execution proportion is set; and when the proportion is in a low range, a value corresponding to the low execution proportion is set. The theoretical reference parameter proportion is determined according to the deviation rate of the theoretical cycle time and the actual processing time in the recent period. When the deviation rate is in a low range, a value corresponding to the low deviation rate is set; when the deviation rate is in a medium range, a value corresponding to the medium deviation rate is set; and when the deviation rate is in a high range, a value corresponding to the high deviation rate is set.

[0106] Further, the device time utilization rate is obtained by dividing the device operation time by the planned operation time. The specific values of the device operation time and the planned operation time are used for division operation to obtain the device time utilization rate.

[0107] Further, the cycle time efficiency ratio is calculated by dividing the theoretical cycle time by the device operation time. The values of the processed theoretical cycle time and the device operation time are used for division operation to obtain the cycle time efficiency ratio.

[0108] Further, the planned capacity coverage rate is obtained by dividing the planned running time by the theoretical cycle time, and the planned running time after calibration and the theoretical cycle time after normalization are used for division calculation to obtain the planned capacity coverage rate.

[0109] Further, the formula comprehensively considers the key dimensions of equipment operation, reflects time utilization, efficiency matching, and capacity coverage through equipment time utilization rate, cycle time efficiency ratio, and planned capacity coverage rate, respectively, and obtains the preliminary operation rate value of the comprehensive operation of the current cycle of the equipment after weighting and adjustment by corresponding weight coefficients and adjustment coefficients, thereby providing a core basis for calculating the time operation rate and avoiding one-sidedness of single-dimensional evaluation.

[0110] Further, when the equipment running time increases and the planned running time remains unchanged, the equipment time utilization rate increases, and under the condition that the running time influence factor is fixed, the calculation result of this part increases, and the preliminary operation rate value rises; when the planned running time increases and the equipment running time remains unchanged, the equipment time utilization rate decreases, the calculation result of this part decreases, and the preliminary operation rate value decreases.

[0111] Further, when the theoretical cycle time decreases and the equipment running time remains unchanged, the cycle time efficiency ratio increases, and when the planned execution weight coefficient is fixed, the calculation result of this part increases, and the preliminary operation rate value rises; when the equipment running time increases and the theoretical cycle time remains unchanged, the cycle time efficiency ratio decreases, the calculation result of this part decreases, and the preliminary operation rate value decreases.

[0112] Further, when the planned running time increases and the theoretical cycle time remains unchanged, the planned capacity coverage rate increases, and under the condition that the theoretical benchmark parameter proportion is fixed, the calculation result of this part increases, and the preliminary operation rate value rises; when the theoretical cycle time increases and the planned running time remains unchanged, the planned capacity coverage rate decreases, the calculation result of this part decreases, and the preliminary operation rate value decreases.

[0113] Further, when the adjustment coefficient increases and other parameters remain unchanged, the preliminary operation rate value increases accordingly; when the adjustment coefficient decreases, the preliminary operation rate value decreases accordingly. When any one of the running time influence factor, the planned execution weight coefficient, and the theoretical benchmark parameter proportion increases, the corresponding weighted part calculation result increases, and when other parameters remain unchanged, the preliminary operation rate value rises, and vice versa.

[0114] Overall, the dynamic weight distribution differentiates the weight according to the historical operation characteristics of the equipment, avoids the problem of fixed weight adaptation, and provides a reasonable basis for accurate fusion.

[0115] Overall, the weighted fusion combines multi-dimensional operation state and adjustment coefficient to avoid one-sided evaluation, and the preliminary operation rate value comprehensively reflects the comprehensive operation of the equipment.

[0116] In general, the normalization process standardizes the numerical range, ensures that the time utilization rate is comparable and has reference value, and provides standardized indicators for subsequent efficiency analysis.

[0117] S3. Based on the time utilization rate and the real-time performance utilization rate of the device, the running efficiency indicators of the device are dynamically coupled and analyzed to obtain the comprehensive efficiency OEE value of the device;

[0118] In the embodiments of the present application, based on the time utilization rate and the real-time performance utilization rate of the device, the running efficiency indicators of the device are dynamically coupled and analyzed to obtain the comprehensive efficiency OEE value of the device, including:

[0119] According to the dynamic change trend of the time utilization rate and the real-time performance utilization rate, the coupling relationship coefficient between the two is determined;

[0120] Based on the coupling relationship coefficient, a dynamic compensation mechanism for the time utilization rate and the real-time performance utilization rate is constructed;

[0121] Based on the dynamic compensation mechanism, the time utilization rate and the real-time performance utilization rate are optimized to obtain the optimized time utilization rate and performance utilization rate;

[0122] The optimized time utilization rate and performance utilization rate are dynamically balanced and synthesized to obtain the comprehensive efficiency OEE value of the device.

[0123] The optimized time utilization rate and performance utilization rate are dynamically balanced and synthesized to obtain the comprehensive efficiency OEE value of the device, including:

[0124] According to the real-time change characteristics of the optimized time utilization rate and performance utilization rate, the dynamic weight of the optimized time utilization rate and performance utilization rate in comprehensive efficiency evaluation is determined;

[0125] The dynamic weight of the optimized time utilization rate and performance utilization rate is weighted and fused to obtain the weighted time utilization rate and the weighted performance utilization rate;

[0126] The weighted time utilization rate and the weighted performance utilization rate are linearly fused to obtain the comprehensive efficiency OEE value of the device, wherein the calculation formula of the comprehensive efficiency OEE value is as follows:

[0127]

[0128] In the formula, is the comprehensive efficiency OEE value, is the synthesis coefficient of the weighted time utilization rate, a coupling relationship coefficient of the weighted performance up rate and the weighted time up rate, a coupling relationship coefficient of the weighted performance up rate and the weighted time up rate, a coupling relationship coefficient of the weighted performance up rate and the weighted time up rate.

[0129] Specifically, by continuously recording the specific values of the time up rate and the real-time performance up rate of the equipment in multiple production cycles, the change curves of the two are drawn with time as the horizontal axis and the values as the vertical axis, and the change direction and amplitude relationship of the two curves are observed. When the time up rate rises and the real-time performance up rate also rises, and the rising amplitude ratio remains stable, it indicates that the two are positively correlated and strongly coupled, at this time the coupling relationship coefficient is set to a fixed high value. When the time up rate rises and the real-time performance up rate falls, and the falling amplitude ratio remains stable, it indicates that the two are negatively correlated and strongly coupled, at this time the coupling relationship coefficient is set to a fixed low value. When the change direction and amplitude of the two have no stable proportional relationship, it indicates that the coupling relationship is weak, at this time the coupling relationship coefficient is set to a fixed intermediate value. The coupling relationship coefficient between the time up rate and the real-time performance up rate is determined in this way.

[0130] Further, based on the determined coupling relationship coefficient, if the coupling relationship coefficient is high, it indicates that the two are positively correlated and closely related, and the dynamic compensation mechanism constructed is: when the time up rate is lower than the lower limit of the preset normal range, and the real-time performance up rate is higher than the upper limit of the normal range, part of the value of the real-time performance up rate is extracted and supplemented to the time up rate, and the amount of the supplemented value is a fixed proportion of the part of the real-time performance up rate that exceeds the upper limit of the normal range, so that the time up rate rises to the normal range.

[0131] Further, if the coupling relationship coefficient is low, it indicates that the two are negatively correlated and closely related, and the dynamic compensation mechanism constructed is: when the time up rate is higher than the upper limit of the normal range, and the real-time performance up rate is lower than the lower limit of the normal range, part of the value of the time up rate is extracted and supplemented to the real-time performance up rate, and the amount of the supplemented value is a fixed proportion of the part of the time up rate that exceeds the upper limit of the normal range, so that the real-time performance up rate rises to the normal range.

[0132] Further, if the coupling relationship coefficient is intermediate, it indicates that the two are weakly related, and the dynamic compensation mechanism constructed is: each maintains the current value, only records the deviation of the two, and does not supplement the value. In this way, the dynamic compensation mechanism of the time up rate and the real-time performance up rate is constructed.

[0133] Further, based on the dynamic compensation mechanism, if the mechanism requires numerical supplement, first calculate the specific numerical value that needs to be supplemented, according to the fixed proportion determined in the compensation mechanism, extract the corresponding numerical value from the one whose numerical value exceeds the normal range, add the extracted numerical value to the one whose numerical value is lower than the normal range, recalculate the numerical values of the two after adding, check whether they are both within the normal range, if one of them still does not reach the normal range, repeat the above supplement process until both of them are within the normal range or reach the preset maximum supplement times, the maximum supplement times are determined according to the historical compensation effect of the equipment.

[0134] Further, if the mechanism does not require numerical supplement, the current numerical values of the two are directly kept unchanged, in this way, the time utilization rate and the real-time performance utilization rate are optimized cooperatively, and the optimized time utilization rate and performance utilization rate are obtained.

[0135] Further, when the optimized time utilization rate and performance utilization rate are dynamically balanced and synthesized, first determine the reference proportion of the two in the comprehensive efficiency, the reference proportion is determined based on the importance of time factor and performance factor in the production process of the equipment.

[0136] Further, if the time factor is more important, the reference proportion of the time utilization rate is higher than that of the performance utilization rate, otherwise the reference proportion of the performance utilization rate is higher, if the importance of the two is equivalent, the proportions are the same, then adjust the reference proportion according to the coupling relationship coefficient, if the coupling relationship coefficient is high, appropriately increase the proportion of the higher one in the total proportion of the two.

[0137] Further, if it is low, appropriately reduce the proportion of the higher one in the total proportion of the two; if it is intermediate, keep the reference proportion unchanged, finally multiply the optimized time utilization rate by the adjusted own proportion, multiply the optimized performance utilization rate by the adjusted own proportion, and add the two product results to obtain the total value, which is the comprehensive efficiency OEE value of the equipment.

[0138] Specifically, when determining the dynamic weight according to the real-time change characteristics of the optimized time utilization rate and performance utilization rate, first set the change amplitude threshold of the two in unit time, the threshold is determined based on the historical fluctuation range of the two when the equipment is normally running, when the change amplitude of the optimized time utilization rate in unit time exceeds the threshold and the change amplitude of the optimized performance utilization rate does not exceed the threshold, it indicates that the real-time change of the time utilization rate is more significant, at this time, the dynamic weight of the optimized time utilization rate in the comprehensive efficiency evaluation is set to a higher value, and the dynamic weight of the optimized performance utilization rate is set to a lower value.

[0139] Furthermore, when the change in the optimized performance utilization rate exceeds the threshold within a unit of time, and the change in the optimized time utilization rate does not exceed the threshold, it indicates that the real-time change in the performance utilization rate is more significant. In this case, the dynamic weight of the optimized performance utilization rate is set to a higher value, and the dynamic weight of the optimized time utilization rate is set to a lower value.

[0140] Furthermore, when the changes in both do not exceed the threshold or both exceed the threshold, it indicates that the changes in both are similar. In this case, the dynamic weights of both are set to the same value. In this way, the dynamic weights of the optimized time uptime and performance uptime in the overall efficiency evaluation are determined.

[0141] Furthermore, when weighting and fusing the optimized time utilization rate and performance utilization rate with dynamic weights, the determined optimized time utilization rate, optimized performance utilization rate, and corresponding dynamic weights are first obtained. The optimized time utilization rate is then multiplied by its own dynamic weight, and the result of the multiplication is the weighted time utilization rate.

[0142] Furthermore, the optimized performance utilization rate is multiplied by its own dynamic weight. The result of the multiplication is the weighted performance utilization rate. In this way, the weighted time utilization rate and the weighted performance utilization rate are obtained.

[0143] Furthermore, when linearly fusing the weighted time utilization rate and the weighted performance utilization rate, the weighted time utilization rate and the weighted performance utilization rate are directly added together. The sum obtained is the overall efficiency (OEE) value of the equipment. If the sum exceeds the theoretical maximum value of the overall efficiency of the equipment, which is determined based on the ideal operating conditions during equipment design, then the theoretical maximum value is taken as the overall efficiency (OEE) value of the equipment.

[0144] Furthermore, if the sum of the sums is lower than the theoretical minimum value of the overall equipment efficiency (OEE), which is the efficiency value at which the equipment cannot operate at all, then the theoretical minimum value is taken as the overall equipment efficiency (OEE) value. The overall equipment efficiency (OEE) value is obtained in this way.

[0145] Specifically, it will closely follow the definition and relationship of each parameter in the formula, and elaborate on it in detail from three aspects: source, significance and trend. It will ensure that each part fits the given content, clearly defines the product and does not use uncertain words, and is presented in paragraph form as required.

[0146] Further, the synthesis coefficient of the weighted time utilization rate is determined based on the degree of dependence of the equipment production process on the time dimension. First, analyze the proportion of the influence of the time utilization rate on the final production efficiency in the normal production process of the equipment. If the rationality of time utilization directly determines the production progress in the production process of the equipment, such as a pipeline equipment, the synthesis coefficient of the weighted time utilization rate is set to a higher value.

[0147] Further, if the equipment production relies more on performance stability than on time length, such as precision machining equipment, the synthesis coefficient is set to a lower value. In the setting process, refer to the synthesis coefficient value of similar equipment in the same production scenario to finally determine the fixed synthesis coefficient of the weighted time utilization rate.

[0148] Further, the synthesis coefficient of the weighted performance utilization rate is determined according to the degree of dependence of the equipment production process on the performance dimension. When the performance of the equipment directly affects the product quality and production efficiency, such as high-speed machining equipment, the synthesis coefficient of the weighted performance utilization rate is set to a higher value. When the performance of the equipment has less impact on production than the time factor, such as low-speed assembly equipment, the synthesis coefficient is set to a lower value. Similarly, refer to the value experience of similar equipment to determine the fixed synthesis coefficient of the weighted performance utilization rate.

[0149] Further, the weighted time utilization rate is obtained by multiplying the optimized time utilization rate and the dynamic weight of itself in the comprehensive efficiency evaluation. First, obtain the optimized time utilization rate and the corresponding dynamic weight, and then perform multiplication operation. The result of the operation is the weighted time utilization rate.

[0150] Further, the weighted performance utilization rate is obtained by multiplying the optimized performance utilization rate and the dynamic weight of itself in the comprehensive efficiency evaluation. After obtaining the optimized performance utilization rate and the corresponding dynamic weight, perform multiplication operation to obtain the weighted performance utilization rate.

[0151] Further, the comprehensive efficiency OEE value is obtained by multiplying the synthesis coefficient of the weighted time utilization rate and the weighted time utilization rate, multiplying the synthesis coefficient of the weighted performance utilization rate and the weighted performance utilization rate, and finally adding the two product results. The entire calculation process is performed in a fixed order, and the result is the comprehensive efficiency OEE value.

[0152] Further, the significance of the formula is to give the weighted time utilization rate and the weighted performance utilization rate the importance degree of matching the requirements of the equipment production process through the synthesis coefficient of the weighted time utilization rate and the synthesis coefficient of the weighted performance utilization rate, and then combine and calculate them according to the corresponding importance degree to finally obtain the comprehensive efficiency OEE value.

[0153] Further, the value can reflect the comprehensive performance of the equipment in time utilization and performance exertion at the same time, avoid the one-sidedness of the evaluation of the overall operation efficiency of the equipment caused by focusing on a single dimension, provide a comprehensive and objective quantitative basis for the staff to judge whether the equipment meets the production requirements and whether the operation parameters need to be adjusted, and ensure that the evaluation result is highly consistent with the needs of the actual production scene of the equipment.

[0154] Further, from the formula trend, when the weighted time utilization rate increases and the synthesis coefficient of the weighted time utilization rate remains unchanged, the product of the weighted time utilization rate and the synthesis coefficient will increase, and if the weighted performance utilization rate and the synthesis coefficient of the weighted performance utilization rate remain unchanged, the overall efficiency OEE value will show an upward trend; if the weighted time utilization rate decreases and the other parameters remain unchanged, the overall efficiency OEE value will decrease. When the weighted performance utilization rate increases and the synthesis coefficient of the weighted performance utilization rate remains unchanged, the product of the weighted performance utilization rate and the synthesis coefficient will increase, and if the weighted time utilization rate and the synthesis coefficient of the weighted time utilization rate remain unchanged, the overall efficiency OEE value will increase; if the weighted performance utilization rate decreases and the other parameters remain unchanged, the overall efficiency OEE value will decrease.

[0155] Further, when the synthesis coefficient of the weighted time utilization rate increases and the weighted time utilization rate remains unchanged, the corresponding product will increase, and when the other parameters remain unchanged, the overall efficiency OEE value will increase; if the synthesis coefficient decreases and the other parameters remain unchanged, the overall efficiency OEE value will decrease.

[0156] Further, when the synthesis coefficient of the weighted performance utilization rate increases and the weighted performance utilization rate remains unchanged, the corresponding product will increase, and when the other parameters remain unchanged, the overall efficiency OEE value will increase; if the synthesis coefficient decreases and the other parameters remain unchanged, the overall efficiency OEE value will decrease.

[0157] Overall, the coupling relationship coefficient is determined according to the dynamic change trend of the two types of utilization rates, the correlation degree of the two is accurately quantified, the fixed assumption is avoided to be inconsistent with the actual situation, and a scientific basis is provided for the dynamic compensation mechanism.

[0158] Overall, the dynamic compensation mechanism is constructed based on the coupling relationship coefficient, and different strategies are formulated according to different coupling scenarios to avoid distortion caused by indiscriminate compensation, make the compensation adapt to the correlation characteristics, and support collaborative optimization.

[0159] Overall, the dynamic compensation mechanism is used to collaboratively optimize the two types of utilization rates, which are gradually adjusted to the normal range, and the single optimization defect is eliminated to provide a high-quality index basis for the synthesis of the OEE value.

[0160] Overall, the dynamic balance synthesis optimizes the two types of opening rates, combines the process benchmark proportion, and adjusts the coupling coefficient to avoid the limitations of fixed proportions, so that the OEE value fully reflects the comprehensive level of the equipment.

[0161] Overall, the dynamic weight is determined according to the real-time change characteristics of the two types of opening rates after optimization, the amplitude threshold is used to distinguish the change significance and assign values, the fixed weight adaptation problem is avoided, the weight is related to the real-time state, and an actual basis is provided for weighted fusion.

[0162] Overall, the two types of opening rates are weighted and fused with dynamic weights, the weights are assigned by multiplication, the real-time importance of the indicators is embodied, the imbalance is avoided, calibrated data is provided for linear fusion, and the OEE value is used to reflect the key indicators.

[0163] Overall, the two types of opening rates are weighted and fused with dynamic weights, the weights are assigned by multiplication, the real-time importance of the indicators is embodied, the imbalance is avoided, calibrated data is provided for linear fusion, and the OEE value is used to reflect the key indicators.

[0164] S4. Real-time state analysis is performed on the comprehensive efficiency OEE value to obtain the efficiency analysis result of the device;

[0165] In the embodiments of the present application, the real-time state analysis of the comprehensive efficiency OEE value to obtain the efficiency analysis result of the device comprises:

[0166] The comprehensive efficiency OEE value is compared with the preset efficiency threshold interval to obtain the efficiency deviation state of the comprehensive efficiency OEE value;

[0167] Based on the efficiency deviation state, the real-time running parameters of the device are classified as abnormal, and the parameter abnormality class of the device is obtained;

[0168] The parameter abnormality class is extracted and analyzed to obtain the key efficiency influencing factor of the parameter abnormality class;

[0169] The key efficiency influencing factor is analyzed for efficiency rationality to obtain the efficiency analysis result of the device.

[0170] Specifically, when comparing the overall efficiency OEE value with the preset efficiency threshold interval, the preset efficiency threshold interval is first determined, which is determined according to the ideal efficiency range during equipment design, the average efficiency level of similar equipment in the industry, and the best efficiency range in the historical operation of the equipment, and is divided into three parts, namely, the efficiency normal interval, the efficiency low interval, and the efficiency high interval, wherein the efficiency normal interval is the common fluctuation range of the overall efficiency OEE value during normal operation of the equipment, the efficiency low interval is the range below the lower limit of the normal interval, and the efficiency high interval is the range above the upper limit of the normal interval.

[0171] Further, the calculated overall efficiency OEE value is compared with the three intervals, if the overall efficiency OEE value is in the efficiency normal interval, it is determined that the efficiency deviation state of the overall efficiency OEE value is no deviation, if it is in the efficiency low interval, it is determined to be negative deviation, and if it is in the efficiency high interval, it is determined to be positive deviation, and the efficiency deviation state of the overall efficiency OEE value is obtained in this way.

[0172] Further, when identifying the abnormal category of the real-time operation parameters of the equipment based on the efficiency deviation state, the current real-time operation parameters of the equipment are first collected, which include the operating speed, energy consumption, temperature, pressure and other indicators directly reflecting the operating state of the equipment, if the efficiency deviation state is negative deviation, each real-time operation parameter is checked one by one to see if it exceeds the corresponding normal range, which is determined based on the design parameters of the equipment and the parameter range during the normal operation, and the parameters exceeding the normal range are marked as abnormal parameters, and then the abnormal parameters are classified according to their types.

[0173] For example, the parameters with operating speed lower than the normal range are classified as speed abnormality, and the parameters with temperature higher than the normal range are classified as temperature abnormality, so as to obtain the parameter abnormality category of the equipment, if the efficiency deviation state is positive deviation or no deviation, the real-time operation parameters are also checked, if there are parameters exceeding the normal range, the parameter abnormality category is determined in the above-mentioned manner, if all parameters are within the normal range, the parameter abnormality category is no abnormality, and the parameter abnormality category of the equipment is obtained in this way.

[0174] Further, when extracting and analyzing the key efficiency influencing factors of the parameter abnormality category, a corresponding relationship library of the parameter abnormality category and the efficiency influencing factors is first established, which is constructed based on the working principle of the equipment, the historical failure data and the equipment maintenance experience in the industry, wherein the efficiency influencing factors that each parameter abnormality category may correspond to are recorded.

[0175] For example, the influencing factors that the speed abnormality may correspond to include drive motor failure, transmission system wear, etc., and the influencing factors that the temperature abnormality may correspond to include cooling system failure, poor heat dissipation, etc. Further, the calculated overall efficiency OEE value is compared with the three intervals, if the overall efficiency OEE value is in the efficiency normal interval, it is determined that the efficiency deviation state of the overall efficiency OEE value is no deviation, if it is in the efficiency low interval, it is determined to be negative deviation, and if it is in the efficiency high interval, it is determined to be positive deviation, and the efficiency deviation state of the overall efficiency OEE value is obtained in this way.

[0172] Further, when identifying the abnormal category of the real-time operation parameters of the equipment based on the efficiency deviation state, the current real-time operation parameters of the equipment are first collected, which include the operating speed, energy consumption, temperature, pressure and other indicators directly reflecting the operating state of the equipment, if the efficiency deviation state is negative deviation, each real-time operation parameter is checked one by one to see if it exceeds the corresponding normal range, which is determined based on the design parameters of the equipment and the parameter range during the normal operation, and the parameters exceeding the normal range are marked as abnormal parameters, and then the abnormal parameters are classified according to their types.

[0173] For example, the parameters with operating speed lower than the normal range are classified as speed abnormality, and the parameters with temperature higher than the normal range are classified as temperature abnormality, so as to obtain the parameter abnormality category of the equipment, if the efficiency deviation state is positive deviation or no deviation, the real-time operation parameters are also checked, if there are parameters exceeding the normal range, the parameter abnormality category is determined in the above-mentioned manner, if all parameters are within the normal range, the parameter abnormality category is no abnormality, and the parameter abnormality category of the equipment is obtained in this way.

[0174] Further, when extracting and analyzing the key efficiency influencing factors of the parameter abnormality category, a corresponding relationship library of the parameter abnormality category and the efficiency influencing factors is first established, which is constructed based on the working principle of the equipment, the historical failure data and the equipment maintenance experience in the industry, wherein the efficiency influencing factors that each parameter abnormality category may correspond to are recorded.

[0175] For example, the influencing factors that the speed abnormality may correspond to include drive motor failure, transmission system wear, etc., and the influencing factors that the temperature abnormality may correspond to include cooling system failure, poor heat dissipation, etc.

[0176] Further, according to the determined parameter abnormality category, all possible influencing factors matching the same are searched in the corresponding relationship library, and then the correlation degree of each influencing factor to the deviation state of the overall efficiency OEE value is calculated, the correlation degree is determined by the proportion of the number of times that the influencing factor causes the same efficiency deviation state in history, and the top three influencing factors with the highest correlation degree are taken as the key efficiency influencing factors. In this way, the key efficiency influencing factors of the parameter abnormality category are obtained.

[0177] Further, when performing efficiency rationality analysis on the key efficiency influencing factors, the standard performance characteristics of the key efficiency influencing factors of the device in the normal running state are first obtained, the characteristics are determined based on the design standards and historical normal running data of the device, for example, the standard performance characteristics of the driving motor fault influencing factor is that the output power of the motor is lower than a certain proportion of the design value, and then the actual performance of the current key efficiency influencing factors is compared with the standard performance characteristics to analyze the difference degree and the specific influence of the difference on the overall efficiency OEE value.

[0178] For example, if the actual output power of the driving motor is lower than the standard value, it will cause the running speed of the device to decrease, thereby reducing the time utilization rate and the performance utilization rate, and finally making the overall efficiency OEE value low. Combined with the production demand and process standard of the device, it is judged whether the key efficiency influencing factor is within an acceptable range. If the difference degree is within the acceptable range, it is determined as a reasonable efficiency fluctuation; if it exceeds the acceptable range, it is determined as an unreasonable efficiency loss, and the specific reason and possible consequences of causing the loss are specified. In this way, the efficiency analysis result of the device is obtained.

[0179] Overall, the OEE value is compared with the preset threshold value combined with the ideal efficiency of the device, the industry level and the historical data to accurately determine the deviation state and provide an objective basis for abnormality identification.

[0180] Overall, the device parameters are searched according to the efficiency deviation type, the abnormalities are classified by referring to the standard markers, the indiscriminate search is avoided, and the accuracy of abnormality positioning is improved.

[0181] Overall, the key influencing factors are extracted according to the correlation number proportion based on the device principle, historical fault correlation library, etc., the blind listing is avoided, and the analysis direction is clear.

[0182] Overall, the actual and design standard performance of the key factors are compared, the difference acceptability is combined, the reasonable fluctuation or loss reason is specified, and the subsequent optimization is supported.

[0183] S5. Based on the efficiency analysis result, the efficiency parameters of the device are optimized to obtain an optimized running configuration of the device.

[0184] In the embodiment of the present application, the efficiency parameter optimization is performed on the device based on the efficiency analysis result, and an optimized operation configuration of the device is obtained.

[0185] The efficiency optimization parameter is identified based on the efficiency analysis result, and the efficiency optimization parameter of the device is obtained.

[0186] The parameter adjustment strategy is performed on the efficiency optimization parameter, and a multi-stage adjustment strategy of the device is obtained.

[0187] The controllable operation parameter of the device is configured and optimized based on the multi-stage adjustment strategy, and the optimized operation configuration of the device is obtained.

[0188] Specifically, when the efficiency optimization parameter of the device is identified based on the efficiency analysis result, the unreasonable efficiency loss and the corresponding key efficiency influencing factors indicated in the efficiency analysis result are first determined, and then the operation parameters directly related to these influencing factors are determined according to the correlation between the operation parameters and the key efficiency influencing factors.

[0189] For example, if the key efficiency influencing factor is insufficient output power of the driving motor, the corresponding operation parameters are motor operating voltage and current, and these parameters are the possible efficiency optimization parameters. Then, the difference between the current values of these parameters and the standard values of the device in normal operation is checked, and the parameters whose difference exceeds the preset allowable range are marked as parameters that need to be optimized, and finally the efficiency optimization parameters of the device are obtained.

[0190] Further, when the parameter adjustment strategy is developed for the efficiency optimization parameter, the adjustment direction is first determined according to the type of the efficiency optimization parameter and the difference between the current value and the standard value, i.e., the adjustment direction is to increase the parameter if the current value is lower than the standard value, or to decrease the parameter if the current value is higher than the standard value. Then, the multi-stage adjustment amplitudes are set in combination with the operation characteristics and historical adjustment data of the device, the first-stage adjustment amplitude is a smaller proportion of the difference degree and is suitable for initial adjustment to avoid large fluctuations, the second-stage adjustment amplitude is a medium proportion of the difference degree and is suitable for the case where the standard is not reached after the first-stage adjustment, and the third-stage adjustment amplitude is a larger proportion of the difference degree and is suitable for the case where there is still a significant difference after the first two stages of adjustment. At the same time, the effect evaluation index after each stage of adjustment and the trigger condition for re-adjustment are determined, and the multi-stage adjustment strategy of the device is formed.

[0191] Further, when the controllable operation parameter of the device is configured and optimized based on the multi-stage adjustment strategy, the controllable operation parameter corresponding to the efficiency optimization parameter is adjusted according to the first-stage adjustment amplitude in the multi-stage adjustment strategy, and the running state of the device and the change of the overall efficiency OEE value are monitored in real time during the adjustment process.

[0192] Further, after the adjustment is completed, it is judged whether the adjustment is effective according to a preset effect evaluation index, if the comprehensive efficiency OEE value approaches the normal range and the equipment runs stably, it is indicated that the adjustment is effective, at this time, the current configuration is maintained, if the comprehensive efficiency OEE value has no obvious improvement or the equipment appears an unstable running state after the adjustment, then the second level adjustment range is used for adjusting again, the above monitoring and evaluation process is repeated, if the second level adjustment still does not achieve the expected effect, then the third level adjustment is executed, until the comprehensive efficiency OEE value of the equipment enters the normal range and runs stably, at this time, the controllable running parameter configuration is the optimized running configuration of the equipment.

[0193] In summary, the to-be-optimized parameters are identified according to the efficiency analysis result, the parameters associated with the efficiency problem are locked, the optimization direction deviation is avoided, the accurate object is provided for subsequent strategy making, and the pertinence is ensured.

[0194] In summary, the multi-level adjustment strategy is made for the efficiency optimization parameters, the multi-level range is set according to the parameter type and difference degree, and the evaluation index is clearly defined, the running fluctuation or incomplete optimization is avoided, and the scientific scheme is provided.

[0195] In summary, the controllable parameters are optimized according to the multi-level strategy, the equipment state and OEE value are adjusted and monitored gradually, until the OEE is normal and stable, the configuration confusion is avoided, and the optimized configuration is ensured to adapt to the demand and support the efficient running.

[0196] S6. Based on the optimized running configuration, the effect of the running parameter of the equipment is evaluated, and a final efficiency optimization report of the equipment is obtained

[0197] In the embodiment of the application, based on the optimized running configuration, the effect of the running parameter of the equipment is evaluated, and a final efficiency optimization report of the equipment is obtained, which comprises:

[0198] Based on the optimized running configuration, the running parameter of the equipment is simulated and adjusted, and simulated running data of the equipment is obtained;

[0199] The simulated running data and the historical running data are compared and analyzed, and an optimization effect evaluation result of the equipment is obtained;

[0200] According to the optimization effect evaluation result, the final efficiency optimization report is determined.

[0201] The simulated running data and the historical running data are compared and analyzed, and an optimization effect evaluation result of the equipment is obtained, which comprises:

[0202] Based on a preset comparison rule, corresponding key indicators are extracted from the simulated running data and the historical running data respectively, and simulated key indicators of the simulated running data and historical key indicators of the historical running data are obtained;

[0203] The simulation production characteristic quantity of the simulation key indicator is constructed to obtain a production characteristic quantity of the simulation key indicator;

[0204] The historical operation benchmark quantity of the historical key indicator is extracted to obtain a historical operation benchmark quantity of the historical key indicator;

[0205] According to the degree of coincidence between the production characteristic quantity and the historical operation benchmark quantity, the improvement effect of the optimized operation configuration on the equipment efficiency is evaluated to obtain the optimization effect evaluation result.

[0206] Specifically, when the operation parameters of the equipment are simulated and adjusted based on the optimized operation configuration, a simulation operation platform consistent with the actual operation environment of the equipment is first built. The platform can accurately reproduce the operation logic, load condition and external environmental condition of the equipment. Then, the values of the controllable operation parameters determined in the optimized operation configuration are input into the simulation platform, and a simulation operation program is started to make the platform run for a preset time according to the normal production process of the equipment.

[0207] Further, the preset time is determined according to a complete production cycle of the equipment. During the simulation operation process, the comprehensive efficiency OEE value, the time utilization rate, the performance utilization rate and the change data of each key operation parameter of the equipment are recorded in real time. These data constitute the simulation operation data of the equipment.

[0208] Further, when the simulation operation data and the historical operation data are compared and analyzed, the historical operation data of the equipment within the same production cycle before optimization is first collected. The data should include the same index items as the simulation operation data, such as the comprehensive efficiency OEE value and the time utilization rate. Then, the values of each index are compared one by one, and the change amount of each index in the simulation operation data relative to the historical operation data is calculated.

[0209] For example, the improvement amount of the comprehensive efficiency OEE value and the increase amount of the time utilization rate.

[0210] Further, the fluctuation of each key operation parameter in the simulation operation and the historical operation is analyzed simultaneously to determine whether the parameter stability is improved. Finally, according to the preset evaluation standard, such as the comprehensive efficiency OEE value reaching a specific proportion, the parameter fluctuation amplitude being reduced to a specific range, etc., the improvement degree of the equipment operation efficiency by the optimization measures is determined, and the optimization effect evaluation result of the equipment is obtained.

[0211] Further, when the final efficiency optimization report is determined according to the optimization effect evaluation result, the core content in the optimization effect evaluation result is first sorted out, including the change of the comprehensive efficiency OEE value, the improvement amplitude of each operation parameter, the influence of the optimization measures on the equipment stability, etc. Then, the optimized operation configuration used in the optimization process is recorded in detail, including the specific values and adjustment methods of each controllable operation parameter.

[0212] Further, the specific reasons for the optimization effect reaching or not reaching the expectation are analyzed, if the expectation is reached, the effective optimization experience is summarized, if the expectation is not reached, the possible limiting factors and improvement direction are explained, and finally the above contents are organized into a complete document according to the fixed report format, the document needs to include evaluation conclusion, data comparison details, optimization configuration description and subsequent suggestions, etc. The document is the final efficiency optimization report of the equipment.

[0213] Specifically, according to the step requirements, the implementation details of each step are disassembled in detail, from the key indicator extraction, production characteristic quantity construction, to the historical running benchmark quantity extraction and optimization effect evaluation, to ensure that each link method is clear, the product is accurate, and meets the requirements of no uncertain words, no parameter formula, etc.

[0214] Further, when extracting the corresponding key indicators from the simulation running data and the historical running data based on the preset comparison rules, first, the key indicator types specified in the preset comparison rules are determined, these types are determined according to the core requirements of equipment efficiency evaluation, then the data items completely matching these types are selected from the simulation running data, and these selected data items are arranged into a structured data set, which is the simulation key indicators of the simulation running data.

[0215] Further, the corresponding historical data items are selected from the historical running data based on the same key indicator type as the standard, and are also arranged into a structured data set to obtain the historical key indicators of the historical running data.

[0216] Further, when constructing the simulation production characteristic quantity of the simulation key indicators, first, the production association logic of each data item in the simulation key indicators is analyzed, then the simulation key indicators are classified and integrated according to these association logics, the data items related to the same production link are grouped into a group, and the equipment running speed and energy consumption are grouped into a running class characteristic group.

[0217] Further, the average value, stable duration, peak value occurrence times and other characteristic parameters of each group of data items in the simulation running period are calculated, these characteristic parameters are combined with the corresponding data item groups to form structured information that can reflect the production characteristics of the simulation running, and this information is the production characteristic quantity of the simulation key indicators.

[0218] Further, when extracting the historical running benchmark quantity of the historical key indicators, first, the benchmark period of the historical running data is determined, which is consistent with the simulation running period to ensure the fairness of data comparison, then all data items in the benchmark period are extracted from the historical key indicators, and are grouped according to the same classification method as the simulation key indicators.

[0219] Further, then calculate the average value, stable duration, peak occurrence times and other parameters of each group of data items in the benchmark period, combine these parameters with the corresponding data item groups to form structured information reflecting the historical normal operation benchmark characteristics, which is the historical operation benchmark quantity of the historical key indicators.

[0220] Further, when evaluating the improvement effect of the optimized operation configuration on the equipment efficiency according to the degree of coincidence between the production characteristic quantity and the historical operation benchmark quantity, first compare the characteristic parameters of the same group and the same type in the production characteristic quantity and the historical operation benchmark quantity, such as the average value of the comprehensive efficiency OEE value in the efficiency type characteristic group and the average value of the energy consumption in the operation type characteristic group.

[0221] Further, calculate the change ratio of each parameter in the production characteristic quantity relative to the corresponding parameter in the historical operation benchmark quantity. If the change ratio of the average value of the comprehensive efficiency OEE value is positive growth and the change ratio of the average value of the energy consumption is negative reduction, it means that the efficiency is improved and the energy consumption is optimized. Then, according to the preset improvement effect level standard, divide the improvement degree corresponding to the change ratio into three levels: significant improvement, general improvement, and no improvement.

[0222] For example, if the average value of the comprehensive efficiency OEE value increases by more than a certain percentage and the average value of the energy consumption decreases by more than a certain percentage, it is determined to be significantly improved. Finally, combine the evaluation levels of all groups to comprehensively judge the overall improvement effect of the optimized operation configuration on the equipment efficiency, and form an evaluation conclusion containing the improvement level, specific parameter change details, and improvement direction. The conclusion is the optimization effect evaluation result.

[0223] In summary, according to the simulation adjustment of the equipment parameters of the optimized configuration, a simulation platform is built to record key data that fits the actual situation, avoiding interruptions in production during physical debugging and providing reliable simulation benchmarks for comparative analysis.

[0224] In summary, by comparing the simulation and historical data of the same period before optimization, and quantifying the efficiency improvement degree according to the preset standard, subjective and one-sided judgments are avoided, and data support is provided for the report.

[0225] In summary, sort out the evaluation results, optimized configurations, and reason analysis, and form a report containing conclusions and suggestions according to the format, avoiding scattered records, forming an optimization closed loop, and supporting subsequent improvements.

[0226] In summary, extract the key indicators of simulation and historical data according to the preset rules, focus on the core data of efficiency evaluation, avoid interference, and provide accurate basis for subsequent analysis.

[0227] In summary, construct the production characteristic quantity of the simulation key indicators, calculate the characteristic parameters, and deeply analyze the simulation operation characteristics to provide detailed information for the optimization effect evaluation.

[0228] In general, the running benchmark quantity of the historical key indicators is extracted, the consistent benchmark period is determined, the reliable historical reference is established, and the comparison analysis is more scientific.

[0229] In general, the optimization effect is quantified and the promotion level is determined according to the degree of coincidence between the production characteristic quantity and the historical benchmark quantity, the efficiency improvement degree is objectively and clearly determined, and the basis for subsequent improvement is provided.

[0230] As shown in Figure 2 Figure 1 is a functional module diagram of a real-time calculation and analysis system for equipment overall efficiency OEE according to an embodiment of the present application.

[0231] The real-time calculation and analysis system 100 for equipment overall efficiency OEE according to the present application can be installed in an electronic device. According to the functions implemented, the real-time calculation and analysis system 100 for equipment overall efficiency OEE can include a data acquisition module 101, a data fusion module 102, a dynamic coupling module 103, a state analysis module 104, a running optimization module 105, and an effect evaluation module 106. The modules according to the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete a fixed function, and are stored in the memory of the electronic device.

[0232] In the present embodiment, the functions of each module / unit are as follows:

[0233] The data acquisition module 101 is used to acquire the running data of the equipment in real time, wherein the running data includes the equipment running time, the planned running time, and the theoretical cycle time.

[0234] The data fusion module 102 is used to perform exponential weighted fusion of multi-source data on the equipment running time, the planned running time, and the theoretical cycle time to obtain the time utilization rate of the running data.

[0235] The dynamic coupling module 103 is used to perform dynamic coupling analysis on the running efficiency indicators of the equipment based on the time utilization rate and the real-time performance utilization rate of the equipment to obtain the overall efficiency OEE value of the equipment.

[0236] The state analysis module 104 is used to perform real-time state analysis on the overall efficiency OEE value to obtain the efficiency analysis result of the equipment.

[0237] The running optimization module 105 is used to optimize the efficiency parameters of the equipment based on the efficiency analysis result to obtain the optimized running configuration of the equipment.

[0238] The effect evaluation module 106 is configured to evaluate the operation parameters of the device based on the optimized operation configuration, and obtain a final efficiency optimization report of the device.

[0239] In several embodiments provided in the present application, it should be understood that the disclosed method and system can be implemented in other ways. For example, the above-described system embodiments are only illustrative, for example, the division of the modules is only a logical functional division, and actual implementation can have another division manner.

[0240] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical units, that is, they can be located in one place or distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0241] In addition, each functional module in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically independently, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software functional module.

[0242] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0243] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Among them, artificial intelligence is to use digital computers or machine controlled by digital computers to simulate, extend and expand human intelligence, perceive environment, acquire knowledge and use knowledge to obtain the best results. Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application and are not limited. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for real-time calculation and analysis of overall equipment efficiency (OEE), characterized in that, The method comprises: S1. Real-time acquisition of equipment operation data, wherein the operation data comprises equipment operation time, planned operation time and theoretical cycle time; S2. Exponential weighted fusion of multi-source data of the equipment operation time, the planned operation time and the theoretical cycle time to obtain a time opening rate of the operation data; S3. Dynamic coupling analysis of the operation efficiency index of the equipment based on the time opening rate and the real-time performance opening rate of the equipment to obtain a comprehensive efficiency OEE value of the equipment; S4. Real-time state analysis of the comprehensive efficiency OEE value to obtain an efficiency analysis result of the equipment; S5. Efficiency parameter optimization of the equipment based on the efficiency analysis result to obtain an optimized operation configuration of the equipment; S6. Effect evaluation of the operation parameters of the equipment based on the optimized operation configuration to obtain a final efficiency optimization report of the equipment.

2. The method of real-time calculation and analysis of overall equipment effectiveness (OEE) of a device according to claim 1, wherein, The real-time acquisition of equipment operation data, wherein the operation data comprises equipment operation time, planned operation time and theoretical cycle time, comprises: Acquiring original time series data representing the equipment operation state in the equipment control system; Noise identification filtering of the effective production period of the equipment operation time in the original time series data to obtain the effective production period of the original time series data; Boundary calibration correction of the planned production period of the planned operation time in the original time series data to obtain the planned production period of the original time series data; Normalization benchmarking of the standard single-piece processing time of the theoretical cycle time in the original time series data to obtain the standard single-piece processing time of the original time series data.

3. The method of real-time calculation and analysis of overall equipment effectiveness (OEE) of a device according to claim 1, wherein, The exponential weighted fusion of multi-source data of the equipment operation time, the planned operation time and the theoretical cycle time to obtain the time opening rate of the operation data comprises: Corresponding dynamic weight distribution of the equipment operation time, the planned operation time and the theoretical cycle time to obtain an operation time influence factor of the equipment operation time, a planned execution weight coefficient of the planned operation time and a theoretical benchmark parameter proportion of the theoretical cycle time; Exponential weighted fusion of the equipment operation time, the planned operation time and the theoretical cycle time according to the operation time influence factor, the planned execution weight coefficient and the theoretical benchmark parameter proportion to obtain a preliminary opening rate value of the equipment, wherein the calculation formula of the preliminary opening rate value is as follows: wherein, is the preliminary start-up rate value, , , is the adjustment coefficient, is the equipment running time, is the planned running time, is the theoretical cycle time, is the running time influence factor, is the planned execution weight coefficient, is the theoretical reference parameter proportion, is the equipment time utilization rate, is the cycle time efficiency ratio, is the planned production capacity coverage rate; Normalization processing of the preliminary opening rate value to obtain the time opening rate of the operation data.

4. The method of real-time calculation and analysis of overall equipment effectiveness (OEE) of a device according to claim 1, wherein, The dynamic coupling analysis of the operation efficiency index of the equipment based on the time opening rate and the real-time performance opening rate of the equipment to obtain the comprehensive efficiency OEE value of the equipment comprises: Determination of a coupling relationship coefficient between the time opening rate and the real-time performance opening rate according to the dynamic change trend thereof; Construction of a dynamic compensation mechanism of the time opening rate and the real-time performance opening rate based on the coupling relationship coefficient; Based on the dynamic compensation mechanism, the time utilization rate and the real-time performance utilization rate are optimized to obtain an optimized time utilization rate and performance utilization rate; The optimized time utilization rate and performance utilization rate are dynamically balanced and synthesized to obtain the overall efficiency OEE value of the device.

5. The method of real-time calculation and analysis of overall equipment effectiveness (OEE) of a device according to claim 4, wherein, The optimized time utilization rate and performance utilization rate are dynamically balanced and synthesized to obtain the overall efficiency OEE value of the device, including: According to the real-time change characteristics of the optimized time utilization rate and performance utilization rate, the dynamic weights of the optimized time utilization rate and performance utilization rate in the overall efficiency evaluation are determined; The dynamic weights of the optimized time utilization rate and performance utilization rate are weighted and fused to obtain a weighted time utilization rate and a weighted performance utilization rate; The weighted time utilization rate and the weighted performance utilization rate are linearly fused to obtain the overall efficiency OEE value of the device, wherein the calculation formula of the overall efficiency OEE value is as follows: wherein is the overall efficiency OEE value, is a synthesis coefficient for the weighted time utilization, is a synthesis coefficient for the weighted performance utilization, is the weighted time utilization, is the weighted performance utilization.

6. The method of real-time calculation and analysis of overall equipment effectiveness (OEE) of a device according to claim 1, wherein, The overall efficiency OEE value is analyzed in real time to obtain the efficiency analysis result of the device, including: The overall efficiency OEE value is compared with the preset efficiency threshold interval to obtain the efficiency deviation state of the overall efficiency OEE value; Based on the efficiency deviation state, the real-time running parameters of the device are identified to obtain the parameter abnormal category of the device; The parameter abnormal category is analyzed to extract the key efficiency influencing factors to obtain the key efficiency influencing factors of the parameter abnormal category; The key efficiency influencing factors are analyzed for efficiency rationality to obtain the efficiency analysis result of the device.

7. The method of real-time calculation and analysis of overall equipment effectiveness (OEE) of a device according to claim 1, wherein, Based on the efficiency analysis result, the efficiency parameters of the device are optimized to obtain the optimized running configuration of the device, including: Based on the efficiency analysis result, the efficiency optimization parameters of the device are identified to obtain the efficiency optimization parameters of the device; The efficiency optimization parameters are adjusted to obtain a multi-level adjustment strategy for the device; Based on the multi-level adjustment strategy, the controllable running parameters of the device are configured and optimized to obtain the optimized running configuration of the device.

8. The method of real-time calculation and analysis of overall equipment effectiveness (OEE) of a device according to claim 1, wherein, Based on the optimized running configuration, the running parameters of the device are evaluated to obtain the final efficiency optimization report of the device, including: Based on the optimized running configuration, the running parameters of the device are simulated and adjusted to obtain the simulation running data of the device; The simulation running data and the historical running data are compared and analyzed to obtain the optimization effect evaluation result of the device; According to the optimization effect evaluation result, the final efficiency optimization report is determined.

9. The method of real-time calculation and analysis of overall equipment effectiveness (OEE) of a device according to claim 8, wherein, The simulation running data and the historical running data are compared and analyzed to obtain the optimization effect evaluation result of the device, including: Based on the preset comparison rule, the corresponding key indicators are extracted from the simulation running data and the historical running data to obtain the simulation key indicators of the simulation running data and the historical key indicators of the historical running data; The simulation key indicator is subjected to simulation production characteristic quantity construction, and production characteristic quantity of the simulation key indicator is obtained. The historical key indicator is subjected to historical operation benchmark quantity extraction, and historical operation benchmark quantity of the historical key indicator is obtained. According to the coincidence degree of the production characteristic quantity and the historical operation benchmark quantity, the promotion effect of the optimized operation configuration on the equipment efficiency is evaluated, and the optimization effect evaluation result is obtained.

10. A real-time calculation and analysis system for overall equipment efficiency (OEE), characterized in that, The system comprises: A data acquisition module is configured to acquire operation data of the equipment in real time, wherein the operation data comprises equipment operation time, planned operation time and theoretical cycle time; A data fusion module is configured to perform exponential weighted fusion of multi-source data on the equipment operation time, the planned operation time and the theoretical cycle time, and obtain a time opening rate of the operation data; A dynamic coupling module is configured to perform dynamic coupling analysis on an operation efficiency indicator of the equipment based on the time opening rate and a real-time performance opening rate of the equipment, and obtain a comprehensive efficiency OEE value of the equipment; A state analysis module is configured to perform real-time state analysis on the comprehensive efficiency OEE value, and obtain an efficiency analysis result of the equipment; An operation optimization module is configured to perform efficiency parameter optimization on the equipment based on the efficiency analysis result, and obtain an optimized operation configuration of the equipment; An effect evaluation module is configured to perform effect evaluation on operation parameters of the equipment based on the optimized operation configuration, and obtain a final efficiency optimization report of the equipment.

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