CN116502925A

CN116502925A - Digital factory equipment inspection evaluation method, system and medium based on big data

Info

Publication number: CN116502925A
Application number: CN202310772335.4A
Authority: CN
Inventors: 李大利; 王毅; 袁石安
Original assignee: Shenzhen Pfiter Information Technology Co ltd
Current assignee: Shenzhen Pfiter Information Technology Co ltd
Priority date: 2023-06-28
Filing date: 2023-06-28
Publication date: 2023-07-28
Anticipated expiration: 2043-06-28
Also published as: CN116502925B

Abstract

The application provides a digital factory equipment inspection evaluation method, system and medium based on big data. The method comprises the following steps: acquiring the running performance of a single device and the data of an abnormal log, combining the data processing of standard indexes of the type and class devices to which the device belongs to obtain performance detection deviation degree data, comparing the performance detection deviation degree distribution data with the performance detection deviation degree distribution data of the type and class devices to obtain a device group performance difference discrete index, combining the obtained ring condition stability excitation coefficient and the system stability compensation coefficient with the performance detection deviation degree data to obtain a performance effective running detection index, and comparing the performance effective running detection index with the obtained device performance detection correction threshold to judge and evaluate the running condition of the device; the method comprises the steps of processing the factory equipment according to the deviation condition of the running performance and the related detection coefficient of the environmental working condition and the system condition based on the big data to obtain the detection index of equipment running, and carrying out inspection evaluation on the equipment, so that an intelligent technology for carrying out inspection evaluation on the equipment running condition through the big data is realized.

Description

Digital factory equipment inspection evaluation method, system and medium based on big data

Technical Field

The application relates to the technical field of big data and plant equipment maintenance, in particular to a digital plant equipment inspection evaluation method, a digital plant equipment inspection evaluation system and a digital plant equipment inspection evaluation medium based on big data.

Background

The inspection of the equipment operation performance of the digital factory is one of the cores of the factory operation supervision and the quality supervision, and as the models of various equipment of the factory are various, the working operation environment of each equipment and the operation condition of the system of each equipment are different, so that the performance inspection of the equipment is non-uniform and variable, and the performance quality of each equipment of the similar equipment is different due to the difference of the self working attribute or the using mode, the performance judgment and the identification of each single equipment of the similar equipment are also non-uniform, so that the inspection of the single equipment of each equipment of the digital factory is free from the evaluation means and the judgment standard, and the technical means for effectively evaluating and accurately judging the performance condition of each equipment of the digital factory according to the performance of each equipment of the digital factory and the performance deviation attribute of the equipment of the same class, the environment, the working condition and the system element are lacked.

In view of the above problems, an effective technical solution is currently needed.

Disclosure of Invention

The invention aims to provide a digital factory equipment inspection evaluation method, a digital factory equipment inspection evaluation system and a digital factory equipment inspection evaluation medium based on big data, which can process factory equipment according to running performance deviation conditions and environmental working conditions and system condition related detection coefficients to obtain equipment running detection indexes to carry out inspection evaluation on equipment, and realize intelligent technology of carrying out inspection evaluation on equipment running conditions through the big data.

The application also provides a digital factory equipment inspection evaluation method based on big data, which comprises the following steps:

acquiring equipment operation monitoring information and equipment operation log information of a single equipment in various types of equipment in a digital factory within a preset time period, and extracting equipment operation performance characteristic data and equipment operation abnormal log information data;

acquiring preset equipment standard operation index data of equipment of the type of the single equipment, processing the preset equipment standard operation index data and the equipment operation performance characteristic data to acquire operation performance deviation degree data of the single equipment, and processing the operation performance deviation degree data by combining the equipment operation abnormal log information data to acquire performance detection deviation degree data;

Acquiring performance detection deviation degree distribution data of equipment of the same type as the single equipment in the preset time period, and comparing the performance detection deviation degree distribution data with the performance detection deviation degree data to obtain an equipment group performance difference discrete index;

acquiring operation environment working condition detection data of the single equipment in the preset time period and system state monitoring data of a production operation system where the single equipment is located, and respectively processing the operation environment working condition detection data and the system state monitoring data to acquire a ring condition stability excitation coefficient and a system stability compensation coefficient;

correcting the performance detection deviation data according to the device group performance difference discrete index and the ring condition stability excitation coefficient and the system stability compensation coefficient to obtain a performance effective operation detection index of the single device;

acquiring a preset performance detection threshold value of the type class equipment to which the single equipment belongs, and carrying out weighted correction with the performance detection deviation distribution data to acquire an equipment performance detection correction threshold value;

and comparing the threshold value according to the performance effective operation detection index of the single equipment with the equipment performance detection correction threshold value of the equipment with the model type, judging and evaluating the operation condition of all the single equipment in the equipment with each model type according to the threshold value comparison result, and displaying.

Optionally, in the big data based digital plant equipment inspection and evaluation method described in the present application, the obtaining equipment operation monitoring information and equipment operation log information of a single equipment in each type of equipment of the digital plant in a preset time period, and extracting equipment operation performance characteristic data and equipment operation abnormal log information data includes:

acquiring equipment operation monitoring information of single equipment in various types of equipment in a digital factory in a preset time period, and extracting equipment operation performance characteristic data comprising operation efficiency data, processing defective rate data, operation power consumption data and operation abnormality index quantity data according to the equipment operation monitoring information;

and acquiring the equipment operation log information of the single equipment in a preset time period, and extracting equipment operation abnormal log information data, wherein the equipment operation abnormal log information data comprises the number of operation abnormal prompt frequencies and the number of abnormal alarm stages of abnormal prompts.

Optionally, in the big data based digital factory equipment inspection and evaluation method described in the present application, the obtaining the preset equipment standard operation index data of the equipment of the type class to which the single equipment belongs, processing the preset equipment standard operation index data and the equipment operation performance characteristic data to obtain the operation performance deviation degree data of the single equipment, and processing the operation performance deviation degree data by combining with the equipment operation abnormality log information data to obtain the performance detection deviation degree data includes:

Obtaining preset equipment standard operation index data of equipment of the type of the single equipment through a preset digital factory equipment production parameter database, wherein the preset equipment standard operation index data comprises efficiency index data, failure rate index data, power consumption index data and abnormal quantity index data;

processing according to the standard operation index data of the preset equipment and the operation performance characteristic data of the equipment to obtain operation performance deviation degree data of the single equipment;

processing according to the running performance deviation data and combining the running abnormal prompt frequency times and the abnormal alarm progression to obtain performance detection deviation data of the single equipment;

the calculation formula of the performance detection deviation data is as follows:

；

wherein, the liquid crystal display device comprises a liquid crystal display device,detecting deviation data for performance, +.>For the running performance deviation data, +.>For the number of abnormal operation frequency->The number of abnormality alert stages for the ith abnormality alert, n being the number of abnormality alerts in a predetermined period,/for the predetermined period of time>、/>Is a preset characteristic coefficient.

Optionally, in the method for inspecting and evaluating digital factory equipment based on big data described in the present application, the obtaining the performance detection deviation distribution data of the equipment of the same type as the single equipment in the preset time period, and comparing the performance detection deviation distribution data with the performance detection deviation data, and obtaining the equipment group performance difference discrete index includes:

Acquiring performance detection deviation distribution data of the equipment of the same type as the single equipment in the digital factory in the preset time period, wherein the performance detection deviation distribution data comprises performance detection deviation data of the equipment of the same type;

the performance detection deviation data sets are formed according to the performance detection deviation data sets of the plurality of the same-model type devices;

comparing the performance detection deviation degree data set with the performance detection deviation degree data of the single equipment to obtain an equipment group performance difference dispersion index of the single equipment;

the calculation formula of the device group performance difference dispersion index is as follows:

；

wherein, the liquid crystal display device comprises a liquid crystal display device,discrete index for device group performance difference, +.>Detecting deviation data for the performance of the kth individual device,>for the preset characteristic coefficient corresponding to the kth individual device,/->Correction factor for the deviation of the preset performance for the type of device to which the individual device belongs>For the performance detection deviation data set, m is the number of single devices contained in the single device model class device, +.>Detecting deviation data for the performance of the j-th single device of the m single devices,/for the performance of the j-th single device>The preset characteristic coefficient is the j-th single device in m single devices.

Optionally, in the method for inspecting and evaluating digital plant equipment based on big data described in the present application, the obtaining the operation environment condition detection data of the single equipment in the preset time period and the system state monitoring data of the production operation system where the single equipment is located, and processing the operation environment condition detection data and the system state monitoring data respectively to obtain the ring condition stability excitation coefficient and the system stability compensation coefficient includes:

acquiring operation environment working condition detection data of the single equipment in the preset time period, wherein the operation environment working condition detection data comprise temperature and humidity environment data, overload operation monitoring data, a life stability index and a predicted fault rate frequency;

acquiring system state monitoring data of a production operation system where the single equipment is located in the preset time period, wherein the system state monitoring data comprise system overload operation data, system failure rate data and system operation total efficiency data;

and respectively processing the operating environment working condition detection data and the system state monitoring data to correspondingly obtain a ring condition stability excitation coefficient and a system stability compensation coefficient.

Optionally, in the method for evaluating the patrol of digital plant equipment based on big data described in the present application, the correcting the performance detection deviation data according to the equipment group performance difference dispersion index and the ring condition stability excitation coefficient and the system stability compensation coefficient to obtain a performance effective operation detection index of the single equipment includes:

Correcting the performance detection deviation data of the single equipment according to the equipment group performance difference dispersion index and combining the ring condition stability excitation coefficient and the system stability compensation coefficient to obtain a performance effective operation detection index of the single equipment;

the correction calculation formula of the performance effective operation detection index is as follows:

；

wherein, the liquid crystal display device comprises a liquid crystal display device,run detection index for performance effectiveness, +.>Detecting deviation data for performance, +.>Discrete index for device group performance difference, +.>For the stability factor of the ring condition, < > excitation>Compensating coefficient for system stability>、/>、/>、/>Is a preset characteristic coefficient.

Optionally, in the method for inspecting and evaluating digital factory equipment based on big data described in the present application, the obtaining a preset performance detection threshold of a type class equipment to which the single equipment belongs, and performing weighted correction with the performance detection deviation distribution data, to obtain an equipment performance detection correction threshold includes:

acquiring a preset performance detection threshold value of the type and class equipment to which the single equipment belongs through the preset digital factory equipment production parameter database;

performing weighted correction according to the preset performance detection threshold and the performance detection deviation data set to obtain an equipment performance detection correction threshold;

The correction formula of the equipment performance detection correction threshold value is as follows:

；

wherein, the liquid crystal display device comprises a liquid crystal display device,correction threshold for device performance detection,/->For performance detection bias data set, m is the number of individual devices, +.>A performance detection threshold is preset.

In a second aspect, the present application provides a digital plant inspection evaluation system based on big data, the system comprising: the system comprises a memory and a processor, wherein the memory comprises a program of a digital factory equipment inspection evaluation method based on big data, and the program of the digital factory equipment inspection evaluation method based on the big data realizes the following steps when being executed by the processor:

Optionally, in the big data based digital plant equipment inspection and evaluation system described in the present application, the obtaining the equipment operation monitoring information and the equipment operation log information of the single equipment in each type of equipment of the digital plant in the preset time period, and extracting the equipment operation performance characteristic data and the equipment operation abnormal log information data includes:

In a third aspect, the present application further provides a computer readable storage medium, where the computer readable storage medium includes a big data based digital plant inspection evaluation method program, where the big data based digital plant inspection evaluation method program, when executed by a processor, implements the steps of the big data based digital plant inspection evaluation method according to any one of the above.

As can be seen from the above, the digital factory equipment inspection and evaluation method, system and medium based on big data provided by the application obtain performance detection deviation degree data by obtaining equipment operation performance characteristic data and equipment operation abnormality log information data of a single equipment and combining with preset equipment standard operation index data processing of equipment of a model type to which the equipment belongs, obtain performance detection deviation degree distribution data of equipment of a same model type to the single equipment, compare the obtained equipment group performance difference discrete index, combine the obtained ring condition stability excitation coefficient and system stability compensation coefficient obtained by processing operation environment working condition detection data and system state monitoring data to process the performance detection deviation degree data to obtain a performance effective operation detection index of the single equipment, compare the performance detection deviation degree data with an equipment performance detection correction threshold obtained by weighting correction according to a preset performance detection threshold and the performance detection deviation degree distribution data, and judge and evaluate the operation condition of the equipment according to the result and display; the method comprises the steps of processing the factory equipment according to the deviation condition of the running performance and the related detection coefficient of the environmental working condition and the system condition based on the big data to obtain the detection index of equipment running, and carrying out inspection evaluation on the equipment, so that an intelligent technology for carrying out inspection evaluation on the equipment running condition through the big data is realized.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objects and other advantages of the present application may be realized and attained by the structure particularly pointed out in the written description and drawings.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flowchart of a digital factory equipment inspection and evaluation method based on big data provided in an embodiment of the present application;

FIG. 2 is a flowchart of acquiring device operation performance characteristic data and device operation anomaly log information data according to the big data-based digital factory device inspection evaluation method provided in the embodiment of the present application;

FIG. 3 is a flowchart of obtaining performance detection deviation data of a big data based digital factory equipment inspection and evaluation method according to an embodiment of the present application;

Fig. 4 is a schematic structural diagram of a digital factory equipment inspection and evaluation system based on big data according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.

It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.

Referring to fig. 1, fig. 1 is a flowchart of a digital factory equipment inspection and evaluation method based on big data in some embodiments of the present application. The digital factory equipment inspection evaluation method based on big data is used in terminal equipment, such as a computer, a mobile phone terminal and the like. The digital factory equipment inspection evaluation method based on big data comprises the following steps:

s101, acquiring equipment operation monitoring information and equipment operation log information of a single equipment in various types of equipment of a digital factory in a preset time period, and extracting equipment operation performance characteristic data and equipment operation abnormal log information data;

s102, acquiring preset equipment standard operation index data of equipment of a model class to which the single equipment belongs, processing the preset equipment standard operation index data and the equipment operation performance characteristic data to acquire operation performance deviation degree data of the single equipment, and processing the operation performance deviation degree data by combining the equipment operation abnormality log information data to acquire performance detection deviation degree data;

s103, acquiring performance detection deviation degree distribution data of the equipment with the same type as the single equipment in the preset time period, and comparing the performance detection deviation degree distribution data with the performance detection deviation degree data to obtain an equipment group performance difference dispersion index;

S104, acquiring operation environment working condition detection data of the single equipment in the preset time period and system state monitoring data of a production operation system where the single equipment is located, and respectively processing the operation environment working condition detection data and the system state monitoring data to acquire a ring condition stability excitation coefficient and a system stability compensation coefficient;

s105, correcting the performance detection deviation data according to the device group performance difference discrete index and combining the ring condition stability excitation coefficient and the system stability compensation coefficient to obtain a performance effective operation detection index of the single device;

s106, acquiring a preset performance detection threshold of the type class equipment to which the single equipment belongs, and carrying out weighted correction with the performance detection deviation distribution data to acquire an equipment performance detection correction threshold;

s107, comparing the performance effective operation detection index of the single equipment with the equipment performance detection correction threshold value of the equipment of the type of the single equipment, judging and evaluating the operation condition of all the single equipment in the equipment of the type of the single equipment according to the threshold value comparison result, and displaying.

It should be noted that, because the performance differences of the individual devices exist in the devices of the types in the digital factory, and the evaluation of the operation conditions of the devices also receives the influence and the interference of the environment working conditions where the devices are operated and the system conditions of the devices, meanwhile, the different performance deviations exist in the devices of the same type due to the influence of the devices or the environment and the system, so that the performance of each single device is within the common range of the performance deviations of the devices of the same type, and the performance deviation of each single device exceeds the common range of the deviation of the devices of the same type, namely, the deviation is not within the deviation margin range, so that the performance deviation exists among the independent devices, therefore, in order to accurately identify the performance of the individual independent devices, taking the working operation environment of the equipment, the operation condition of the system and the deviation condition of the performance difference degree of the single equipment and the similar equipment into consideration, obtaining the accurate performance detection result of the single equipment through correction and compensation processing, obtaining the performance detection deviation degree data by obtaining the equipment operation performance characteristic data and the equipment operation abnormality log information data of the single equipment in various types of equipment and combining the preset equipment standard operation index data of the equipment of the type of equipment, comparing the performance detection deviation degree data of the single equipment with the deviation distribution condition of the performance detection deviation degree of the equipment of the type of the equipment to obtain the discrete condition of the performance deviation degree between the single equipment and the equipment group of the type of the equipment, namely the equipment group performance difference discrete index, and processing the acquired operating environment working condition detection data of the single equipment and the system state monitoring data of the system to acquire a ring condition stability excitation coefficient and a system stability compensation coefficient, namely an environment working condition stability influence coefficient of equipment performance and a system operation influence compensation coefficient of equipment performance stability, processing the performance detection deviation data by combining the two coefficients with the performance detection deviation data to acquire a performance effective operation detection index of the single equipment, performing threshold comparison with an equipment performance detection correction threshold value acquired by weighting correction according to the preset performance detection threshold value and the performance detection deviation distribution data of the equipment of the same type of the single equipment, judging and evaluating the equipment operating condition according to the result, displaying, if the performance effective operation detection index meets the threshold value comparison requirement of the equipment performance detection correction threshold value, indicating that the operation performance condition of the single equipment for inspection is abnormal, otherwise, displaying the result of inspection evaluation, and realizing the inspection evaluation technology of the equipment according to the large data factory, combining the environment working condition and the system condition related detection coefficient.

Referring to fig. 2, fig. 2 is a flowchart of acquiring equipment operation performance characteristic data and equipment operation anomaly log information data according to a digital factory equipment inspection evaluation method based on big data in some embodiments of the present application. According to the embodiment of the invention, the device operation monitoring information and the device operation log information of a single device in various types of devices in a digital factory in a preset time period are obtained, and the device operation performance characteristic data and the device operation abnormal log information data are extracted, specifically:

s201, acquiring equipment operation monitoring information of a single equipment in various types of equipment in a digital factory in a preset time period, and extracting equipment operation performance characteristic data including operation efficiency data, processing failure rate data, operation power consumption data and operation abnormal index quantity data according to the equipment operation monitoring information;

s202, acquiring equipment operation log information of the single equipment in a preset time period, and extracting equipment operation abnormal log information data, wherein the equipment operation abnormal log information data comprises operation abnormal prompt frequency times and abnormal alarm progression of abnormal prompts.

It should be noted that, to obtain the performance deviation of each individual device for inspection, first, device operation monitoring information and device operation log information of each type of device in a digital factory in a certain preset time period are required to be obtained, the device operation monitoring information is information related to the monitoring and collecting of the working performance in the device operation process in the time period, then, device operation performance characteristic data is extracted through the information, the characteristic data is characteristic data reflecting the performance condition of the device, wherein the characteristic data comprises operation efficiency data of the device, residual rate data obtained by checking a processed product, operation power consumption data and related data reflecting the number of abnormal indexes appearing in the operation process, the device operation log information is an abnormal condition log reflecting the automatic record of the device in the operation process in the time period, the device operation abnormal alarm condition is reflected, the device operation abnormal log information data is extracted, and the operation abnormal alarm frequency and the abnormal alarm number of the device abnormal alarm are included, namely, the frequency of the abnormal alarm of the device abnormal alarm is extracted through the log information, and the number of the abnormal alarm is sent out at different time points, and the number of the abnormal alarm number is corresponding to the abnormal alarm number.

Referring to fig. 3, fig. 3 is a flowchart of obtaining performance detection deviation data of a digital factory equipment inspection evaluation method based on big data in some embodiments of the present application. According to the embodiment of the invention, the preset equipment standard operation index data of the equipment with the model type of the single equipment is obtained, the preset equipment standard operation index data and the equipment operation performance characteristic data are processed to obtain the operation performance deviation degree data of the single equipment, and the operation abnormal log information data of the equipment are combined to process to obtain the performance detection deviation degree data, specifically:

s301, obtaining preset equipment standard operation index data of equipment of a model class to which the single equipment belongs through a preset digital factory equipment production parameter database, wherein the preset equipment standard operation index data comprise efficiency index data, failure rate index data, power consumption index data and abnormal quantity index data;

s302, processing according to the standard operation index data of the preset equipment and the operation performance characteristic data of the equipment to obtain operation performance deviation degree data of the single equipment;

s303, processing according to the running performance deviation data and combining the running abnormality prompting frequency number and the abnormality alarming number to obtain performance detection deviation data of the single equipment;

；

After the operation performance characteristic data and the operation abnormality log information data of the single equipment are obtained, to evaluate the deviation situation of the performance of the single equipment, the operation performance deviation degree data of the single equipment is required to be obtained, the preset index data corresponding to the type class to which the single equipment belongs is required to be obtained, and then the operation performance deviation degree data reflecting the performance deviation situation of the single equipment is obtained by performing comparison processing with the index data, namely, the preset equipment standard operation index data of the type class equipment to which the single equipment belongs is obtained through a preset digital factory equipment production parameter database, the related data including the operation efficiency index, the processing product defective rate index, the operation power consumption index and the preset index of the number of abnormal indexes occurring in the operation process in a time period are obtained, the operation performance deviation degree data of the single equipment is further obtained by performing processing according to the index data and the performance characteristic data, and the accurate performance state deviation situation of the single equipment is further obtained by performing processing according to the operation performance deviation degree data in combination with the operation abnormality prompt frequency and the abnormality alarm stage number; the calculation formula of the running performance deviation degree data is as follows:

；

Wherein, the liquid crystal display device comprises a liquid crystal display device,for the running performance deviation data, +.>、/>、/>、/>Respectively, operating efficiency data, processing defective rate data, operating power consumption data, operating abnormality index quantity data, and->、/>、/>、/>Respectively, efficiency index data, defective rate index data, power consumption index data, abnormal quantity index data,/>Correction factor for the deviation of the preset performance for the type of device to which the individual device belongs>、/>、/>、/>The characteristic coefficient is preset (the performance deviation correction factor and the characteristic coefficient are obtained through a digital factory equipment production parameter database).

According to the embodiment of the invention, the performance detection deviation degree distribution data of the equipment with the same type as the single equipment in the preset time period is obtained and compared with the performance detection deviation degree data to obtain the equipment group performance difference discrete index, which is specifically as follows:

；

After the performance detection deviation data reflecting the performance deviation condition of the single device is obtained, in order to identify the deviation of the performance deviation of each single device in the same model class device from the performance deviation of other devices in the same model class, namely, the deviation between a single individual and a group, so as to identify a single device exceeding the deviation co-ordination of the device group, the performance detection deviation data of the single device is compared with the deviation distribution condition of the performance detection deviation of the device in the same model class, so as to obtain the discrete condition of the performance deviation between the single device and the group of the device in the same model class, the performance detection deviation distribution data comprises the performance detection deviation data of a plurality of devices in the same model class, namely, the performance detection deviation data of the discrete distribution of each single device in the device group, and the performance detection deviation data of the plurality of devices in the model class is collected into a performance detection deviation data set, and then the performance detection deviation data of the single device is compared with the performance detection deviation data of the single device, so as to obtain the discrete condition of the performance deviation between the single device and the group, namely, the performance detection deviation between the single device in the model class is reflected by the discrete condition of each single device, and the performance detection deviation between the single device in the model class is greatly different from the individual.

According to the embodiment of the invention, the operation environment working condition detection data of the single equipment in the preset time period and the system state monitoring data of the production operation system where the single equipment is located are obtained, and the operation environment working condition detection data and the system state monitoring data are respectively processed to obtain the ring condition stability excitation coefficient and the system stability compensation coefficient, specifically:

It should be noted that, the condition evaluation of the operation of the device is affected by the operation environment and the working condition of the device and the system condition of the system where the device is located, so, in order to evaluate the operation performance condition of the device, the detection data of the environment working condition and the system are required to be obtained and processed, the stability influence coefficient of the environment working condition on the performance of the device and the influence compensation coefficient of the system operation on the performance stability of the device are obtained, the operation environment working condition detection data of the single device in a preset time period including the temperature and humidity environment data, the monitoring data of the device when the device is in overload operation, the preset performance stability index of the device in the time period and the frequency of the predicted device failure rate are obtained, the system state monitoring data of the production operation system where the single device is located in the preset time period including the system overload operation data, the system failure rate data and the total system operation efficiency data are obtained, and the operation environment detection data and the system state monitoring data are processed respectively, and the environment stability excitation coefficient and the system stability compensation coefficient are obtained correspondingly; the calculation formula of the ring condition stability excitation coefficient is as follows:

；

Wherein, the liquid crystal display device comprises a liquid crystal display device,for the stability factor of the ring condition, < > excitation>、/>、/>、/>Respectively temperature and humidity environment data, overload operation monitoring data, time and life stability index, predicted fault rate frequency, < ->、/>、/>、/>Is a preset characteristic coefficient;

the calculation formula of the system stability compensation coefficient is as follows:

；

wherein, the liquid crystal display device comprises a liquid crystal display device,compensating coefficient for system stability>、/>、/>Respectively overload operation data, failure rate data and total efficiency number of system operationAccording to (I)>For presetting the efficiency factor of the production running system, +.>、/>、/>The characteristic coefficient is preset (the efficiency factor and the characteristic coefficient of the production running system are obtained through a production parameter database of the digital factory equipment).

According to the embodiment of the invention, the performance detection deviation data is corrected according to the device group performance difference discrete index and the ring condition stability excitation coefficient and the system stability compensation coefficient, so as to obtain the performance effective operation detection index of the single device, which is specifically as follows:

；

wherein, the liquid crystal display device comprises a liquid crystal display device,run detection index for performance effectiveness, +.>Detecting deviation data for performance, +.>Discrete index for device group performance difference, +.>For the stability factor of the ring condition, < > excitation>Compensating coefficient for system stability>、/>、/>、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained through a digital factory equipment production parameter database).

After the environmental stability excitation coefficient and the system stability compensation coefficient of the influence of the operating environment working condition of the equipment and the system condition of the system of the equipment are obtained, the operating environment of the single equipment, the system operating condition of the single equipment and the deviation condition of the performance difference degree of the single equipment and the equipment of the same type are comprehensively corrected, the performance effective operation detection index of the single equipment is obtained, the performance effective detection state index of the single equipment under the influence of the environmental condition and the system condition is reflected more accurately by the detection index, and the performance deviation degree discrete degree of the single equipment and the equipment of the same type is compensated and considered.

According to the embodiment of the invention, the preset performance detection threshold value of the type class device to which the single device belongs is obtained, and the performance detection deviation distribution data is subjected to weighted correction to obtain the device performance detection correction threshold value, specifically:

；

In order to more accurately identify the performance evaluation result of each single device, the preset performance detection threshold of the device with the same type of single device needs to be corrected to obtain a more accurate correction threshold, the average deviation of the performance detection deviation data set of the device group is subjected to weighted correction with the preset threshold to obtain a device performance detection correction threshold, and then the single device is subjected to threshold comparison judgment according to the correction threshold to evaluate the performance condition of the single device.

As shown in fig. 4, the present invention further discloses a digital plant equipment inspection and evaluation system 4 based on big data, which includes a memory 41 and a processor 42, wherein the memory includes a digital plant equipment inspection and evaluation method program based on big data, and when the digital plant equipment inspection and evaluation method program based on big data is executed by the processor, the following steps are implemented:

According to the embodiment of the invention, the device operation monitoring information and the device operation log information of a single device in various types of devices in a digital factory in a preset time period are obtained, and the device operation performance characteristic data and the device operation abnormal log information data are extracted, specifically:

According to the embodiment of the invention, the preset equipment standard operation index data of the equipment with the model type of the single equipment is obtained, the preset equipment standard operation index data and the equipment operation performance characteristic data are processed to obtain the operation performance deviation degree data of the single equipment, and the operation abnormal log information data of the equipment are combined to process to obtain the performance detection deviation degree data, specifically:

；

wherein, the liquid crystal display device comprises a liquid crystal display device,detecting deviation data for performance, +. >For the running performance deviation data, +.>For the number of abnormal operation frequency->The number of abnormal alarm stages for the ith abnormal prompt, n is the number of abnormal prompts in a preset time period,/>、/>is a preset characteristic coefficient.

；/>

；

wherein, the liquid crystal display device comprises a liquid crystal display device,compensating coefficient for system stability>、/>、/>Respectively, overload operation data of the system, failure rate data of the system and total efficiency data of the system, and +.>For presetting the efficiency factor of the production running system, +.>、/>、/>The characteristic coefficient is preset (the efficiency factor and the characteristic coefficient of the production running system are obtained through a production parameter database of the digital factory equipment).

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A third aspect of the present invention provides a readable storage medium having embodied therein a big data based digital plant inspection evaluation method program which, when executed by a processor, implements the steps of the big data based digital plant inspection evaluation method as described in any one of the above.

The invention discloses a big data-based digital factory equipment inspection evaluation method, a system and a medium, which are characterized in that equipment operation performance characteristic data and equipment operation abnormal log information data of a single equipment are obtained, performance detection deviation degree data is obtained by combining preset equipment standard operation index data processing of equipment of a type of equipment belonging to the equipment, performance detection deviation degree distribution data of equipment of the type of equipment of the same type of equipment is obtained by comparing the obtained performance detection deviation degree distribution data with the single equipment, equipment group performance difference discrete indexes are obtained by comparing the obtained performance detection deviation degree distribution data, performance effective operation detection indexes of the single equipment are obtained by combining ring condition stability excitation coefficients and system stability compensation coefficients obtained by processing operation environment working condition detection data and system state monitoring data, and then threshold value comparison is carried out with equipment performance detection correction threshold value obtained by weighting correction according to preset performance detection threshold value and performance detection deviation degree distribution data, and equipment operation conditions are judged and evaluated according to the result and displayed; the method comprises the steps of processing the factory equipment according to the deviation condition of the running performance and the related detection coefficient of the environmental working condition and the system condition based on the big data to obtain the detection index of equipment running, and carrying out inspection evaluation on the equipment, so that an intelligent technology for carrying out inspection evaluation on the equipment running condition through the big data is realized.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.

The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.

Alternatively, the above-described integrated units of the present invention may be stored in a readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.

Claims

1. The digital factory equipment inspection evaluation method based on big data is characterized by comprising the following steps of:

2. The method for inspecting and evaluating digital plant equipment based on big data according to claim 1, wherein the steps of obtaining equipment operation monitoring information and equipment operation log information of a single equipment in each type of equipment in the digital plant in a preset time period, and extracting equipment operation performance characteristic data and equipment operation abnormality log information data include:

3. The method for inspecting and evaluating digital plant equipment based on big data according to claim 2, wherein the steps of obtaining the standard operation index data of the preset equipment of the type class equipment to which the single equipment belongs, processing the standard operation index data with the operation performance characteristic data of the equipment to obtain the operation performance deviation degree data of the single equipment, and processing the operation performance deviation degree data by combining the operation abnormality log information data of the equipment to obtain the performance detection deviation degree data comprise the following steps:

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4. The method for inspecting and evaluating digital industrial equipment based on big data according to claim 3, wherein the step of obtaining the performance detection deviation distribution data of the equipment of the same type as the single equipment in the preset time period and comparing the performance detection deviation distribution data with the performance detection deviation data to obtain the equipment group performance difference dispersion index comprises the following steps:

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wherein, the liquid crystal display device comprises a liquid crystal display device,discrete index for device group performance difference, +.>Deviation data is detected for the performance of the kth individual device,for the preset characteristic coefficient corresponding to the kth individual device,/->Correction factor for the deviation of the preset performance for the type of device to which the individual device belongs>For the performance detection deviation data set, m is the number of single devices contained in the single device model class device, +.>Detecting deviation data for the performance of the j-th single device of the m single devices,/for the performance of the j-th single device>The preset characteristic coefficient is the j-th single device in m single devices.

5. The method for inspecting and evaluating digital plant equipment based on big data according to claim 4, wherein the steps of obtaining the operation environment condition detection data of the single equipment in the preset time period and the system state monitoring data of the production operation system where the single equipment is located, respectively processing the operation environment condition detection data and the system state monitoring data to obtain the ring condition stability excitation coefficient and the system stability compensation coefficient, and include:

6. The method for evaluating the patrol of the digital plant equipment based on big data according to claim 5, wherein the correcting the performance test deviation data according to the equipment group performance difference dispersion index in combination with the ring condition stability excitation coefficient and the system stability compensation coefficient to obtain the performance effective operation test index of the single equipment comprises:

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wherein, the liquid crystal display device comprises a liquid crystal display device,run detection index for performance effectiveness, +.>Detecting deviation data for performance, +.>To set upDiscrete index of group performance difference->For the stability factor of the ring condition, < > excitation>Compensating coefficient for system stability>、/>、/>、/>Is a preset characteristic coefficient.

7. The method for inspecting and evaluating digital industrial equipment based on big data according to claim 6, wherein the steps of obtaining a preset performance detection threshold of the type and class equipment to which the single equipment belongs, and performing weighted correction with the performance detection deviation distribution data to obtain an equipment performance detection correction threshold comprise:

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wherein, the liquid crystal display device comprises a liquid crystal display device,correction threshold for device performance detection,/->For performance detection bias data set, m is the number of individual devices, +. >A performance detection threshold is preset.

8. Digital factory equipment inspection evaluation system based on big data, characterized in that the system includes: the system comprises a memory and a processor, wherein the memory comprises a program of a digital factory equipment inspection evaluation method based on big data, and the program of the digital factory equipment inspection evaluation method based on the big data realizes the following steps when being executed by the processor:

9. The big data based digital plant equipment inspection and assessment system according to claim 8, wherein the obtaining equipment operation monitoring information and equipment operation log information of a single equipment in each type of equipment in the digital plant in a preset time period, and extracting equipment operation performance characteristic data and equipment operation abnormality log information data, comprises:

10. A computer-readable storage medium, wherein the computer-readable storage medium includes therein a big data based digital plant inspection evaluation method program, which when executed by a processor, implements the steps of the big data based digital plant inspection evaluation method according to any one of claims 1 to 7.