CN115454014A - Controller performance testing method and system based on adaptive neural fuzzy - Google Patents
Controller performance testing method and system based on adaptive neural fuzzy Download PDFInfo
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- CN115454014A CN115454014A CN202210894695.7A CN202210894695A CN115454014A CN 115454014 A CN115454014 A CN 115454014A CN 202210894695 A CN202210894695 A CN 202210894695A CN 115454014 A CN115454014 A CN 115454014A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
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Abstract
According to the controller performance testing method and system based on the adaptive neural fuzzy, the overall equipment performance detection data are obtained, different operation effects are achieved under different scenes based on the overall equipment operation conditions, the equipment operation results are determined by combining operation performance changes of the equipment in the overall equipment performance detection data, and then accurate real-time equipment operation results are obtained. The working condition of the equipment can be determined according to the performance quantification indication, so that the working performance of the equipment can be effectively guaranteed, and the cost is effectively reduced.
Description
Technical Field
The application relates to the technical field of data testing, in particular to a controller performance testing method and system based on adaptive neural fuzzy.
Background
With continuous progress and optimization of artificial intelligence, the technical field related to the artificial intelligence is wider. In the process of artificial intelligence and machine performance testing, the inventor finds that the performance testing may be inaccurate in the process of performance testing. Therefore, a need exists for a solution to the above-mentioned problems.
Disclosure of Invention
In order to solve the technical problems in the related art, the application provides a method and a system for testing the performance of a controller based on adaptive neural fuzzy.
In a first aspect, a method for testing the performance of a controller based on adaptive neural fuzzy is provided, the method at least comprising: determining an equipment operation description record, wherein the equipment operation description record comprises a plurality of pieces of equipment performance detection data which are acquired one by one in a preset segment description, and the equipment performance detection data are used for expressing equipment performance detection expression of an equipment monitoring space; determining a target value corresponding to each piece of equipment performance detection data, wherein the target value is used for representing the equipment operation condition in the equipment monitoring space; positioning certainty and energy quantification indication of the device performance detection data corresponding to each target value in the node is combined, and the performance quantification indication is used for representing the performance test result of the device in the device monitoring space in a preset segment description; and determining a target equipment operation result by combining the performance quantification indication, wherein the target equipment operation result represents an overall equipment operation result corresponding to the equipment operation description record.
In an independently implemented embodiment, the determining the target value corresponding to each of the device performance detection data includes: determining a heat analysis map corresponding to each piece of equipment performance detection data, wherein the heat analysis map is used for representing equipment performance detection expression of equipment in the equipment monitoring space; and determining a target value of the corresponding equipment performance detection data by combining the preference variable of each heat analysis map.
In an independently implemented embodiment, the determining the heat analysis map corresponding to each of the device performance test data includes: determining an equipment operation description list in each piece of equipment performance detection data, wherein the equipment operation description list is the content of performance description of equipment in the equipment performance detection data; and performing clustering processing on the equipment operation description list by combining with the preset performance optimization description to obtain a heat analysis map.
In an independently implemented embodiment, the clustering the device operation description list in combination with a preset performance optimization description to obtain a heat analysis map includes: and clustering the equipment operation description list at least comprising two operations of a feature extraction thread and a data updating thread to obtain a heat analysis map.
In an independently implemented embodiment, the clustering the device operation description list in combination with a preset performance optimization description to obtain a heat analysis map includes: and performing diversified cleaning, a feature extraction thread, a data updating thread and a data correction thread on the equipment operation description list one by one to obtain a heat analysis map.
In an independently implemented embodiment, said location-deterministic energy-enabling indication in the node of the corresponding device performance detection data in combination with each of said target values comprises; analyzing a pending performance description distribution diagram in combination with the positioning of the equipment performance detection data corresponding to each target value in the node; and cleaning the undetermined performance description distribution graph by combining with an equipment operation result node to obtain a performance quantification indication, wherein the equipment operation result node is determined by combining with global equipment operation result constraint condition prediction.
In a separately implemented embodiment, the determining the target device operation result in combination with the performance quantification indication comprises: and optimizing the performance quantification indication, and determining the corresponding optimal optimization result in the optimization results as the operation result of the target equipment.
In a second aspect, an adaptive neuro-fuzzy-based controller performance testing system is provided, which comprises a processor and a memory, which are communicated with each other, wherein the processor is used for reading a computer program from the memory and executing the computer program to realize the method.
The method and the system for testing the performance of the controller based on the adaptive neural fuzzy control system, provided by the embodiment of the application, are used for acquiring the performance detection data of the global equipment, have different operation effects in different scenes based on the operation condition of the global equipment, determining the operation result of the equipment by combining the operation performance change of the equipment in the performance detection data of the global equipment, and further acquiring the accurate real-time operation result of the equipment. The working condition of the equipment can be determined according to the performance quantification indication, so that the working performance of the equipment can be effectively guaranteed, and the cost is effectively reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a flowchart of a method for testing performance of a controller based on adaptive neural fuzzy provided in an embodiment of the present application.
Fig. 2 is a block diagram of an apparatus for testing performance of a controller based on adaptive neural fuzzy provided in an embodiment of the present application.
Fig. 3 is an architecture diagram of a controller performance testing system based on adaptive neural fuzzy provided by an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions, the technical solutions of the present application are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.
Referring to fig. 1, a method for testing performance of a controller based on adaptive neural fuzzy is shown, which may include the following steps 100-300.
Step 100, determining an equipment operation description record, wherein the equipment operation description record comprises a plurality of pieces of equipment performance detection data which are acquired one by one in a preset segment description, and the equipment performance detection data are used for representing equipment performance detection expression of an equipment monitoring space.
It can be understood that, when the contents described in steps 100 to 300 are executed, the global device performance detection data is obtained, different operation effects are obtained in different scenes based on the operation conditions of the global device, and the device operation result is determined by combining the operation performance changes of the devices in the plurality of global device performance detection data, so as to obtain an accurate real-time device operation result. The working condition of the equipment can be determined according to the performance quantification indication, so that the working performance of the equipment can be effectively guaranteed, and the cost is effectively reduced.
In an alternative embodiment, the determining the target value corresponding to each of the device performance detection data includes: determining a heat analysis map corresponding to each piece of equipment performance detection data, wherein the heat analysis map is used for representing equipment performance detection expression of equipment in the equipment monitoring space; and determining a target value of the corresponding equipment performance detection data by combining the preference variable of each heat analysis map.
In an alternative embodiment, the determining the heat analysis map corresponding to each of the device performance test data includes: determining an equipment operation description list in each piece of equipment performance detection data, wherein the equipment operation description list is the performance description content of the equipment in the equipment performance detection data; and carrying out clustering processing on the equipment operation description list by combining with the preset performance optimization description to obtain a heat analysis map.
In an alternative embodiment, the clustering the device operation description list in combination with the preset performance optimization description to obtain the heat analysis map includes: and clustering the equipment operation description list at least comprising two operations of a feature extraction thread and a data updating thread to obtain a heat analysis map.
In an alternative embodiment, the clustering the device operation description list in combination with the preset performance optimization description to obtain the heat analysis map includes: and performing diversified cleaning, a feature extraction thread, a data updating thread and a data correction thread on the equipment operation description list one by one to obtain a heat analysis map.
In an alternative embodiment, said location-determinative energetic indication in the node in conjunction with each said target value corresponding device performance detection data comprises; analyzing a pending performance description distribution diagram in combination with the positioning of the device performance detection data corresponding to each target value in the node; and cleaning the undetermined performance description distribution graph by combining an equipment operation result node to obtain a performance quantification indication, wherein the equipment operation result node is determined by combining global equipment operation result constraint condition prediction.
In an alternative embodiment, said determining a target device operational result in combination with said quantitative indication of performance comprises: and optimizing the performance quantification indication, and determining the corresponding optimal optimization result in the optimization results as the operation result of the target equipment.
On the basis of the above, please refer to fig. 2 in combination, which provides an adaptive neural-fuzzy-based controller performance testing apparatus 200, applied to an adaptive neural-fuzzy-based controller performance testing system, the apparatus includes:
a record determining module 210, configured to determine an apparatus operation description record, where the apparatus operation description record includes a plurality of apparatus performance detection data that are obtained one by one in a preset segment description, and the apparatus performance detection data are used to represent an apparatus performance detection expression of an apparatus monitoring space;
a condition determining module 220, configured to determine a target value corresponding to each piece of equipment performance detection data, where the target value is used to represent an equipment operating condition in the equipment monitoring space;
a result determining module 230, configured to enable a quantified indication of the positioning certainty of the device performance detection data in the node in combination with each target value, where the quantified indication of the performance is used to represent a performance test result of the device in the device monitoring space in a preset segment description; and determining a target equipment operation result by combining the performance quantification indication, wherein the target equipment operation result represents an overall equipment operation result corresponding to the equipment operation description record.
On the basis of the above, please refer to fig. 3, which shows an adaptive neuro-fuzzy-based controller performance testing system 300, which includes a processor 310 and a memory 320, which are communicated with each other, wherein the processor 310 is configured to read a computer program from the memory 320 and execute the computer program to implement the above method.
On the basis of the above, there is also provided a computer-readable storage medium on which a computer program is stored, which when executed implements the above-described method.
In summary, based on the above scheme, global device performance detection data is obtained, different operation effects are achieved in different scenes based on the operation conditions of the global devices, and the operation results of the devices in the plurality of global device performance detection data are combined to determine the operation results of the devices, so that accurate real-time device operation results are obtained. According to the method and the device, the working condition of the equipment can be determined according to the performance quantification indication, so that the working performance of the equipment can be effectively guaranteed, and the cost can be effectively reduced.
It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, for example such code provided on a carrier medium such as a diskette, CD-or DVD-ROM, programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).
It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered as illustrative only and not limiting of the application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such alterations, modifications, and improvements are intended to be suggested herein and are intended to be within the spirit and scope of the exemplary embodiments of this application.
Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C + +, C #, VB.NET, python, and the like, a conventional programming language such as C, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit-preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is to be understood that the descriptions, definitions and/or uses of terms in the attached materials of this application shall control if they are inconsistent or inconsistent with the statements and/or uses of this application.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application may be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (8)
1. A method for testing the performance of a controller based on adaptive neural fuzzy, the method at least comprising:
determining an equipment operation description record, wherein the equipment operation description record comprises a plurality of pieces of equipment performance detection data which are acquired one by one in a preset segment description, and the equipment performance detection data are used for expressing equipment performance detection expression of an equipment monitoring space;
determining a target value corresponding to each piece of equipment performance detection data, wherein the target value is used for representing the equipment operation condition in the equipment monitoring space;
positioning certainty and energy quantification indication in the node combined with the device performance detection data corresponding to each target value, wherein the performance quantification indication is used for representing the performance test result of the device in the device monitoring space in the preset segment description; and determining a target equipment operation result by combining the performance quantification indication, wherein the target equipment operation result represents an overall equipment operation result corresponding to the equipment operation description record.
2. The method of claim 1, wherein the determining a target value for each of the device performance measurement data comprises: determining a heat analysis map corresponding to each piece of equipment performance detection data, wherein the heat analysis map is used for representing equipment performance detection expression of equipment in the equipment monitoring space; and determining a target value of corresponding equipment performance detection data by combining the preference variable of each heat analysis map.
3. The method of claim 2, wherein determining the thermal analysis map corresponding to each of the device performance measurement data comprises: determining an equipment operation description list in each piece of equipment performance detection data, wherein the equipment operation description list is the performance description content of the equipment in the equipment performance detection data; and carrying out clustering processing on the equipment operation description list by combining with the preset performance optimization description to obtain a heat analysis map.
4. The method according to claim 3, wherein the clustering the device operation description list in combination with the preset performance optimization description to obtain the heat analysis map comprises: and clustering the equipment operation description list at least comprising two operations of a feature extraction thread and a data updating thread to obtain a heat analysis map.
5. The method according to claim 3 or 4, wherein the clustering the device operation description list in combination with the preset performance optimization description to obtain the heat analysis map comprises: and performing diversified cleaning, a feature extraction thread, a data updating thread and a data correction thread on the equipment operation description list one by one to obtain a heat analysis map.
6. The method of claim 5, wherein said integrating each said target value corresponds to a location-deterministic energy-efficient indication of device performance measurement data in the node comprises; analyzing a pending performance description distribution diagram in combination with the positioning of the device performance detection data corresponding to each target value in the node; and cleaning the undetermined performance description distribution graph by combining with an equipment operation result node to obtain a performance quantification indication, wherein the equipment operation result node is determined by combining with global equipment operation result constraint condition prediction.
7. The method of claim 6, wherein determining a target device operational result in conjunction with the quantitative indication of performance comprises: and optimizing the performance quantification indication, and determining the corresponding optimal optimization result in the optimization results as the operation result of the target equipment.
8. An adaptive neuro-fuzzy-based controller performance testing system comprising a processor and a memory in communication with each other, the processor configured to read a computer program from the memory and execute the computer program to implement the method of any of claims 1-7.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202210894695.7A CN115454014A (en) | 2022-07-28 | 2022-07-28 | Controller performance testing method and system based on adaptive neural fuzzy |
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| CN202210894695.7A CN115454014A (en) | 2022-07-28 | 2022-07-28 | Controller performance testing method and system based on adaptive neural fuzzy |
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| CN115454014A true CN115454014A (en) | 2022-12-09 |
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| CN202210894695.7A Pending CN115454014A (en) | 2022-07-28 | 2022-07-28 | Controller performance testing method and system based on adaptive neural fuzzy |
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