EP4666142A1

EP4666142A1 - Method for configuring performance index computing model, computing method, apparatus, and computing device

Info

Publication number: EP4666142A1
Application number: EP23929451.5A
Authority: EP
Inventors: Liang Zhang; Yang Wang; Li Hong HU; Wei Sun; Yan Lin; Yin Shen; Xuan YANG
Original assignee: Siemens AG; Siemens Corp
Current assignee: Siemens AG; Siemens Corp
Priority date: 2023-03-31
Filing date: 2023-03-31
Publication date: 2025-12-24
Also published as: WO2024197868A1; CN120770011A

Abstract

The present disclosure relates to a method for configuring a performance index computing model, a computing method, an apparatus and a computing device. The method includes: an arranging labels step of arranging labels for data from different data sources respectively, wherein the different data sources monitor different states of each of a plurality of objects in a production system respectively, the data comprise a duration that the object remains in a certain state, and each label marks a state of an object; an associating labels step of associating different categories of events with corresponding labels respectively according to different states of the object comprised in each category of event in the production system; a determining duration step of performing, specific to each category of event, summation on durations of all data with the label associated with the event respectively to serve as a total time of each category of event; and a determining performance index computing mode step of determining a performance index computing mode of the object based on the total time of each category of event, a preset constant and a pre-determined operational formula.

Description

METHOD FOR CONFIGURING PERFORMANCE INDEX COMPUTING MODEL, COMPUTING METHOD, APPARATUS, AND COMPUTING DEVICE

TECHNICAL FIELD

The present disclosure usually relates to the technical field of automated systems, in particular to a method for configuring a performance index computing model, a computing method, an apparatus, and a computing device.

BACKGROUND

With improvement of an automation degree, more and more automated production devices are applied to a production process. Therefore, performance of the devices is of great importance to the whole production efficiency. For example, overall equipment effectiveness (OEE) is a common key performance index for assessing the devices.
A general definition of OEE is as follows.
OEE = availability × performance × quality
Availability=run time/planned production time
Performance= (ideal cycle time × total count) /run time
Quality=good count/total count
A concept of OEE is quite simple, but different factories may have different understandings and different computing modes. One factory may even have different computing methods for different types of devices. For example, how to define “run time” .
- Taking a punching machine as an example, a working process of the punching machine roughly includes the following several operations: selecting and mounting a proper cutting tool, calibrating a position, loading a material (such as a steel plate) , punching (or continuing to complete punching of a whole plate) , unloading a material, checking quality of a punched product, and then performing work in a next round. Some factories may only use the “punching” operation as “run time” , but other factories may also use “loading/unloading a material” as “run time” , especially, when the “loading/unloading” operation is automated.
- Taking a bending machine as another example, a working process of the bending machine roughly includes the following several operations: taking a steel plate, placing the steel plate in a correct position of the bending machine, bending the steel plate (a tool travels up and down at a time) , taking down the steel plate, and performing a next round. In general, the "bending" operation may need only a few seconds, and most of time is spent on waiting for placing a material onto a correct position. If only the “bending” operation is regarded as “run time” , as the other operations may be completed by people or other robots, OEE of the bending machine will be quite low. Thus, some factories may also use the other auxiliary operations (for example, loading/unloading a plate) as “run time” .
- Some special machines may have two or more working modes, for example, one advanced punching machine may also perform laser cutting, and as for different modes of one machine, computing of OEE may also be different.
- More importantly, some factories may expect to define and compute various types of OEE for one device, and in this way, they can assess performance of one device in different modes and compare these types of OEE to obtain more inspirations. For example, OEE_1: using “loading/unloading a material” as “run time” ; OEE_2: not using “loading/unloading a material” as “run time” ; OEE_3: using “lunchtime” as “planned production time” ; and the like.
In a word, OEE or other performance indexs may have different computing methods for different factories, different devices, different working modes of one device or even the same working mode of one device. Therefore, OEE computing of most of factories is implemented in manufacturing software/system in a customized mode. It is quite difficult to provide all factories with unified software for computing the OEE.
Customized implementation is not a big issue in the past but only needs great effort and takes much time, for each factory may have its own manufacturing system. However, as software as a service (SaaS) becomes a trend of a manufacturing industry, customized implementation turns out to be a big issue for an SaaS provider. The SaaS provider is almost impossible to perform customized implementation for each factory, because customization cost of each customer is too high, which is unacceptable in an SaaS mode.
The SaaS provider will provide unified OEE computing specific to a specified input. A customer may upload data related to the specified input. However, it is highly difficult to meet various demands of each customer for OEE computing.
The customer needs customized implementation and to self-define computing of OEE or KPI. It is a customized solution and needs great effort and takes much time.
Some simple programming tools, such as Node-red, may simplify programming and provide a flexible customization mode. But these tools still seem too complicated for a customer lacking programming experience, resulting in failure in self-defining KPI.
SUMMARY
A brief description on the present disclosure is given below to provide basic understanding in some aspects of the present disclosure. It is to be understood that the brief description is not exhaustive for the present disclosure. It is neither intended to determine a key or important part of the present disclosure, nor intended to limit the scope of the present disclosure. It only aims to give some concepts in a simplified mode to serve as a preamble of the following more detailed description.
In view of this, the present disclosure provides a method for dynamically configuring a computing model of a performance index in a production system and a method for computing the performance index of the production system by using the computing model so as to solve the above problems.
According to an aspect of the present disclosure, a method for configuring a performance index computing model is provided and includes:
an arranging labels step of arranging labels for data from different data sources respectively, where the different data sources monitor different states of each of a plurality of objects in a production system respectively, the data include a duration that the object remains in a certain state, and each label marks a state of the object;
an associating labels step of associating different categories of events with the corresponding labels respectively according to the different states of the object included in each category of event in the production system;
a determining duration step of performing, specific to each category of event, summation on durations of all data with the label associated with the event respectively to serve as a total time of each category of event; and
a determining performance index computing mode step of determining a performance index computing mode of the object based on the total time of each category of event, a preset constant and a pre-determined operational formula.
In this way, a user may self-define a performance index computing mode of the object according to demands without performing a large amount of programming.
Optionally, in an example of the above aspect, the method further includes: a step of determining a performance index model:
changing at least one of the different states of the object included in each category of event, a type of the preset constant, a value of the preset constant and the pre-determined operational formula, repeatedly executing the arranging labels step, the determining duration step and the determining performance index computing mode step to obtain a performance index computing mode type, and then determining, specific to the different objects in the production system, a set of corresponding candidate performance index computing mode types as a performance index computing model respectively.
In this way, the user may be allowed to define, specific to one machine, a performance index computing mode under different conditions, for example, a performance index is computed in different working modes of one machine according to different modes; and the user is allowed to define and store, specific to one machine, various performance index computing modes, so that influence factors on machine effectiveness are analyzed by comparing different types of performance indexs.
According to another aspect of the present disclosure, a method for computing a performance index is provided and includes:
receiving data from different data sources, where the data include labels, durations of the data and objects corresponding to the data;
receiving a specified time period in which a performance index is to be computed; and
determining, based on data in the specified time period, a performance index of each object in the specified time period by using the pre-configured performance index computing model.
Optionally, in an example of the above aspect, after receiving the specified time period in which the performance index is to be computed, the method further includes: receiving a value of a specified preset parameter, and using the received value of the preset parameter as a value of a preset constant in the performance index computing model.
According to another aspect of the present disclosure, an apparatus for configuring a performance index computing model is provided and includes:
a label arranging unit, configured to arrange labels for data from different data sources respectively, where the different data sources monitor different states of each of a plurality of objects in a production system respectively, the data include a duration that the object remains in a certain state, and each label marks a state of the object;
a label associating unit, configured to associate different categories of events with the corresponding labels respectively according to the different states of the object included in each category of event in the production system;
a duration determining unit, configured to perform, specific to each category of event, summation on durations of all data with the label associated with the event respectively to serve as a total time of each category of event; and
a performance index computing mode determining unit, configured to determine a performance index computing mode of the object based on the total time of each category of event, a preset constant and a pre-determined operational formula.
According to another aspect of the present disclosure, an apparatus for computing a performance index is provided and includes:
a data receiving unit, configured to receive data from different data sources, where the data include labels, durations of the data and objects corresponding to the data;
a time period receiving unit, configured to receive a specified time period in which a performance index is to be computed; and
a performance index computing unit, configured to determine, based on data in the specified time period, a performance index of each object in the specified time period by using a pre-configured performance index computing model.
According to another aspect of the present disclosure, a computing device is provided and includes: at least one processor; and a memory coupled with the at least one processor, where the memory is configured to store an instruction, and the instruction, when executed by the at least one processor, causes the processor to execute the above methods.
According to another aspect of the present disclosure, a non-transitory machine-readable storage medium, storing an executable instruction, where the instruction, when executed, causes a machine to execute the above methods.
According to another aspect of the present disclosure, a computer program product is provided, tangibly stored on a computer-readable medium and including a computer-executable instruction, where the computer-executable instruction, when executed, causes at least one processor to execute the above methods.
According to the methods and apparatuses of embodiments of the present disclosure, a flexible method for configuring a performance index computing mode is provided, so that demands of different users for customized performance index computing modes may be met, and customized programming is replaced with simple configuration to greatly reduce cost of customizing the performance index computing mode.

BRIEF DESCRIPTION OF THE DRAWINGS

The above or other objectives, characteristics and advantages of the present disclosure will be easier to understand with reference to the following description of embodiments of the present disclosure in combination with the accompanying drawings. Components in the accompanying drawings are only intended to illustrate a principle of the present disclosure. In the accompanying drawings, the same or similar technical features or components will be represented by using the same or similar reference numerals. In the accompanying drawings:
FIG. 1 is a flowchart of an exemplary process of a method for configuring a performance index computing model in a production system according to an embodiment of the present disclosure.
FIG. 2 is a flowchart of an exemplary process of a method for computing a performance index according to an embodiment of the present disclosure.
FIG. 3 is a schematic diagram of received data from different data sources.
FIG. 4 is a block diagram of an exemplary configuration of an apparatus for configuring a performance index computing model according to an embodiment of the present disclosure.
FIG. 5 is a block diagram of an exemplary configuration of an apparatus for computing a performance index according to an embodiment of the present disclosure.
FIG. 6 illustrates a block diagram of a computing device according to an embodiment of the present disclosure.
The reference numerals are as follows:

DETAILED DESCRIPTION

The topic described herein will be discussed now with reference to exemplary implementations. It is to be understood that these implementations are discussed only for making those skilled in the art better understand and thus implement the topic described herein, but not for limiting the protection scope, applicability or examples described in claims. Functions and arrangement of discussed elements may be changed without departing from the protection scope of the contents of the present disclosure. Various processes or components in each example may be omitted, replaced or added according to demands. For example, the described method may be executed in a sequence different from the described one, and the various steps may be added with another one, omitted or combined. Besides, features described in some examples may also be combined in other examples.
The term “include” and variants thereof used herein indicate an open-ended term, which means “including but not limited to” . The term “based on” indicates “based at least in part on”. The terms “an embodiment” and “one embodiment"indicate “at least one embodiment. ” The term “another embodiment” indicates “at least one additional embodiment” . The terms “first” , “second” and the like may refer to different or identical objects. Other explicit and implicit definitions may also be included below. Unless otherwise indicated clearly in the context, a definition of a term is consistent in the whole specification.
In view of this, the present disclosure provides a method for dynamically configuring a performance index computing model in a production system and a method for computing a performance index of the production system by using the computing model.
A method for configuring a computing model, a method for computing a performance index and an apparatus according to embodiments of the present disclosure will be described below with reference to the accompanying drawings.
FIG. 1 is a flowchart of an exemplary process of a method 100 for configuring a performance index computing model in a production system according to an embodiment of the present disclosure.
Firstly, in a step S102 of arranging labels, labels are arranged for data from different data sources respectively, where the different data sources monitor different states of each of a plurality of objects in the production system respectively, the data include a duration that the object remains in a certain state, and each label marks a state of the object.
Here, the data sources are, for example, different states of the plurality of different objects collected by different sensors respectively, or may be directly read from the system. The production system includes the plurality of different objects, each object may have a plurality of states, and each data source is configured to monitor a certain state of one object. The data from the data sources may include a start time point and an end time point that the one object remains in the state, the duration that the object remains in the state may be obtained by computing based on the start time point and the end time point, or the duration that the object remains in the state may be obtained directly from the data sources, or the data may include the obtained start time point, end time point and duration at the same time. In order to determine the duration that the object remains in each state, one label is arranged for data from the different data sources respectively, in which state the object is may be conveniently identified based on the labels, and the duration of remaining in the state is obtained. In the following step, a performance index of the object may be computed based on a sum of durations that the object is in the different states.
In the production system, one object may be, for example, a device or a worker. A description is made below by taking the object being a punching machine as a specific example.
In a production process, the punching machine may be in different states. Hypothetically, the punching machine has two working modes of punching and laser cutting, and the punching machine may be in a punching state, a laser cutting state, a material loading state, a material unloading state, a wait state, a maintenance state, a failure state, a repair state and other states. Here, the different states of the punching machine are collected by using different sensors respectively, and a duration that the punching machine remains in a certain state may be obtained according to a label of data uploaded by a sensor.
For example, the punching machine remaining in the punching state is marked as L1 and remaining in the laser cutting state is marked as L3, and there may be other working states in actual application, which may be respectively marked as L5…Ln. The punching machine remaining in the material loading state is marked as L2, remaining in the material unloading state is marked as L4, remaining in the wait state is marked as L6, remaining in the maintenance state is marked as L8, and remaining in the failure state is marked as L10 till Lm. That is to say, if the data collected by the sensor carry a label L1, the data indicate the duration that the punching machine remains in the punching state.
Afterwards, in a step S104 of associating with labels, different categories of events may be associated with the corresponding labels respectively according to the different states of the object included in each category of event in the production system as required;
As for the production system, the different categories of events may be included, and each category of event includes the different states of the object. For example, in the above example, one punching machine may include two categories of events, one category of event is “work” , which may be represented by A, and the other category of event is “idle” , which may be represented by B.
States included in one category of event may have different arrangement modes. For example, in a first case, it is considered that the punching machine remaining in the punching state L1 and the laser cutting state L3 belongs to the event A of a work category, and the material loading state L2, the material unloading state L4, the wait state L6, the maintenance state L8, the failure state L10 and the like belong to the event B of an idle category. In a second case, it may be considered that the punching machine remaining in the punching state L1 and the laser cutting state L3 belongs to the event A of the work category, the wait state L6, the maintenance state L8, the failure state L10 and the like belong to the event B of the idle category, and the material loading state L2 and the material unloading state L4 belong to an event C of a wait-for-work category. That is to say, the states included in each category of event may have different classification modes according to demands.
Afterwards, in a step S106 of determining a duration, specific to each category of event, summation is performed on durations of all data with the label associated with the event respectively to serve as a total time of each category of event.
Within a specified time length, the total time of one category of event is a sum of the durations of all the data of the label associated with the event.
Within a specified time length, for example, within one day, the punching machine may remain in the punching state many times, so a plurality of data with the label L1 may be collected, on this day, a total duration that the punching machine remains in the punching state is a sum of durations indicated by all data of the label L1; a plurality of data with the label L3 that the punching machine remains in the laser cutting state may be collected, on this day, a total duration that the punching machine remains in the laser cutting state is a sum of durations indicated by all data of the label L3; and thus, in the above first case, the total time of the event A of the work category of the punching machine is a sum of the sum of the durations of all the data with the label L1 and the sum of the durations of the data with the label L3 of the machine, which may be represented by TA, that is, a sum of all work time periods.
Idle time of the punching machine is a sum of durations of all data with the labels L2, L4, L6, L8 and L10 respectively.
Finally, in a step S108 of determining a performance index computing mode, a performance index computing mode of the object is determined based on the total time of each category of event, a preset constant and a pre-determined operational formula.
A performance index may be, for example, an operation time proportion (also called a production efficiency or OEE below) , an idle time proportion, a failure time proportion, a maintenance and repair time proportion and the like.
In order to compute the performance index, besides considering a time length that the object remains in the different states, some constants may also be preset.
In the above example, for example, planned work time a on one day may be preset to be 8 hours; within one day, the total number of good products is b, and this parameter may be obtained from, for example, an external quality system; a total yield within one day is c, which may also be obtained from the external quality system; a preset ideal product cycle is d; and an actual cycle may be equal to a total work time/the total yield c.
It may be understood that the method according to this embodiment of the present disclosure may be used for computing any performance index of one object (including a device, a worker and the like) , for example, computing the idle time proportion of one device, a total time that the device remains in the wait state, the maintenance state and the failure state may be considered, a proper preset constant and an operational formula are adopted for computing, and in the specification, a description is made by taking overall equipment effectiveness (OEE) of a computing device as a specific example, computing modes of other performance indexs are not described in detail.
Hypothetically, OEE of the punching machine may be computed by using the following formula:
OEE=availability*performance index*quality index,
where, the availability=operation time/planned work time=TA/a, the quality index=good products/the total yield=b/c, and the performance index=the ideal cycle time/actual cycle time=d/ (TA/c) .
Then, OEE_11=TA/a*b/c *d/ (TA/c) (1)
In an equation (1) , TA is the total time of the event A of the work category of the punching machine, a, b, c and d are preset constants respectively, TA, a, b, c and d are computed according to the pre-determined operational formula, namely, a production efficiency computing mode of the object.
It may be understood that those skilled in the art may preset a proper constant type according to demands, a value of a constant may be pre-determined, or may be set by a user, an operational formula may also be preset by the user, proper computing is performed by using the total time of each category of event, the preset constant and the operational formula so as to compute the performance index of the object. In the method according to this embodiment of the present disclosure, different performance indexs of the object may be computed according to demands, and a constant type, a value of the constant and a specific operational formula are not limited.
Through the above equation (1) , a production efficiency computing mode of the punching machine in the above first case may be determined. In the above second case, namely, in a case that the material loading state L2 and the material unloading state L4 belong to the event C of the wait-for-work category, if it is considered that the event C of the wait-for-work category also belongs to operation time, the operation time is TA+TC, so OEE_12= (TA+TC) /a*b/c *d/ (TA/c) may be obtained.
That is to say, the category of the event in the production system may have different definitions, in a case of different definitions, each category of event may include different states, so that different performance index computing modes may also be obtained. Besides, for example, the device may have a plurality of working modes, and in the different working modes, the performance index computing modes are also different.
Therefore, the configuring method 100 according to this embodiment of the present disclosure further includes a step S110 of determining a performance index model.
Specifically, the step S110 of determining the performance index model includes: at least one of the different states of the object included in each category of event, a type of the preset constant, a value of the preset constant or the pre-determined operational formula is changed, and the step of associating with the labels, the step of determining the duration and the step of determining a performance index computing mode are repeatedly executed to obtain different performance index computing modes;
structured combination is performed on the different performance index computing modes to obtain a performance index computing mode type; and
then specific to the different objects in the production system, a set of corresponding candidate performance index computing mode types is determined as a performance index computing model respectively.
Through the method according to this embodiment of the present disclosure, the performance index computing modes for the different objects in different conditions may be obtained respectively, and the different conditions are, for example, the different states of the object included in the same event category, different constant types, different constant values, different pre-determined operational formulas and the like. Different conditions may be traversed once for the number of times of repeatedly executing the step of associating with the labels, the step of determining the duration and the step of determining a performance index computing mode according to demands, which is not described in detail here.
The structured combination refers to combining the performance index computing modes with an association relationship to obtain a performance index computing mode type.
For example, a plurality of performance index computing mode types may be obtained through the structured combination:
OEE_1 = { (condition 1, OEE_11) ; (condition 2, OEE_12) ; (condition 3, OEE_13) } (2)
OEE_2 = { (condition 1, OEE_21) ; (condition 2, OEE_22) (3)
OEE_3
…
where, the equation (2) represents OEE_1 =OEE_11 in a condition 1, OEE_1= OEE_12 in a condition 2, and OEE_1= OEE_13 in a condition 3.
The equation (3) represents OEE_2 =OEE_21 in the condition 1, and OEE_2= OEE_22 in the condition 2.
The condition 1 and the condition 2 are, for example, different working modes in which the device works; OEE_1, OEE_2 and OEE_3 are, for example, computed by using different constants and operational formulas. For example, OEE_1 uses “loading/unloading a material” as “run time" ; OEE_2 does not use “loading/unloading a material” as “run time” ; and OEE_3 uses “lunchtime” as “planned production time” and the like.
There may be various different performance index computing mode types specific to each object, and a set of proper candidate performance index computing mode types may be determined according to demands.
Through the above method, a performance index computing model may be obtained and includes the set of the candidate performance index computing mode types corresponding to the different objects.
For example: M1: {OEE_1, OEE_3…}
M2: {OEE_1, OEE_2…}
M3: {OEE_2}
…
In the model, a first object M1 includes the performance index computing mode types OEE_1, OEE_3 and the like, a second object M2 includes the performance index computing mode types OEE_1, OEE_2 and the like, and a third object M3 includes the performance index computing mode types OEE_2, and performance index computing mode types of all the other objects.
Afterwards, a method for computing a performance index of an object by using the performance index computing model obtained by the configuring method according to an embodiment of the present disclosure will be described with reference to FIG. 2.
FIG. 2 is a flowchart of an exemplary process of a method 200 for computing a performance index according to an embodiment of the present disclosure.
Firstly, in a step S202, data from different data sources are received, the data include at least labels, durations of the data, and objects corresponding to the data.
The labels may indicate in which state the object is.
The duration of the data indicates how long the object remains in the state, and the data may include a start time point and an end time point to compute the duration, or may directly include the duration.
As for the objects corresponding to the data, that is, of which object monitored by a data source the data are.
FIG. 3 is a schematic diagram of a specific example of received data from different data sources. For example, in a first piece of data in FIG. 3, t1-t2 represents the start time and the end time of the state, L1 is a label of data, and in the above example, it indicates that the data are data that a punching machine is in a punching state, and M1 represents that the data are data of an object M1.
Afterwards, in a step S204, a specified time period in which a performance index is to be computed is received.
For example, it may be one workday, one month, or a time period from first time t1 to second time t2.
Optionally, after receiving the specified time period in which the performance index is to be computed, the method for computing the performance index may further include a step S205: a value of a specified preset parameter is received, and the received value of the preset parameter is used as a value of a preset constant in the performance index computing model.
In a step S206, based on the data within the specified time period, a performance index of each object within the specified time period is determined by using the performance index computing model pre-configured according to the above method.
Those skilled in the present disclosure may understand a specific process of computing the performance index of each object within the time period by using the pre-configured performance index computing model based on all data received within the specified time period, which is not described in detail here.
The performance index of one object here includes a set of values computed according to a performance index computing mode type corresponding to the object. For example, it may be obtained:
object M1: values of {OEE_1, OEE_3…} respectively
object M2: values of {OEE_1, OEE_2…} respectively
object M3: a value of {OEE_2}
…
By executing the method for computing the performance index according to this embodiment of the present disclosure, a value of a performance index of each device or worker in different cases may be obtained, so that values of different types of performance indexs of, for example, one device may be compared, or respective performance indexs of different types of devices may be compared. Besides, a historical value of a performance index may also be displayed specific to one or more devices to analyze a tendency of the performance index.
FIG. 4 is a block diagram of an exemplary configuration of an apparatus 400 for configuring a performance index computing model according to an embodiment of the present disclosure.
As shown in FIG. 4, the apparatus 400 for configuring the performance index computing model includes: a label arranging unit 402, a label associating unit 404, a duration determining unit 406 and a performance index computing mode determining unit 408.
The label arranging unit 402 is configured to arrange labels for data from different data sources respectively, where the different data sources monitor different states of each of a plurality of objects in a production system respectively, the data include a duration that the object remains in a certain state, and each label marks a state of the object.
The label associating unit 404 is configured to associate different categories of events with the corresponding labels respectively according to the different states of the object included in each category of event in the production system.
The duration determining unit 406 is configured to perform, specific to each category of event, summation on durations of all data with the label associated with the event respectively to serve as a total time of each category of event.
The performance index computing mode determining unit 408 is configured to determine a performance index computing mode of the object based on the total time of each category of event, a preset constant and a pre-determined operational formula.
Preferably, the apparatus 400 for configuring the performance index computing model may further include a performance index model determining unit 410, configured to change at least one of the different states of the object included in each category of event, a type of the preset constant, a value of the preset constant or the pre-determined operational formula, control the label associating unit 404, the duration determining unit 406 and the performance index computing mode determining unit 408 to execute operations repeatedly to obtain different performance index computing modes, perform structured combination on the different performance index computing modes to obtain a performance index computing mode type, and then determine, specific to the different objects in the production system, a set of corresponding candidate performance index computing mode types as a performance index computing model respectively.
FIG. 5 is a block diagram of an exemplary configuration of an apparatus 500 for computing a performance index according to an embodiment of the present disclosure.
As shown in FIG. 5, the apparatus 500 for computing a performance index includes a data receiving unit 502, a time period receiving unit 504 and a performance index computing unit 506.
The data receiving unit 502 is configured to receive data from different data sources, where the data include labels, durations of the data, and objects corresponding to the data.
The time period receiving unit 504 is configured to receive a specified time period in which a performance index is to be computed.
The performance index computing unit 506 is configured to determine, based on data in the specified time period, a performance index of each object in the specified time period by using a pre-configured performance index computing model.
Preferably, the apparatus 500 for computing the performance index may further include: a preset parameter receiving unit 505, configured to receive a value of a specified preset parameter and use the received value of the preset parameter as a value of a preset constant in the performance index computing model.
Details of operations and functions of various parts of the apparatus 400 for configuring the performance index computing model and the apparatus 500 for computing a performance index may be, for example, the same as or similar to related parts of embodiments of the method 100 for configuring the performance index computing model and the method 200 for computing the performance index of the present disclosure described with reference to FIG. 1 to FIG. 3, which is not described in detail here.
It needs to be noted that structures of the apparatus 400 for configuring the performance index computing model, the apparatus 500 for computing the performance index and their constitutional units shown in FIG. 4 and FIG. 5 are merely exemplary, and those skilled in the art may modify structural block diagrams shown in FIG. 4 and FIG. 5 according to demands.
The method and the apparatus according to the present disclosure at least have the following advantages:
the present disclosure provides a flexible method for configuring the performance index computing model, so that a user may simply customize a demanded performance index computing model without performing a large amount of programming.
Thus, a user having little programming experience or even having no programming experience may also define the customized performance index computing model by simple configuration.
The user is allowed to define performance index computing modes in different conditions for one machine, for example, the performance index is computed in different working modes of one machine according to different modes.
The user is allowed to define and store various performance index computing modes for one machine, so that influence factors on machine effectiveness may be analyzed by comparing different types of performance indexs.
Through the flexible method for configuring the performance index computing mode, the demands of the different users for the customized performance index computing modes may be met, and customized programming is replaced with simple configuration to greatly reduce cost of customizing the performance index computing mode.
As described above, referring to FIG. 1 to FIG. 5, the method and the apparatus according to the embodiments of the present disclosure are described. All the units of the apparatus for configuring the performance index computing model and the apparatus for computing the performance index as described above may be implemented by using hardware, or by using software or by combining hardware and software.
FIG. 6 shows a block diagram of a computing device 600 according to an embodiment of the present disclosure. According to one embodiment, the computing device 600 may include at least one processor 602, the processor 602 executes at least one computer-readable instruction stored or coded in a computer-readable storage medium (namely, a memory 604) .
It is to be understood that the computer-executable instruction stored in the memory 604, when executed, causes the at least one processor 602 to perform various operations and functions described above in the various embodiments of the present disclosure with reference to FIG. 1 to FIG. 5.
A non-transitory machine-readable medium is provided according to an embodiment. The non-transitory machine-readable medium may have a machine-executable instruction, and the instruction, when executed by a machine, causes the machine to execute various operations and functions described above in the various embodiments of the present disclosure with reference to FIG. 1 to FIG. 5.
A computer program is provided according to an embodiment and includes a computer-executable instruction, and the computer-executable instruction, when executed, causes at least one processor to execute various operations and functions described above in the various embodiments of the present disclosure with reference to FIG. 1 to FIG. 5.
A computer program product is provided according to an embodiment and includes a computer-executable instruction, and the computer-executable instruction, when executed, caused at least one processor to execute various operations and functions described above in the various embodiments of the present disclosure with reference to FIG. 1 to FIG. 5.
It is to be understood that all the embodiments in the specification are described in a progressive mode, the same or similar parts among all the embodiments may refer to one another, and what is mainly described in each embodiment is different from other embodiments. For example, as for the above embodiment related to the apparatuses, the above embodiment related to the computing device and the above embodiment related to the machine-readable storage medium, they are basically similar to the method embodiments and thus described simply, and related parts refer to the partial description of the method embodiments.
Specific embodiments of the specification are described above. The other embodiments fall within the scope of the appended claims. In some cases, actions or steps recorded in the claims may be executed in a sequence different from that in the embodiments and may still achieve expected results. Besides, processes illustrated in the accompanying drawings do not necessarily need the illustrated specific sequences or consecutive orders to achieve expected results. In some implementations, multi-task processing and parallel processing are also suitable or possibly beneficial.
The steps and units in the various above flows and the various system structural diagrams are not all necessary, and some steps or units may be omitted according to actual demands. Apparatus structures described in the various above embodiments may be physical structures or logical structures, namely, some units may be implemented by the same physical entity, or some units may be implemented by a plurality of physical entities respectively, or implemented jointly by some components in a plurality of independent devices.
Specific implementations described above with reference to the accompanying drawings describe exemplary embodiments, but do not represent all embodiments that can be implemented or fall within the protection scope of the claims. The term “exemplary” used in the whole specification means “used as examples, instances or for example” , and does not mean “preferred” or “superior” compared with other embodiments. For providing understanding of the described technologies, the specific implementations include specific details. However, these technologies may be implemented without these specific details. In some instances, in order to prevent concepts of the described embodiments from being difficult to understand, known structures and apparatuses are shown in a form of block diagrams.
The above description of the contents of the present disclosure are provided for making any person ordinarily skilled in the art capable of implementing or using the contents of the present disclosure. Various modifications to the contents of the present disclosure are apparent to those ordinarily skilled in the art, besides, general principles defined here may be applied to other transformations without departing from the protection scope of the contents of the present disclosure. Thus, the contents of the present disclosure are not limited to the examples and designs described herein, but are consistent with the widest scope suitable for the principles and novel features disclosed herein.
The foregoing descriptions are merely preferred embodiments of the present disclosure, but are not intended to limit the present disclosure. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure fall within the protection scope of the present disclosure.

Claims

A method (100) for configuring a performance index computing model, comprising:

an arranging labels step (S102) of arranging labels for data from different data sources respectively, wherein the different data sources monitor different states of each of a plurality of objects in a production system respectively, the data comprise a duration that the object remains in a certain state, and each label marks a state of an object;

an associating labels step (S104) of associating different categories of events with corresponding labels respectively according to different states of the object comprised in each category of event in the production system;

a determining duration step (S106) of performing, specific to each category of event, summation on durations of all data with the label associated with the event respectively to serve as a total time of each category of event; and

a determining performance index computing mode step (S108) of determining a performance index computing mode of the object based on the total time of each category of event, a preset constant and a pre-determined operational formula.
The method (100) according to claim 1, further comprising: a step (S110) of determining a performance index model:

changing at least one of the different states of the object comprised in each category of event, a type of the preset constant, a value of the preset constant and the pre-determined operational formula, repeatedly executing the arranging labels step, the determining duration step and the determining performance index computing mode step to obtain different performance index computing modes, performing structured combination on the different performance index computing modes to obtain a performance index computing mode type, and then determining, specific to the different objects in the production system, a set of corresponding candidate performance index computing mode types as a performance index computing model respectively.
A method (200) for computing a performance index, comprising:

receiving data from different data sources, wherein the data comprise at least labels, durations of the data and objects corresponding to the data (S202) ;

receiving a specified time period in which a performance index is to be computed (S204) ; and

determining, based on data in the specified time period, a performance index of each object in the specified time period by using the performance index computing model pre-configured by the method according to claim 1 or 2 (S206) .
The method (200) according to claim 3, wherein after receiving the specified time period in which the performance index is to be computed, the method further comprises: receiving a value of a specified preset parameter, and using the received value of the preset parameter as a value of a preset constant in the performance index computing model (S205) .
An apparatus (400) for configuring a performance index computing model, comprising:

a label arranging unit (402) , configured to arrange labels for data from different data sources respectively, wherein the different data sources monitor different states of each of a plurality of objects in a production system respectively, the data comprise a duration that the object remains in a certain state, and each label marks a state of the object;

a label associating unit (404) , configured to associate different categories of events with the corresponding labels respectively according to different states of the object comprised in each category of event in the production system;

a duration determining unit (406) , configured to perform, specific to each category of event, summation on durations of all data with the label associated with the event respectively to serve as a total time of each category of event; and

a performance index computing mode determining unit (408) , configured to determine a performance index computing mode of the object based on the total time of each category of event, a preset constant and a pre-determined operational formula.
The apparatus (400) according to claim 5, further comprising: a performance index model determining unit (410) , configured to change at least one of the different states of the object comprised in each category of event, a type of the preset constant, a value of the preset constant or the pre-determined operational formula, control the label associating unit, the duration determining unit and the performance index computing mode determining unit to execute operations repeatedly to obtain different performance index computing modes, perform structured combination on the different performance index computing modes to obtain a performance index computing mode type, and then determine, specific to the different objects in the production system, a set of corresponding candidate performance index computing mode types as a performance index computing model respectively.
An apparatus (500) for computing a performance index, comprising:

a data receiving unit (502) , configured to receive data from different data sources, wherein the data comprise labels, durations of the data and objects corresponding to the data;

a time period receiving unit (504) , configured to receive a specified time period in which a performance index is to be computed; and

a performance index computing unit (506) , configured to determine, based on data in the specified time period, a performance index of each object in the specified time period by using a pre-configured performance index computing model.
The apparatus (500) according to claim 7, further comprising: a preset parameter receiving unit (505) , configured to receive a value of a specified preset parameter and use the received value of the preset parameter as a value of a preset constant in the performance index computing model.
A computing device (600) , comprising:

at least one processor (602) ; and

a memory (604) coupled with the at least one processor (602) , wherein the memory is configured to store an instruction, and the instruction, when executed by the at least one processor (602) , causes the processor (602) to execute the method according to claim 1 or 2, 3 or 4.
A non-transitory machine-readable storage medium, storing an executable instruction, wherein the instruction, when executed, causes a machine to execute the method according to claim 1 or 2, 3 or 4.
A computer program product, tangibly stored on a computer-readable medium and comprising a computer-executable instruction, wherein the computer-executable instruction, when executed, causes at least one processor to execute the method according to claim 1 or 2, 3 or 4.