CN112115418B - Method, device and equipment for acquiring bias estimation information - Google Patents
Method, device and equipment for acquiring bias estimation information Download PDFInfo
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Abstract
The application is applicable to the technical field of industrial Internet of things, and provides an analysis method of bias estimation information, which comprises the following steps: acquiring actual operation data of industrial equipment to be analyzed in a preset analysis period; standard state operation data of industrial equipment are obtained, and deviation estimation information is calculated according to a preset deviation estimation rule, the actual operation data and the standard state operation data. According to the scheme, the deviation estimation information is obtained through calculation of the preset deviation estimation rule, and the gap between the actual running state and the ideal running state is quantized, so that a user of the industrial equipment can intuitively know the current running state of the industrial equipment.
Description
Technical Field
The application belongs to the technical field of industrial Internet of things, and particularly relates to a method, a device and equipment for acquiring bias estimation information.
Background
With the advent of the industrial internet era, industrial field industrial equipment is various and has various scenes. While the industrial equipment is in operation, a user hopes that the industrial equipment is in an ideal operation state, but when the industrial equipment is in actual operation, the actual operation state of the industrial equipment and the ideal operation state can be different due to the reasons of the industrial equipment or the external environment. In the prior art, no method is available for quantifying the gap between the actual running state and the ideal running state, so that the user of the industrial equipment cannot intuitively know the current running state of the industrial equipment.
Disclosure of Invention
The embodiment of the application provides a method, a device and equipment for acquiring bias estimation information, which can solve the problem that in the prior art, no method can quantify the gap between the actual running state and the ideal running state, so that an industrial equipment user cannot intuitively know the current running state of the industrial equipment.
In a first aspect, an embodiment of the present application provides a method for analyzing bias estimation information, including:
acquiring actual operation data of industrial equipment to be analyzed in a preset analysis period; the actual operation data comprise actual parameter data corresponding to at least one analysis parameter;
acquiring standard state operation data of the industrial equipment; the standard state operation data comprise standard state parameter data corresponding to the analysis parameters;
and calculating bias estimation information according to a preset bias estimation rule, the actual operation data and the standard state operation data.
Further, the calculating the bias estimation information according to the preset bias estimation rule, the operation data and the current standard state operation data includes:
calculating average Euclidean distance information between the operation data and the current standard state operation data according to the operation data and the current standard state operation data;
and calculating the bias estimation information according to the average Euclidean distance information and a preset bias estimation function.
Further, the calculating, according to the operation data and the current standard state operation data, average euclidean distance information between the operation data and the current standard state operation data includes:
and calculating first average Euclidean distance information between the actual running data and the standard running data and second average Euclidean distance information of the actual parameter data and the standard parameter data corresponding to each analysis parameter according to the actual running data and the standard running data.
Further, the preset bias estimation function is:
wherein Dev estimation represents the bias estimation information; a represents a coefficient; x represents the actual operation data; z represents the standard state operation data; x is X i Representing actual operation data corresponding to the ith analysis parameter; z is Z i Representing standard state operation data corresponding to the ith analysis parameter;representing the first average euclidean distance value; />Representing the second average euclidean distance value; i=1, 2,3.
Further, before the obtaining the standard state operation data of the industrial equipment, the method further comprises:
and acquiring a standard state selection instruction, and searching standard state operation data corresponding to the standard state selection instruction from a preset storage space.
Further, before the obtaining the standard state operation data of the industrial equipment, the method further comprises:
obtaining a standard state setting instruction, wherein the standard state setting instruction comprises one or more standard state operation data;
and storing the one or more standard state operation data into a preset storage space.
In a second aspect, an embodiment of the present application provides an analysis apparatus for bias estimation information, including:
the first acquisition unit is used for acquiring actual operation data of the industrial equipment to be analyzed in a preset analysis period; the actual operation data comprise actual parameter data corresponding to at least one analysis parameter;
the second acquisition unit is used for acquiring standard state operation data of the industrial equipment; the standard state operation data comprise standard state parameter data corresponding to the analysis parameters;
the first calculation unit is used for calculating the bias estimation information according to a preset bias estimation rule, the actual operation data and the standard state operation data.
Further, the first computing unit includes:
the second calculation unit is used for calculating average Euclidean distance information between the operation data and the current standard state operation data according to the operation data and the current standard state operation data;
and the third calculation unit is used for calculating the bias estimation information according to the average Euclidean distance information and a preset bias estimation function.
Further, the second computing unit is specifically configured to:
and calculating first average Euclidean distance information between the actual running data and the standard running data and second average Euclidean distance information of the actual parameter data and the standard parameter data corresponding to each analysis parameter according to the actual running data and the standard running data.
Further, the preset bias estimation function is:
wherein Dev estimation represents the bias estimation information; a represents a coefficient; x represents the actual operation data; z represents the standard state operation data; x is X i Representing the actual shipment corresponding to the ith analysis parameterLine data; z is Z i Representing standard state operation data corresponding to the ith analysis parameter;representing the first average euclidean distance value; />Representing the second average euclidean distance value; i=1, 2,3.
Further, the analysis device of the bias estimation information further includes:
the first processing unit is used for acquiring the standard state selection instruction and searching standard state operation data corresponding to the standard state selection instruction from a preset storage space.
Further, the analysis device of the bias estimation information further includes:
the third acquisition unit is used for acquiring a standard state setting instruction, wherein the standard state setting instruction comprises one or more standard state operation data;
and the second processing unit is used for storing the one or more standard state operation data into a preset storage space.
In a third aspect, an embodiment of the present application provides an apparatus for acquiring bias estimation information, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the method for acquiring bias estimation information according to the first aspect when executing the computer program.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program, where the computer program is executed by a processor to implement the method for acquiring the bias estimation information according to the first aspect.
Compared with the prior art, the embodiment of the application has the beneficial effects that: acquiring actual operation data of industrial equipment to be analyzed in a preset analysis period; standard state operation data of industrial equipment are obtained, and deviation estimation information is calculated according to a preset deviation estimation rule, the actual operation data and the standard state operation data. According to the scheme, the deviation estimation information is obtained through calculation of the preset deviation estimation rule, and the gap between the actual running state and the ideal running state is quantized, so that a user of the industrial equipment can intuitively know the current running state of the industrial equipment.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required for the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for analyzing bias estimation information according to a first embodiment of the present application;
fig. 2 is a schematic flowchart of S103 refinement in a method for analyzing bias estimation information according to the first embodiment of the present application;
FIG. 3 is a schematic diagram of an analysis device for bias estimation information according to a second embodiment of the present application;
fig. 4 is a schematic diagram of an analysis apparatus for bias estimation information according to a third embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
Referring to fig. 1, fig. 1 is a schematic flowchart of an analysis method of bias estimation information according to a first embodiment of the present application. An execution subject of an analysis method of bias estimation information in this embodiment is a device having an analysis function of bias estimation information, for example, a server or the like. The analysis method of the bias estimation information shown in fig. 1 may include:
s101: acquiring actual operation data of industrial equipment to be analyzed in a preset analysis period; the actual operation data comprise actual parameter data corresponding to at least one analysis parameter.
In an actual industrial setting, there may be a variety of industrial equipment and a variety of industrial scenarios. Ideally, we want the industrial equipment to always operate in a relatively perfect "ideal state," named "standard state" in this embodiment, which may be defined as the standard operating range of other relevant parameters that the customer considers to be in a certain set point. When the industrial equipment is operated, a user hopes that the industrial equipment is in an ideal operation state, and taking a 32KW variable-frequency compressor as an example, the ideal operation state of full-load (100% load) operation is as follows: the output gas pressure should be Z 1 =8kg/cm 2, gas temperature is Z 2 =46 ℃, flow is Z 3 =39nm3/Hr, energy consumption is Z per hour 4 =36 KWHr. However, when the industrial equipment is actually operated, there may be a deviation between the actual operating state and the ideal operating state of the industrial equipment due to the industrial equipment itself or due to external environment. Possibly because the industrial equipment itself needs to be overhauled, for example, carbon deposition in a boiler is increased; or subject to some external fluctuations or problems during use, such as the fuel burned by the natural gas boiler, the non-industrial equipment itself problems such as the natural gas concentration not reaching the standard, etc., result in equipment that is not operating in a standard state. This biased state is called a "biased state," or "biased state. In fact, "bias" is a normal state.
In this embodiment, the deviation between the actual running state and the ideal running state of the industrial equipment is quantified, and the deviation between the actual running state and the ideal running state of the industrial equipment is intuitively reflected by calculating the quantified result, so that a user of the industrial equipment knows whether the current running state is the ideal state or not, and how much the current running state deviates from the ideal state.
The equipment acquires actual operation data of the industrial equipment to be analyzed in a preset analysis period, wherein the operation data comprises actual parameter data corresponding to at least one analysis parameter.
The device may determine the industrial device to be analyzed according to the user's selection instructions, for example, a biogas power station, the main device having: a marsh gas pressurizing and purifying device and three marsh gas generators. The marsh gas purifying and pressurizing equipment purifies, filters and pressurizes marsh gas pumped out from the marsh gas tank, and then conveys the marsh gas to a marsh gas generator to generate electricity. Besides the four devices, instruments are also used for detecting the methane flow, pressure and temperature before and after purifying and pressurizing, and methane concentration, methane flow transmitted to each generator, electric quantity generated by each generator and the electric quantity generated by the whole power station. In this industrial scenario, the user may select a biogas pressurized purification device as the device to be analyzed, and the user may also select a biogas generator as the device to be analyzed.
The device may store a preset analysis period in advance, or the device may determine the preset analysis period according to an analysis period setting instruction, which is not limited herein. The device is to acquire actual operating data of the industrial device within a preset analysis period. The preset analysis period may be set according to a period of time in which the industrial equipment is actually operated, for example, the preset analysis period may be set to one hour, and then the equipment is required to acquire actual operation data of the industrial equipment within one hour.
The actual operation data is real-time data of the industrial equipment during operation, and the actual operation data comprises actual parameter data corresponding to at least one analysis parameter. The analysis parameters are the type of parameters of the industrial plant when operating, such as temperature, pressure, flow, energy consumption, etc. The actual parameter data corresponding to each analysis parameter is the data corresponding to the analysis parameter in the preset analysis period. For example, when the analysis parameter is temperature, the actual parameter data corresponding to the analysis parameter is the value of the temperature in the preset analysis period. Real worldThe actual operation data may include actual parameter data corresponding to a plurality of analysis parameters, for example, the actual operation data may include an actual output gas pressure of the compressor in a preset analysis period, a gas temperature in the preset analysis period, a flow rate in the preset analysis period, and an energy consumption in the preset analysis period. Specifically, the actual output gas pressure (X 1 ) Temperature of gas (X) 2 ) Flow rate (X) 3 ) Energy consumption (X) 4 ) The actual operating data may be expressed as X (t) = { X 1 (t),X 2 (t),X 3 (t),X 4 (t)|t＝t 0 ,t 1 ,t 2 ,…,t n }. Wherein t is a preset analysis period.
According to the representation method in the above example, it can be obtained that the general representation of the actual operation data can be X (t) = { X 1 (t),X 2 (t),X 3 (t),…,X n (t)|t＝t 0 ,t 1 ,t 2 ,…,t n }，X n And (t) is actual parameter data corresponding to the nth analysis parameter, and X (t) is represented as actual operation data.
S102: acquiring standard state operation data of the industrial equipment; the standard state operation data comprise standard state parameter data corresponding to the analysis parameters.
The equipment acquires standard state operation data of the industrial equipment, wherein the standard state operation data comprises standard state parameter data corresponding to analysis parameters. Since the definition of the standard state mentioned above may be a standard operating range for other relevant parameters that the customer considers to be in a certain set point. There may be a plurality of settings for an industrial facility, so that one industrial facility may have a plurality of standard states. For example, standard conditions for several cases of a 32KW inverter compressor:
standard state a: the standard conditions at full load (100% load) operation are: the output gas pressure should be Z 1 =8kg/cm 2, gas temperature is Z 2 =46 ℃, flow is Z 3 =39nm3/Hr, energy consumption is Z per hour 4 ＝36KWHr；
Standard state B: the standard conditions at full load (80% load) operation are: output gas pressureThe force should be Z 1 =7kg/cm 2, gas temperature is Z 2 =43 ℃, flow is Z 3 =30Nm3/Hr, energy consumption is Z per hour 4 ＝31KWHr；
Standard state C: the standard conditions at full load (60% load) operation are: the output gas pressure should be Z 1 =6.5 Kg/cm2, gas temperature is Z 2 =43 ℃, flow is Z 3 =24Nm3/Hr, energy consumption is Z per hour 4 ＝24KWHr。
Therefore, the standard state operation data of the industrial equipment acquired by the equipment is the standard state operation data of the currently set industrial equipment. The standard state of the industrial equipment may be set before the standard state operation data of the industrial equipment is acquired, so as to determine the standard state operation data of the industrial equipment.
In one embodiment, prior to obtaining the standard state operation data of the industrial device, the method may include: and acquiring a standard state selection instruction, and searching standard state operation data corresponding to the standard state selection instruction from a preset storage space. In this embodiment, the user may not specifically set standard state operation data, but only need to select a standard state, and different standard state selection instructions are associated in the device to store the corresponding standard state operation data. The user can select the standard state on the device so as to generate a standard state selection instruction, the device acquires the standard state selection instruction, and standard state operation data corresponding to the standard state selection instruction is searched from a preset storage space.
In another embodiment, before obtaining the standard state operation data of the industrial equipment, the method may include: obtaining a standard state setting instruction, wherein the standard state setting instruction comprises one or more standard state operation data; and storing the one or more standard state operation data into a preset storage space. In this embodiment, the user may specifically set standard state operation data, the user inputs the standard state operation data into the device, and generates a standard state setting instruction according to the standard state operation data input by the user, where the standard state setting instruction includes one or more standard state operation data. When the device acquires the standard state setting instruction, one or more standard state operation data are stored in a preset storage space.
S103: and calculating bias estimation information according to a preset bias estimation rule, the actual operation data and the standard state operation data.
The device is pre-stored with a preset bias state estimation rule, wherein the preset bias state estimation rule is used for calculating bias state estimation information, and the bias state estimation information is the difference between actual operation data and standard state operation data. The preset bias estimation rule only needs to calculate the difference value between the actual running data and the standard state running data, various similarity algorithms can be adopted to calculate bias estimation information, the actual running data and the standard state running data can be respectively mapped into graphs, the area difference between the two images is calculated, and the like, and the preset bias estimation rule is not limited.
The following describes how to calculate the bias estimation information, taking the average euclidean distance as an example. S103 may include S1031 to S1032, and as shown in fig. 2, S1031 to S1032 are specifically as follows:
s1031: and calculating average Euclidean distance information between the operation data and the current standard state operation data according to the operation data and the current standard state operation data.
In the present embodiment, euclidean distance calculation bias estimation information is employed. Euclidean metric, also known as euclidean distance, is a commonly used distance definition, referring to the true distance between two points in an m-dimensional space, or the natural length of a vector (i.e., the distance from the point to the origin). The euclidean distance in two and three dimensions is the actual distance between two points. Euclidean distance is a common measure of differential distance. And the device calculates average Euclidean distance information between the operation data and the current standard state operation data according to the operation data and the current standard state operation data. The average euclidean distance information is calculated as an average value for the euclidean distance information.
Further, S1031 may include: calculating the actual operation data according to the actual operation data and the standard state operation dataAnd first average Euclidean distance information between the operation data and the standard state operation data, and second average Euclidean distance information between the actual parameter data and the standard state parameter data corresponding to each analysis parameter. In this embodiment, the average euclidean distance information between the operation data and the current standard state operation data includes first average euclidean distance information between the actual operation data and the standard state operation data, and second average euclidean distance information of the actual parameter data and the standard state parameter data corresponding to the respective analysis parameters. For example, X represents actual operational data, Z represents standard state operational data,representing the first average euclidean distance value; x is X i Representing actual operation data corresponding to the ith analysis parameter, Z i Representing standard state operation data corresponding to the ith analysis parameter,/->Representing the second average euclidean distance value; i=1, 2,3.
S1032: and calculating the bias estimation information according to the average Euclidean distance information and a preset bias estimation function.
The device is pre-stored with a preset bias estimation function, and the preset bias estimation function is used for calculating bias estimation information. The dependent variable of the preset bias estimation function is average Euclidean distance information, and the independent variable of the preset bias estimation function is bias estimation information. The device calculates the bias estimation information according to the average Euclidean distance information and a preset bias estimation function.
Further, the preset bias estimation function is:
wherein Dev estimation represents the bias estimation information; a represents a coefficient; a represents a value that can be set in advance,the ratio between the number of the actual parameter data corresponding to the analysis parameters and the number of the analysis parameters can also be used; x represents the actual operation data; z represents the standard state operation data; x is X i Representing actual operation data corresponding to the ith analysis parameter; z is Z i Representing standard state operation data corresponding to the ith analysis parameter;representing the first average euclidean distance value;representing the second average euclidean distance value; i=1, 2,3.
It can be understood that the above formula is only an example of the preset bias estimation function, and the deformation of the function and the addition of some coefficients or constants are all included in the formula, which falls within the protection scope of the present embodiment.
It will be appreciated that the smaller the bias estimation information, the closer the actual operating state of the current industrial device to the ideal state, and if the actual operating state of the current industrial device is very close to the ideal state, the bias estimation information should be close to 0.
In the embodiment of the application, the actual operation data of the industrial equipment to be analyzed in a preset analysis period are obtained; standard state operation data of industrial equipment are obtained, and deviation estimation information is calculated according to a preset deviation estimation rule, the actual operation data and the standard state operation data. According to the scheme, the deviation estimation information is obtained through calculation of the preset deviation estimation rule, and the gap between the actual running state and the ideal running state is quantized, so that a user of the industrial equipment can intuitively know the current running state of the industrial equipment.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.
Referring to fig. 3, fig. 3 is a schematic diagram of an analysis apparatus for bias estimation information according to a second embodiment of the present application. The units included are used to perform the steps in the corresponding embodiments of fig. 1-2. Refer specifically to the related descriptions in the respective embodiments of fig. 1-2. For convenience of explanation, only the portions related to the present embodiment are shown. Referring to fig. 3, the analysis device 3 of the bias estimation information includes:
a first obtaining unit 310, configured to obtain actual operation data of the industrial device to be analyzed in a preset analysis period; the actual operation data comprise actual parameter data corresponding to at least one analysis parameter;
a second obtaining unit 320, configured to obtain standard state operation data of the industrial device; the standard state operation data comprise standard state parameter data corresponding to the analysis parameters;
the first calculating unit 330 is configured to calculate bias estimation information according to a preset bias estimation rule, the actual operation data, and the standard state operation data.
Further, the first computing unit 330 includes:
the second calculation unit is used for calculating average Euclidean distance information between the operation data and the current standard state operation data according to the operation data and the current standard state operation data;
and the third calculation unit is used for calculating the bias estimation information according to the average Euclidean distance information and a preset bias estimation function.
Further, the second computing unit is specifically configured to:
and calculating first average Euclidean distance information between the actual running data and the standard running data and second average Euclidean distance information of the actual parameter data and the standard parameter data corresponding to each analysis parameter according to the actual running data and the standard running data.
Further, the preset bias estimation function is:
wherein Dev estimation represents the bias estimation information; a represents a coefficient; x represents the actual operation data; z represents the standard state operation data; x is X i Representing actual operation data corresponding to the ith analysis parameter; z is Z i Representing standard state operation data corresponding to the ith analysis parameter;representing the first average euclidean distance value; />Representing the second average euclidean distance value; i=1, 2,3.
Further, the analysis device 3 for the bias estimation information further includes:
the first processing unit is used for acquiring the standard state selection instruction and searching standard state operation data corresponding to the standard state selection instruction from a preset storage space.
Further, the analysis device 3 for the bias estimation information further includes:
the third acquisition unit is used for acquiring a standard state setting instruction, wherein the standard state setting instruction comprises one or more standard state operation data;
and the second processing unit is used for storing the one or more standard state operation data into a preset storage space.
Fig. 4 is a schematic diagram of an analysis apparatus for bias estimation information according to a third embodiment of the present application. As shown in fig. 4, the analysis device 4 of the bias estimation information of this embodiment includes: a processor 40, a memory 41 and a computer program 42 stored in said memory 41 and executable on said processor 40, for example an analysis program of the bias estimation information. The processor 40, when executing the computer program 42, implements the steps of the above described embodiments of the method for analyzing the respective bias estimation information, such as steps 101 to 103 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, performs the functions of the modules/units of the apparatus embodiments described above, such as the functions of the modules 310-330 shown in fig. 3.
Illustratively, the computer program 42 may be partitioned into one or more modules/units that are stored in the memory 41 and executed by the processor 40 to complete the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions for describing the execution of the computer program 42 in the analysis device 4 of the bias estimation information. For example, the computer program 42 may be divided into a first acquisition unit, a second acquisition unit, and a first calculation unit, each unit specifically functioning as follows:
the first acquisition unit is used for acquiring actual operation data of the industrial equipment to be analyzed in a preset analysis period; the actual operation data comprise actual parameter data corresponding to at least one analysis parameter;
the second acquisition unit is used for acquiring standard state operation data of the industrial equipment; the standard state operation data comprise standard state parameter data corresponding to the analysis parameters;
the first calculation unit is used for calculating the bias estimation information according to a preset bias estimation rule, the actual operation data and the standard state operation data.
The analysis device of the bias estimation information may include, but is not limited to, a processor 40, a memory 41. It will be appreciated by those skilled in the art that fig. 4 is merely an example of the analysis device 4 for the bias estimation information, and does not constitute a limitation of the analysis device 4 for the bias estimation information, and may include more or less components than illustrated, or may combine some components, or different components, e.g., the analysis device for the bias estimation information may further include an input/output device, a network access device, a bus, etc.
The processor 40 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the analysis device 4 for the deviation estimation information, for example a hard disk or a memory of the analysis device 4 for the deviation estimation information. The memory 41 may be an external storage device of the analysis device 4 for the deviation estimation information, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like provided on the analysis device 4 for the deviation estimation information. Further, the analysis device 4 for the bias estimation information may further include both an internal storage unit and an external storage device of the analysis device 4 for the bias estimation information. The memory 41 is used for storing the computer program and other programs and data required for the analysis device of the bias estimation information. The memory 41 may also be used for temporarily storing data that has been output or is to be output.
It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
The embodiment of the application also provides a network device, which comprises: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, which when executed by the processor performs the steps of any of the various method embodiments described above.
Embodiments of the present application also provide a computer readable storage medium storing a computer program which, when executed by a processor, implements steps that may implement the various method embodiments described above.
Embodiments of the present application provide a computer program product which, when run on a mobile terminal, causes the mobile terminal to perform steps that may be performed in the various method embodiments described above.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing device/terminal apparatus, recording medium, computer Memory, read-Only Memory (ROM), random access Memory (RAM, random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other manners. For example, the apparatus/network device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on 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.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (6)
1. A method for analyzing bias estimation information, comprising:
acquiring actual operation data of industrial equipment to be analyzed in a preset analysis period; the actual operation data comprise actual parameter data corresponding to at least one analysis parameter;
acquiring standard state operation data of the industrial equipment; the standard state operation data comprise standard state parameter data corresponding to the analysis parameters;
calculating bias estimation information according to a preset bias estimation rule, the actual operation data and the standard state operation data, wherein the bias estimation information comprises the following steps:
according to the actual operation data and the standard state operation data, calculating average Euclidean distance information between the actual operation data and the standard state operation data, including:
according to the actual operation data and the standard state operation data, calculating first average Euclidean distance information between the actual operation data and the standard state operation data and second average Euclidean distance information of the actual parameter data and the standard state parameter data corresponding to each analysis parameter;
calculating bias estimation information according to the average Euclidean distance information and a preset bias estimation function; wherein, the preset bias estimation function is:
wherein Dev estimation represents the bias estimation information; a represents a coefficient; x represents the actual operation data; z represents the standard state operation data; x is X i Representing actual operation data corresponding to the ith analysis parameter; z is Z i Representing standard state operation data corresponding to the ith analysis parameter;representing a first average euclidean distance value; />Representing a second average euclidean distance value; i=1, 2,3.
2. The method of claim 1, further comprising, prior to said obtaining standard state operational data of said industrial equipment:
and acquiring a standard state selection instruction, and searching standard state operation data corresponding to the standard state selection instruction from a preset storage space.
3. The method of claim 1, further comprising, prior to said obtaining standard state operational data of said industrial equipment:
obtaining a standard state setting instruction, wherein the standard state setting instruction comprises one or more standard state operation data;
and storing the one or more standard state operation data into a preset storage space.
4. An apparatus for analyzing bias estimation information, comprising:
the first acquisition unit is used for acquiring actual operation data of the industrial equipment to be analyzed in a preset analysis period; the operation data comprises actual parameter data corresponding to at least one analysis parameter;
the second acquisition unit is used for acquiring standard state operation data of the industrial equipment; the standard state operation data comprise standard state parameter data corresponding to the analysis parameters;
the first calculating unit is configured to calculate bias estimation information according to a preset bias estimation rule, the actual operation data, and the standard state operation data, and includes:
according to the actual operation data and the standard state operation data, calculating average Euclidean distance information between the actual operation data and the standard state operation data, including:
according to the actual operation data and the standard state operation data, calculating first average Euclidean distance information between the actual operation data and the standard state operation data and second average Euclidean distance information of the actual parameter data and the standard state parameter data corresponding to each analysis parameter;
calculating bias estimation information according to the average Euclidean distance information and a preset bias estimation function; wherein, the preset bias estimation function is:
wherein Dev estimation represents the bias estimation information; a represents a coefficient; x represents the actual operation data; z represents the standard state operation data; x is X i Representing actual operation data corresponding to the ith analysis parameter; z is Z i Representing standard state operation data corresponding to the ith analysis parameter;representing a first average euclidean distance value; />Representing a second average euclidean distance value; i=1, 2,3.
5. An apparatus for obtaining bias estimation information, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 3 when executing the computer program.
6. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method according to any one of claims 1 to 3.
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