Monitoring and optimization method and system based on OEE equipment comprehensive efficiency

Abstract

The present invention relates to the technical field of production operation and management, and discloses a monitoring and optimization method and system based on the comprehensive efficiency of OEE equipment. The method collects equipment operation data in real time through intelligent sampling to maintain the accuracy of the collected data; the collected equipment operation data is pre-processed and stored in a database; the OEE index of the equipment is calculated according to the equipment operation data, and the equipment operation efficiency is evaluated in multiple dimensions; based on the equipment operation data, the operation status of the equipment is monitored in real time, and the cause of the fault is diagnosed, and the fault is discovered and solved in time; when the equipment is abnormal, an alarm is issued and relevant personnel are notified, and measures are taken in time for maintenance and processing; the operation status of the equipment and the OEE index are monitored in real time by means of charts and dashboards, and the comprehensive efficiency of the equipment is visualized, which is convenient for management personnel to monitor and make decisions; according to the operation status of the equipment, the bottleneck of the equipment operation is analyzed, and corresponding improvement plans are formulated to improve the efficiency and production capacity of the equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of production operation and management, and in particular to a monitoring and optimization method and system based on OEE equipment comprehensive efficiency. Background Art

[0002] At present, with the development of science and technology, researchers monitor the operating status of equipment in real time and calculate OEE values ​​to discover the causes of equipment failure and downtime, and formulate corresponding maintenance strategies and maintenance plans to improve the reliability and stability of equipment; optimize equipment maintenance and production scheduling by calculating OEE values, optimize production plans and production processes, thereby improving equipment efficiency and reducing production costs, realizing the digitization, automation and intelligence of the enterprise's production process, and improving the competitiveness and market share of enterprises. However, most of them do not consider the accuracy of the collected data, and the data dimensions involved are relatively few.

[0003] For example, the patent with publication number CN115471048A discloses a method for monitoring and collaborative optimization of the comprehensive efficiency of production line equipment based on digital twins, including the following steps: collecting decision features for calculating the comprehensive efficiency of equipment; using machine learning algorithms to calculate and analyze according to the decision features, and dividing the shutdown types; using the standard calculation formula for the comprehensive efficiency of equipment, combined with the divided shutdown types, to calculate the real-time comprehensive efficiency of the equipment, and divide the comprehensive efficiency of the equipment into multiple levels; according to the level of the comprehensive efficiency of the equipment, the operation tasks are assigned to increase the efficiency of equipment with low comprehensive efficiency; a method for monitoring and collaborative optimization of the comprehensive efficiency of production line equipment based on digital twins can solve the problems of offline data statistics and time lag in the prior art, realize the cyclic iteration and twin optimization of the comprehensive efficiency and collaborative control of production line equipment based on digital twins, and improve the comprehensive efficiency of production line equipment.

[0004] For example, a Chinese patent with authorization announcement number CN116933951B discloses a low-carbon park carbon emission monitoring system and method based on big data, which belongs to the field of carbon emission monitoring technology. In order to solve the problem that carbon emissions are difficult to predict effectively and the effect display of control measures is not intuitive, the net carbon emission curve function is derived to obtain the change trend function of the net carbon emissions, which can predict and display the carbon emission change trend of the park in the future, help the system to better monitor and grasp the carbon emissions of the park, ensure the low-carbon development of the park, and adjust the net carbon emission curve based on different park carbon reduction data under different park carbon reduction measures. By generating different carbon reduction emission data curves under different park carbon reduction measures options, after the staff selects different carbon reduction measures options, the system will automatically display the carbon emission data curve after the carbon reduction measures are taken, which is convenient for the staff to conduct manual monitoring and overall regulation.

[0005] The above patents have the problems raised by this background technology: most of them do not consider the accuracy of the collected data, and the data dimensions involved are relatively few. To solve this problem, the present invention proposes a monitoring and optimization method and system based on the comprehensive efficiency of OEE equipment. Summary of the invention

[0006] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0007] In view of the above-mentioned problems existing in the existing monitoring and optimization methods and systems based on the comprehensive efficiency of OEE equipment, the present invention is proposed.

[0008] Therefore, the object of the present invention is to provide a monitoring and optimization method and system based on OEE equipment comprehensive efficiency.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions: real-time collection of equipment operation data and intelligent sampling;

[0010] The device operation data is intelligently sampled, and the function expression of the intelligent sampling is as follows:

[0011] ;

[0012] In the formula, Indicates the minimum cost of collecting equipment operation data, Indicates the cost difference between the two pieces of equipment operation data collected in the brackets, Represents random historical equipment operation data, Representation and Adjacent historical equipment operation data, Indicates real-time equipment operation data. Indicates historical equipment operation data having the smallest cost difference with the real-time equipment operation data;

[0013] The rules of the intelligent sampling are as follows:

[0014] Randomly select a historical device operation data , then select Adjacent historical equipment operation data ;

[0015] If real-time equipment operation data and adjacent historical equipment operation data If there is no other equipment operation data between them, it means that the adjacent historical equipment operation data To provide real-time equipment operation data The historical equipment operation data with the smallest cost difference is recorded as ;

[0016] If real-time equipment operation data and adjacent historical equipment operation data If there is other equipment running data between them, reselect one that matches Adjacent historical equipment operation data ;

[0017] Continuously collecting the real-time device operation data, and continuously iterating the minimum cost of collecting the device operation data;

[0018] like , then reduce the frequency of collecting the real-time equipment operation data;

[0019] like , the frequency of collecting the real-time equipment operation data remains unchanged;

[0020] like , then increase the frequency of collecting the real-time equipment operation data;

[0021] Preprocessing the collected equipment operation data and storing it in a database;

[0022] Calculate the OEE index of the equipment according to the equipment operation data;

[0023] Based on the equipment operation data, the operation status of the equipment is monitored in real time, and the cause of the failure is diagnosed;

[0024] When the equipment is abnormal, an alarm is sounded and relevant personnel are notified;

[0025] By means of charts and dashboards, the operating status of the equipment and the OEE index are monitored in real time, and the overall efficiency of the equipment is visualized;

[0026] According to the operating status of the equipment, analyze the bottleneck of the equipment operation and formulate corresponding improvement plans.

[0027] As a preferred solution of the monitoring and optimization method based on OEE equipment comprehensive efficiency of the present invention, wherein: the equipment operation data includes the equipment's operating time, downtime, production volume, energy consumption and fault information;

[0028] The operating time of the equipment includes the planned operating time and the actual operating time;

[0029] The downtime of the equipment includes planned downtime, downtime loss time and downtime loss time;

[0030] The production capacity of the equipment includes the total production volume and the qualified product volume.

[0031] As a preferred solution of the method for monitoring and optimizing the comprehensive efficiency of OEE equipment according to the present invention, the framework for storing the equipment operation data is as follows:

[0032] The collected historical equipment operation data is aligned, and the collected real-time equipment operation data is parsed, packaged and serialized, and the collected equipment operation data is stored and calculated using an edge computing gateway.

[0033] As a preferred solution of the monitoring and optimization method based on OEE equipment comprehensive efficiency of the present invention, wherein: the OEE indicators include availability, performance efficiency, quality loss rate, energy efficiency and manpower utilization;

[0034] The functional expression of OEE is as follows:

[0035] ;

[0036] In the formula, It represents the overall equipment efficiency. represents the availability of the equipment, represents the performance efficiency of the device, represents the mass loss rate of the device, represents the energy efficiency of the device, represents the manpower utilization rate of the equipment, represents the total operating time of the device, represents the planned downtime of the equipment in question, represents the total production volume of the equipment in question, represents the rated efficiency of the device in question, Indicates the number of qualified products of the equipment described, represents the energy consumption of the device in question, Indicates the number of employees who actually use the equipment for production. Indicates the total number of employees who plan to produce using the equipment in question.

[0037] As a preferred solution of the monitoring and optimization method based on OEE equipment comprehensive efficiency of the present invention, wherein: based on the equipment operation data, real-time monitoring of the operation status of the equipment includes: judging the change trend of the equipment operation data, and the function expression of the change trend of the equipment operation data is as follows:

[0038] ;

[0039] In the formula, represents the variance of the equipment operation data, Indicates the total number of operation data of the device. represents the actual operating time of the device in question, represents the downtime loss time of the equipment in question, Indicates the number of qualified products of the equipment described, represents the energy consumption of the device in question, Indicates the fault information of the device. represents the average value of the actual operating time of the device, represents the average downtime loss time of the equipment in question, It represents the average value of the number of qualified products of the equipment. represents the average value of the energy consumption of the equipment in question, Indicates the average value of the fault information of the device.

[0040] As a preferred solution of the monitoring and optimization method based on the comprehensive efficiency of OEE equipment of the present invention, the function expression for judging whether the equipment is abnormal is as follows:

[0041] ;

[0042] In the formula, represents the abnormality judgment function value of the device, Indicates abnormal equipment operation data. Indicates the maximum threshold of the given device operation data. Indicates the minimum threshold of the given operation data of the device;

[0043] If the abnormality judgment function value of the device is 0, it means that the device is abnormal;

[0044] If the abnormality judgment function value of the device is 1, it means that there is no abnormality in the device.

[0045] As a preferred solution of the monitoring and optimization method based on the comprehensive efficiency of OEE equipment described in the present invention, the operating status of the equipment, the real-time equipment operating data, and the data of abnormal alarm information are visualized in the form of dashboards, bar charts and pie charts.

[0046] The monitoring and optimization system based on the comprehensive efficiency of OEE equipment includes: data acquisition module, data storage and processing module, OEE calculation module, fault detection module, abnormal alarm module, monitoring and visualization module and operation optimization module;

[0047] The data acquisition module is used to collect equipment operation data in real time;

[0048] The data storage and processing module is used to store the device operation data in a database and perform pre-processing;

[0049] The OEE calculation module is used to calculate the OEE index of the equipment according to the equipment operation data;

[0050] The fault detection module is used to monitor the operating status of the device in real time based on the device operating data and diagnose the cause of the fault;

[0051] The abnormal alarm module is used to sound an alarm and notify relevant personnel when an abnormality occurs in the device;

[0052] The monitoring and visualization module is used to monitor the operating status of the equipment and the OEE index in real time through charts and dashboards, and to visualize the overall efficiency of the equipment;

[0053] The operation optimization module is used to analyze the bottleneck of the equipment operation according to the equipment operation data and the OEE index, and formulate corresponding improvement plans.

[0054] A computer device includes a memory for storing instructions; a processor for executing the instructions, so that the computer device executes a monitoring and optimization method based on the comprehensive efficiency of OEE equipment.

[0055] A computer-readable storage medium stores a computer program, which, when executed, implements a monitoring and optimization method based on the OEE equipment comprehensive efficiency.

[0056] The beneficial effects of the present invention are as follows: the present invention collects equipment operation data in real time through intelligent sampling to maintain the accuracy of the collected data; pre-processes the collected equipment operation data and stores it in a database; calculates the OEE index of the equipment according to the equipment operation data, and evaluates the equipment operation efficiency in multiple dimensions; based on the equipment operation data, monitors the operation status of the equipment in real time, diagnoses the cause of the fault, and promptly discovers and solves the fault; when the equipment is abnormal, issues an alarm and notifies relevant personnel so that timely measures can be taken for maintenance and processing; monitors the operation status and OEE index of the equipment in real time through charts and dashboards, and visualizes the comprehensive efficiency of the equipment, which is convenient for managers to monitor and make decisions; analyzes the bottleneck of equipment operation according to the operation status of the equipment, formulates corresponding improvement plans, and improves the efficiency and production capacity of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0058] Figure 1 It is a method flow chart of the monitoring and optimization method based on OEE equipment comprehensive efficiency of the present invention;

[0059] Figure 2 This is a storage framework diagram of the monitoring and optimization method based on the comprehensive efficiency of OEE equipment of the present invention;

[0060] Figure 3 It is a system structure diagram of the monitoring and optimization system based on OEE equipment comprehensive efficiency of the present invention. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0064] Example 1

[0065] In this embodiment, a method flow chart of a monitoring and optimization method based on OEE equipment comprehensive efficiency is provided, such as Figure 1 As shown in the figure, the monitoring and optimization methods based on OEE equipment comprehensive efficiency include:

[0066] The equipment here refers to equipment for home appliance manufacturing.

[0067] S1. Collect the operation data of home appliance manufacturing equipment in real time and perform intelligent sampling.

[0068] The operation data of home appliance manufacturing equipment includes the operation time, downtime, production volume, energy consumption and fault information of home appliance manufacturing equipment.

[0069] The operating time of home appliance manufacturing equipment includes planned operating time and actual operating time.

[0070] The downtime of home appliance manufacturing equipment includes planned downtime and downtime loss time.

[0071] The production volume of home appliance manufacturing equipment includes the total production volume and the number of qualified products.

[0072] Intelligent sampling of the operating data of home appliance manufacturing equipment is performed, and the function expression of intelligent sampling is as follows:

[0073] ;

[0074] In the formula, represents the minimum cost of collecting operation data of home appliance manufacturing equipment, It represents the cost difference between the two home appliance manufacturing equipment operation data collected in the brackets. represents random historical home appliance manufacturing equipment operation data, Representation and Adjacent historical equipment operation data, Indicates real-time operation data of home appliance manufacturing equipment. Represents historical home appliance manufacturing equipment operation data with the smallest cost difference with real-time home appliance manufacturing equipment operation data.

[0075] The rules of smart sampling are as follows:

[0076] Randomly select a historical home appliance manufacturing equipment operation data , then select Adjacent historical home appliance manufacturing equipment operation data ;

[0077] If real-time operation data of home appliance manufacturing equipment and adjacent historical home appliance manufacturing equipment operation data If there is no other home appliance manufacturing equipment operation data between them, it means that the adjacent historical home appliance manufacturing equipment operation data To obtain real-time operation data of home appliance manufacturing equipment The historical home appliance manufacturing equipment operation data with the smallest cost difference is recorded as ;

[0078] If real-time operation data of home appliance manufacturing equipment and adjacent historical home appliance manufacturing equipment operation data If there is other home appliance manufacturing equipment operation data between them, then reselect one that is related to Adjacent historical home appliance manufacturing equipment operation data ;

[0079] Continuously collect real-time operation data of home appliance manufacturing equipment, and continuously iterate the minimum cost of collecting operation data of home appliance manufacturing equipment;

[0080] like , then the frequency of collecting real-time operation data of home appliance manufacturing equipment is reduced;

[0081] like , the frequency of collecting real-time operation data of home appliance manufacturing equipment remains unchanged;

[0082] like , then increase the frequency of collecting real-time operating data of home appliance manufacturing equipment.

[0083] When collecting the operation data of home appliance manufacturing equipment, check the value that changes after the collection operation occurs in the computer thread as the cost difference, first randomly select a historical home appliance manufacturing equipment operation data, and then select the historical home appliance manufacturing equipment operation data adjacent to the random historical home appliance manufacturing equipment operation data. If there is no other home appliance manufacturing equipment operation data between the adjacent historical home appliance manufacturing equipment operation data and the real-time home appliance manufacturing equipment operation data, then the adjacent historical home appliance manufacturing equipment operation data is the historical home appliance manufacturing equipment operation data with the smallest cost difference with the real-time home appliance manufacturing equipment operation data. If there is, reselect an adjacent historical home appliance manufacturing equipment operation data, and then adjust the frequency and accuracy of collecting the real-time home appliance manufacturing equipment operation data according to the judgment conditions of the above formula.

[0084] S2. Pre-process the collected operation data of the home appliance manufacturing equipment and store it in a database.

[0085] The framework diagram for storing the operation data of home appliance manufacturing equipment is as follows Figure 2 As shown, the details are as follows:

[0086] The collected historical operation data of home appliance manufacturing equipment is aligned, and the collected real-time operation data of home appliance manufacturing equipment is parsed, packaged and serialized, and the edge computing gateway is used to store and calculate the collected operation data of home appliance manufacturing equipment.

[0087] The edge computing gateway can adapt to the protocols of massive heterogeneous home appliance manufacturing equipment, collect data through the network, support local data caching and local computing, and also support data cleaning. It is suitable for networking of home appliance manufacturing equipment in various industries.

[0088] S3. Calculate the OEE index of home appliance manufacturing equipment based on the operation data of home appliance manufacturing equipment.

[0089] OEE indicators include availability, performance efficiency, quality loss rate, energy efficiency and manpower utilization.

[0090] The functional expression of the availability of home appliance manufacturing equipment is as follows:

[0091] ;

[0092] In the formula, Indicates the availability of home appliance manufacturing equipment, Indicates the actual operating time of home appliance manufacturing equipment, represents the planned operating time of the home appliance manufacturing equipment, represents the total operating time of the appliance manufacturing equipment, represents the planned downtime of home appliance manufacturing equipment, Represents the downtime loss time of home appliance manufacturing equipment.

[0093] The functional expression of the performance efficiency of household appliance manufacturing equipment is as follows:

[0094] ;

[0095] In the formula, Indicates the performance efficiency of home appliance manufacturing equipment, represents the total production volume of home appliance manufacturing equipment, Indicates the rated efficiency of home appliance manufacturing equipment, represents the total operating time of the appliance manufacturing equipment, represents the planned downtime of home appliance manufacturing equipment, Represents the downtime loss time of home appliance manufacturing equipment.

[0096] The functional expression of the mass loss rate of household appliance manufacturing equipment is as follows:

[0097] ;

[0098] In the formula, represents the quality loss rate of home appliance manufacturing equipment, represents the total production volume of home appliance manufacturing equipment, Indicates the number of qualified products of home appliance manufacturing equipment.

[0099] The functional expression of energy efficiency of household appliance manufacturing equipment is as follows:

[0100] ;

[0101] In the formula, Indicates the energy efficiency of home appliance manufacturing equipment, represents the total production volume of home appliance manufacturing equipment, Indicates the energy consumption of home appliance manufacturing equipment.

[0102] The functional expression of manpower utilization rate of household appliance manufacturing equipment is as follows:

[0103] ;

[0104] In the formula, Indicates the manpower utilization rate of home appliance manufacturing equipment, Indicates the number of employees who actually use home appliance manufacturing equipment for production, Indicates the total number of employees who plan to use the home appliance manufacturing equipment for production.

[0105] The function expression of OEE is as follows:

[0106] ;

[0107] In the formula, It represents the overall efficiency of home appliance manufacturing equipment. Indicates the availability of home appliance manufacturing equipment, Indicates the performance efficiency of home appliance manufacturing equipment, represents the quality loss rate of home appliance manufacturing equipment, Indicates the energy efficiency of home appliance manufacturing equipment, Indicates the manpower utilization rate of home appliance manufacturing equipment, Indicates the actual operating time of home appliance manufacturing equipment, represents the planned operating time of the home appliance manufacturing equipment, represents the total operating time of the appliance manufacturing equipment, represents the planned downtime of home appliance manufacturing equipment, Indicates the downtime loss time of home appliance manufacturing equipment, represents the total production volume of home appliance manufacturing equipment, Indicates the rated efficiency of home appliance manufacturing equipment, Indicates the number of qualified products of home appliance manufacturing equipment, represents the energy consumption of home appliance manufacturing equipment, Indicates the number of employees who actually use home appliance manufacturing equipment for production, Indicates the total number of employees who plan to use the home appliance manufacturing equipment for production.

[0108] The comprehensive efficiency of home appliance manufacturing equipment is a relatively mature indicator for measuring enterprise productivity. In addition to the availability, performance efficiency and quality loss rate of home appliance manufacturing equipment, the energy consumption efficiency and manpower utilization rate of home appliance manufacturing equipment are also used.

[0109] Among them, the availability rate of household appliance manufacturing equipment refers to the time utilization of household appliance manufacturing equipment, mainly considering the losses caused by downtime; the performance efficiency of household appliance manufacturing equipment refers to the situation of exerting the operating performance of household appliance manufacturing equipment; the quality loss rate of household appliance manufacturing equipment refers to the situation where there are losses in the production of household appliance manufacturing equipment; the energy consumption efficiency of household appliance manufacturing equipment refers to the relationship between the energy consumed and the total production during the production process; the manpower utilization rate of household appliance manufacturing equipment refers to the relationship between the labor force effectively produced using household appliance manufacturing equipment within a certain period of time and the total labor force.

[0110] The comprehensive efficiency of home appliance manufacturing equipment here can reflect the comprehensive indicators of home appliance manufacturing equipment in time utilization, performance, product quality, energy consumption and manpower utilization in a quantitative way. It can comprehensively and objectively reflect the operating status of home appliance manufacturing equipment and facilitate the rapid discovery of process bottlenecks and efficiency losses.

[0111] S4. Based on the operation data of the home appliance manufacturing equipment, the operation status of the home appliance manufacturing equipment is monitored in real time, and the cause of the fault is diagnosed.

[0112] Determine the changing trend of the operation data of home appliance manufacturing equipment. The function expression of the changing trend of the operation data of home appliance manufacturing equipment is as follows:

[0113] ;

[0114] In the formula, represents the variance of the operating data of home appliance manufacturing equipment, Indicates the total number of home appliance manufacturing equipment operation data, Indicates the actual operating time of home appliance manufacturing equipment, Indicates the downtime loss time of home appliance manufacturing equipment, Indicates the number of qualified products of home appliance manufacturing equipment, represents the energy consumption of home appliance manufacturing equipment, Indicates fault information of home appliance manufacturing equipment. It represents the average actual operating time of home appliance manufacturing equipment. It represents the average downtime loss time of home appliance manufacturing equipment. Indicates the average value of the number of qualified products of home appliance manufacturing equipment. Indicates the average value of energy consumption of home appliance manufacturing equipment, Indicates the average value of failure information of home appliance manufacturing equipment.

[0115] Variance is the degree of deviation from the center, which is used to characterize the volatility and stability of data. When the data distribution is more dispersed, the sum of the squares of the differences between each data and the mean is larger, and the variance is larger; when the data distribution is more concentrated, the sum of the squares of the differences between each data and the mean is smaller; therefore, the larger the variance, the greater the volatility of the data, and the smaller the variance, the smaller the volatility of the data.

[0116] The above formula means that the variance of the actual operating time, downtime loss time, qualified product quantity, energy consumption and fault information of household appliance manufacturing equipment is calculated respectively. In order to facilitate the calculation, the fault information is converted into data labels in the range of The closer it is to 1, the more serious the fault is, and the closer it is to 0, the less serious the fault is. The size of the variance is used to determine the changing trend of the operating data of the home appliance manufacturing equipment, determine the operating status of the home appliance manufacturing equipment, and determine the location of the fault through the operating status of the home appliance manufacturing equipment, thereby diagnosing the cause of the failure of the home appliance manufacturing equipment.

[0117] S5. When an abnormality occurs in home appliance manufacturing equipment, an alarm will be issued and relevant personnel will be notified so that timely measures can be taken for maintenance and processing.

[0118] The function expression for judging abnormality of home appliance manufacturing equipment is as follows:

[0119] ;

[0120] In the formula, represents the abnormality judgment function value of the home appliance manufacturing equipment, Indicates abnormal home appliance manufacturing equipment operation data, Indicates the maximum threshold of the given home appliance manufacturing equipment operation data, Indicates the minimum threshold of the given home appliance manufacturing equipment operating data.

[0121] If the abnormality judgment function value of the home appliance manufacturing equipment is 0, it means that the home appliance manufacturing equipment has an abnormality;

[0122] If the abnormality judgment function value of the home appliance manufacturing equipment is 1, it means that there is no abnormality in the home appliance manufacturing equipment.

[0123] During the processing, the selection of two thresholds is the most critical. If the data threshold range is too large, the judgment of abnormal data will not be thorough enough. If the data threshold range is too small, the originally normal data will be filtered out. Therefore, in the early historical data, it is necessary to carefully observe the characteristics of the operating data of home appliance manufacturing equipment and compare the change characteristics respectively, so as to select the appropriate data threshold.

[0124] S6. Use charts and dashboards to monitor the operating status and OEE indicators of home appliance manufacturing equipment in real time, and visualize the comprehensive efficiency of home appliance manufacturing equipment to facilitate monitoring and decision-making by managers.

[0125] The operating status of home appliance manufacturing equipment, real-time home appliance manufacturing equipment operating data, and abnormal alarm information are visualized in the form of dashboards, bar charts, and pie charts.

[0126] It provides intelligent and visual display of the operating status of home appliance manufacturing equipment, real-time home appliance manufacturing equipment operating data, abnormal alarm information, task statistics, and data statistics. It visualizes the production process, facilitates management personnel to monitor and make decisions, and realizes the industry's full intelligent connection benchmark demonstration effect.

[0127] S7. Analyze the bottlenecks of the operation of home appliance manufacturing equipment according to its operating status, formulate corresponding improvement plans, and improve the efficiency and production capacity of home appliance manufacturing equipment.

[0128] According to the operating status of home appliance manufacturing equipment, the bottlenecks of home appliance manufacturing equipment are analyzed, corresponding improvement plans are formulated according to the corresponding bottlenecks, and the problematic home appliance manufacturing equipment is repaired in time to ensure the sustainability of the operation of home appliance manufacturing equipment and improve the efficiency and production capacity of home appliance manufacturing equipment.

[0129] Example 2

[0130] In this embodiment, a system structure diagram of a monitoring and optimization system based on the comprehensive efficiency of OEE home appliance manufacturing equipment is provided, such as Figure 3 As shown, the monitoring and optimization system based on the comprehensive efficiency of OEE home appliance manufacturing equipment includes a data acquisition module, a data storage and processing module, an OEE calculation module, a fault detection module, an abnormal alarm module, a monitoring and visualization module and an operation optimization module.

[0131] The data acquisition module is used to collect the operating data of home appliance manufacturing equipment in real time and perform intelligent sampling.

[0132] The collected operation data of home appliance manufacturing equipment include the operation time, downtime, production volume, energy consumption and fault information of home appliance manufacturing equipment. These operation data of home appliance manufacturing equipment can be obtained from the production line, and intelligent sampling is used to control the accuracy and frequency of the real-time collection of home appliance manufacturing equipment operation data.

[0133] The data storage and processing module is used to store the operating data of the home appliance manufacturing equipment into the database and perform pre-processing.

[0134] The collected historical operation data of home appliance manufacturing equipment is aligned, the collected real-time operation data of home appliance manufacturing equipment is parsed, packaged and serialized, and the edge computing gateway is used to store and calculate the operation data of home appliance manufacturing equipment.

[0135] The OEE calculation module is used to calculate the OEE index of home appliance manufacturing equipment based on the operation data of home appliance manufacturing equipment.

[0136] The availability, performance efficiency, quality loss rate, energy efficiency and manpower utilization rate of home appliance manufacturing equipment are calculated to calculate the OEE value, which objectively reflects the operating status of home appliance manufacturing equipment and facilitates the discovery of process bottlenecks and efficiency losses.

[0137] The fault detection module is used to monitor the operating status of home appliance manufacturing equipment in real time based on the operating data of home appliance manufacturing equipment and diagnose the cause of the fault.

[0138] The changing trend of the operation data of the home appliance manufacturing equipment is used to determine the operation status of the home appliance manufacturing equipment, and the location of the fault is determined by the operation status of the home appliance manufacturing equipment, thereby diagnosing the cause of the failure of the home appliance manufacturing equipment.

[0139] The abnormal alarm module is used to sound an alarm and notify relevant personnel when abnormalities occur in home appliance manufacturing equipment so that timely measures can be taken for maintenance and processing.

[0140] The abnormality judgment function is used to judge whether the home appliance manufacturing equipment has an abnormality. When the home appliance manufacturing equipment has an abnormality, an alarm is issued and relevant personnel are notified so that timely measures can be taken for maintenance and processing.

[0141] The monitoring and visualization module is used to monitor the operating status and OEE indicators of home appliance manufacturing equipment in real time through charts and dashboards, and to visualize the comprehensive efficiency of home appliance manufacturing equipment, making it easier for managers to monitor and make decisions.

[0142] It provides intelligent and visual display of the operating status of home appliance manufacturing equipment, real-time home appliance manufacturing equipment operating data, abnormal alarm information, task statistics, and data statistics. It visualizes the production process, facilitates management personnel to monitor and make decisions, and realizes the industry's full intelligent connection benchmark demonstration effect.

[0143] The operation optimization module is used to analyze the bottlenecks of home appliance manufacturing equipment operation based on the home appliance manufacturing equipment operation data and OEE indicators, formulate corresponding improvement plans, and improve the efficiency and production capacity of home appliance manufacturing equipment.

[0144] According to the operating status of home appliance manufacturing equipment, the bottlenecks of home appliance manufacturing equipment are analyzed, corresponding improvement plans are formulated according to the corresponding bottlenecks, and the problematic home appliance manufacturing equipment is repaired in time to ensure the sustainability of the operation of home appliance manufacturing equipment and improve the efficiency and production capacity of home appliance manufacturing equipment.

[0145] Example 3

[0146] In this embodiment, a computer device is provided, including a memory and a processor, the memory is used to store instructions, and the processor is used to execute the instructions, so that the computer device executes the steps of implementing the above-mentioned monitoring and optimization method based on the comprehensive efficiency of OEE equipment.

[0147] Example 4

[0148] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the steps of the above-mentioned monitoring and optimization method based on the comprehensive efficiency of OEE equipment are implemented.

[0149] The computer-readable storage medium includes: a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, and other media for storing program codes.

[0150] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. The monitoring and optimization method based on OEE equipment comprehensive efficiency is characterized by: include: Collect equipment operation data in real time and perform intelligent sampling; The device operation data is intelligently sampled, and the function expression of the intelligent sampling is as follows: ； In the formula, Indicates the minimum cost of collecting equipment operation data, Indicates the cost difference between the two pieces of equipment operation data collected in the brackets, Represents random historical equipment operation data, Representation and Adjacent historical equipment operation data, Indicates real-time equipment operation data. Indicates historical equipment operation data having the smallest cost difference with the real-time equipment operation data; The rules of the intelligent sampling are as follows: Randomly select a historical device operation data , then select Adjacent historical equipment operation data ; If real-time equipment operation data and adjacent historical equipment operation data If there is no other equipment operation data between them, it means that the adjacent historical equipment operation data To provide real-time equipment operation data The historical equipment operation data with the smallest cost difference is recorded as ; If real-time equipment operation data and adjacent historical equipment operation data If there is other equipment running data between them, reselect one that matches Adjacent historical equipment operation data ; Continuously collecting the real-time device operation data, and continuously iterating the minimum cost of collecting the device operation data; like , then reduce the frequency of collecting the real-time equipment operation data; like , the frequency of collecting the real-time equipment operation data remains unchanged; like , then increase the frequency of collecting the real-time equipment operation data; Preprocessing the collected equipment operation data and storing it in a database; Calculate the OEE index of the equipment according to the equipment operation data; Based on the equipment operation data, the operation status of the equipment is monitored in real time, and the cause of the failure is diagnosed; When the equipment is abnormal, an alarm is sounded and relevant personnel are notified; By means of charts and dashboards, the operating status of the equipment and the OEE index are monitored in real time, and the overall efficiency of the equipment is visualized; According to the operating status of the equipment, analyze the bottleneck of the equipment operation and formulate corresponding improvement plans.

2. The monitoring and optimization method based on OEE equipment comprehensive efficiency according to claim 1, characterized in that: The equipment operation data includes equipment operation time, downtime, production volume, energy consumption and fault information; The operating time of the equipment includes the planned operating time and the actual operating time; The downtime of the equipment includes planned downtime, downtime loss time and downtime loss time; The production capacity of the equipment includes the total production volume and the qualified product volume.

3. The method for monitoring and optimizing the overall efficiency of OEE equipment according to claim 2, characterized in that: The framework for storing the device operation data is as follows: The collected historical equipment operation data is aligned, and the collected real-time equipment operation data is parsed, packaged and serialized, and the collected equipment operation data is stored and calculated using an edge computing gateway.

4. The monitoring and optimization method based on OEE equipment comprehensive efficiency as claimed in claim 3, characterized in that: The OEE indicators include availability, performance efficiency, quality loss rate, energy efficiency and manpower utilization; The functional expression of OEE is as follows: ； In the formula, It represents the overall equipment efficiency. represents the availability of the equipment, represents the performance efficiency of the device, represents the mass loss rate of the device, represents the energy efficiency of the device, represents the manpower utilization rate of the equipment, represents the total operating time of the device, represents the planned downtime of the equipment in question, represents the total production volume of the equipment in question, represents the rated efficiency of the device in question, Indicates the number of qualified products of the equipment described, represents the energy consumption of the device in question, Indicates the number of employees who actually use the equipment for production. Indicates the total number of employees who plan to produce using the equipment in question.

5. The method for monitoring and optimizing the overall efficiency of OEE equipment according to claim 4, characterized in that: Based on the device operation data, the operation status of the device is monitored in real time, including: determining the change trend of the device operation data, and the function expression of the change trend of the device operation data is as follows: ； In the formula, represents the variance of the equipment operation data, Indicates the total number of operation data of the device. represents the actual operating time of the device in question, Indicates the downtime loss time of the equipment in question, Indicates the number of qualified products of the equipment described, represents the energy consumption of the device in question, Indicates the fault information of the device. represents the average value of the actual operating time of the device, represents the average downtime loss time of the equipment in question, It represents the average value of the number of qualified products of the equipment. represents the average value of the energy consumption of the equipment in question, Indicates the average value of the fault information of the device.

6. The method for monitoring and optimizing the overall efficiency of OEE equipment according to claim 5, characterized in that: The function expression for judging whether the device is abnormal is as follows: ； In the formula, represents the abnormality judgment function value of the device, Indicates abnormal equipment operation data. Indicates the maximum threshold of the given device operation data. Indicates the minimum threshold of the given operation data of the device; If the abnormality judgment function value of the device is 0, it means that the device is abnormal; If the abnormality judgment function value of the device is 1, it means that there is no abnormality in the device.

7. The method for monitoring and optimizing the overall efficiency of OEE equipment according to claim 6, characterized in that: The operating status of the equipment, the real-time equipment operating data, and the abnormal alarm information data are visualized in the form of dashboards, bar charts, and pie charts.

8. A monitoring and optimization system based on the comprehensive efficiency of OEE equipment, used to implement the monitoring and optimization method based on the comprehensive efficiency of OEE equipment according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, data storage and processing module, OEE calculation module, fault detection module, abnormal alarm module, monitoring and visualization module and operation optimization module; The data acquisition module is used to collect equipment operation data in real time; The data storage and processing module is used to store the device operation data in a database and perform pre-processing; The OEE calculation module is used to calculate the OEE index of the equipment according to the equipment operation data; The fault detection module is used to monitor the operating status of the device in real time based on the device operating data and diagnose the cause of the fault; The abnormal alarm module is used to sound an alarm and notify relevant personnel when an abnormality occurs in the device; The monitoring and visualization module is used to monitor the operating status of the equipment and the OEE index in real time through charts and dashboards, and to visualize the overall efficiency of the equipment; The operation optimization module is used to analyze the bottleneck of the equipment operation according to the equipment operation data and the OEE index, and formulate corresponding improvement plans.

9. A computer device, characterized in that: include: A memory for storing instructions; A processor is used to execute the instructions so that the computer device executes the monitoring and optimization method based on the comprehensive efficiency of OEE equipment as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the monitoring and optimization method based on OEE equipment comprehensive efficiency as described in any one of claims 1 to 7 is implemented.

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