CN116068396A - Method and related device for testing motor performance based on artificial intelligence - Google Patents
Method and related device for testing motor performance based on artificial intelligence Download PDFInfo
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- CN116068396A CN116068396A CN202310320280.3A CN202310320280A CN116068396A CN 116068396 A CN116068396 A CN 116068396A CN 202310320280 A CN202310320280 A CN 202310320280A CN 116068396 A CN116068396 A CN 116068396A
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Abstract
The invention relates to the field of artificial intelligence, and discloses a motor performance testing method and a motor performance testing device based on artificial intelligence, which are used for improving the performance testing accuracy of a direct current motor. The method comprises the following steps: load power data and operation resistance data in the standard operation data are respectively extracted, a load power distribution curve is constructed according to the load power data, and an operation resistance change curve is generated according to the operation resistance data; extracting curve characteristics of a load power distribution curve and an operating resistance change curve to obtain load power characteristic points and operating resistance characteristic points, and generating a target characteristic point set according to the load power characteristic points and the operating resistance characteristic points; vector mapping is carried out on the target feature point set to obtain a target feature vector, and the target feature vector is input into a preset motor energy efficiency analysis model to carry out energy efficiency analysis, so that a motor energy efficiency analysis result is obtained; and matching a motor management scheme corresponding to the target direct current motor according to the motor energy analysis result.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a motor performance testing method based on artificial intelligence and a related device.
Background
The DC motor is an electromagnetic device for converting or transmitting electric energy according to the law of electromagnetic induction, and has the main functions of generating driving torque as a power source of an electric appliance or various machines.
The existing scheme is usually used for detecting the direct current motors one by manual experience, and the performance detection mode is low in efficiency and is easily influenced by testers, so that the accuracy of the existing scheme is low.
Disclosure of Invention
The invention provides a motor performance testing method based on artificial intelligence and a related device, which are used for improving the performance testing accuracy of a direct current motor.
The first aspect of the invention provides a motor performance testing method based on artificial intelligence, which comprises the following steps:
receiving a direct current motor test request, acquiring test operation data of a target direct current motor from a preset test database according to the direct current motor test request, and performing data preprocessing on the test operation data to obtain standard operation data;
Load power data and operation resistance data in the standard operation data are respectively extracted, a load power distribution curve is constructed according to the load power data, and an operation resistance change curve is generated according to the operation resistance data;
extracting curve characteristics of the load power distribution curve and the running resistance change curve to obtain load power characteristic points and running resistance characteristic points, and generating a target characteristic point set according to the load power characteristic points and the running resistance characteristic points;
vector mapping is carried out on the target feature point set to obtain a target feature vector, the target feature vector is input into a preset motor energy efficiency analysis model to carry out motor energy efficiency analysis, and a motor energy efficiency analysis result is obtained, wherein the motor energy efficiency analysis result comprises: energy efficiency class and maximum load power;
and matching a motor management scheme corresponding to the target direct current motor according to the motor energy analysis result.
With reference to the first aspect, in a first implementation manner of the first aspect of the present invention, the receiving a dc motor test request, obtaining test operation data of a target dc motor from a preset test database according to the dc motor test request, and performing data preprocessing on the test operation data to obtain standard operation data, where the obtaining includes:
Receiving a direct current motor test request sent by a test terminal, and carrying out request analysis on the direct current motor test request to obtain a target motor identifier;
inquiring a target direct current motor corresponding to the direct current motor test request according to the target motor identification;
calling a preset test database, and extracting test operation data corresponding to the target direct current motor from the test database;
and performing data cleaning and interpolation filling on the test operation data to obtain standard operation data.
With reference to the first aspect, in a second implementation manner of the first aspect of the present invention, the extracting load power data and operating resistance data in the standard operating data, and constructing a load power distribution curve according to the load power data, and generating an operating resistance change curve according to the operating resistance data respectively includes:
acquiring a first test keyword and a second test keyword of the target direct current motor based on a pre-constructed keyword list;
extracting load power data in the standard operation data according to the first test keywords, and inquiring operation resistance data in the standard operation data according to the second test keywords;
Drawing a load power distribution curve corresponding to the load power data according to a preset first curve fitting function;
and calculating an operation resistance change curve corresponding to the operation resistance data according to a preset second curve fitting function.
With reference to the first aspect, in a third implementation manner of the first aspect of the present invention, the extracting curve features of the load power distribution curve and the running resistance change curve to obtain a load power feature point and a running resistance feature point, and generating a target feature point set according to the load power feature point and the running resistance feature point includes:
obtaining a standard power distribution curve and a standard resistance change curve according to the target direct current motor;
performing feature extraction on the load power distribution curve and the standard power distribution curve to obtain a plurality of first feature values, and performing feature comparison on the running resistance change curve and the standard resistance change curve to obtain a plurality of second feature values;
calculating a first average value corresponding to the first characteristic values and a second average value corresponding to the second characteristic values;
comparing the plurality of first characteristic values with the first average value to obtain a first comparison result, and generating a load power characteristic point according to the first comparison result;
Comparing the plurality of second characteristic values with the second average value to obtain a second comparison result, and generating an operating resistance characteristic point according to the second comparison result;
and constructing a target characteristic point set according to the load power characteristic points and the running resistance characteristic points.
With reference to the first aspect, in a fourth implementation manner of the first aspect of the present invention, the vector mapping is performed on the target feature point set to obtain a target feature vector, and the target feature vector is input into a preset motor efficiency analysis model to perform motor efficiency analysis, so as to obtain a motor efficiency analysis result, where the motor efficiency analysis result includes: energy efficiency class and maximum load power, comprising:
discretizing the target feature point set, and acquiring an abscissa set and an ordinate set of the target feature point set;
data alignment is carried out on the abscissa set and the ordinate set, and an initial characteristic sequence is obtained;
vector conversion is carried out on the initial feature sequence to obtain a target feature vector;
inputting the target feature vector into a preset motor energy efficiency analysis model, and calculating an energy efficiency analysis result of the target direct current motor through the motor energy efficiency analysis model, wherein the motor energy efficiency analysis result comprises: energy efficiency class and maximum load power.
With reference to the first aspect, in a fifth implementation manner of the first aspect of the present invention, the matching, according to the motor efficiency analysis result, a motor management scheme corresponding to the target dc motor includes:
acquiring a preset management scheme list, and extracting list information from the management scheme list to obtain a plurality of candidate management schemes;
according to the energy efficiency level, matching degree calculation is carried out on the plurality of candidate management schemes, and the matching degree of each candidate management scheme is obtained;
and selecting a motor management scheme corresponding to the target direct current motor according to the matching degree of each candidate management scheme.
With reference to the first aspect, in a sixth implementation manner of the first aspect of the present invention, the method for testing motor performance based on artificial intelligence further includes:
performing information association processing on the motor management scheme and the target direct current motor, and performing noise detection on the target direct current motor to generate a noise detection result;
and optimizing the motor management scheme according to the noise detection result to obtain an optimized motor management scheme, and visually displaying the optimized motor management scheme.
The second aspect of the present invention provides a device for testing motor performance based on artificial intelligence, the device for testing motor performance based on artificial intelligence comprising:
The acquisition module is used for receiving a direct current motor test request, acquiring test operation data of a target direct current motor from a preset test database according to the direct current motor test request, and carrying out data preprocessing on the test operation data to obtain standard operation data;
the construction module is used for respectively extracting load power data and operation resistance data in the standard operation data, constructing a load power distribution curve according to the load power data and generating an operation resistance change curve according to the operation resistance data;
the extraction module is used for extracting curve characteristics of the load power distribution curve and the running resistance change curve to obtain load power characteristic points and running resistance characteristic points, and generating a target characteristic point set according to the load power characteristic points and the running resistance characteristic points;
the analysis module is used for carrying out vector mapping on the target feature point set to obtain a target feature vector, inputting the target feature vector into a preset energy efficiency analysis model to carry out motor efficiency analysis to obtain a motor efficiency analysis result, wherein the motor efficiency analysis result comprises the following steps: energy efficiency class and maximum load power;
And the matching module is used for matching the motor management scheme corresponding to the target direct current motor according to the motor energy efficiency analysis result.
A third aspect of the present invention provides an artificial intelligence based motor performance testing apparatus, comprising: a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to cause the artificial intelligence based motor performance testing apparatus to perform the artificial intelligence based motor performance testing method described above.
A fourth aspect of the invention provides a computer readable storage medium having instructions stored therein which, when run on a computer, cause the computer to perform the above-described artificial intelligence based motor performance test method.
In the technical scheme provided by the invention, load power data and operation resistance data in standard operation data are respectively extracted, a load power distribution curve is constructed according to the load power data, and an operation resistance change curve is generated according to the operation resistance data; extracting curve characteristics of a load power distribution curve and an operating resistance change curve to obtain load power characteristic points and operating resistance characteristic points, and generating a target characteristic point set according to the load power characteristic points and the operating resistance characteristic points; vector mapping is carried out on the target feature point set to obtain a target feature vector, and the target feature vector is input into a preset motor energy efficiency analysis model to carry out energy efficiency analysis, so that a motor energy efficiency analysis result is obtained; according to the motor management scheme corresponding to the target direct current motor, the load power and the running resistance of the target direct current motor are intelligently analyzed according to the energy efficiency analysis result, and then the energy efficiency grade and the maximum load power of the target direct current motor are analyzed through the energy efficiency analysis model, so that the performance test accuracy of the direct current motor is improved.
Drawings
FIG. 1 is a schematic diagram of one embodiment of a method for testing motor performance based on artificial intelligence in an embodiment of the invention;
FIG. 2 is a flow chart of constructing a load power profile and an operating resistance profile in accordance with an embodiment of the present invention;
FIG. 3 is a flowchart of generating a target feature point set in an embodiment of the invention;
FIG. 4 is a flow chart of a motor efficiency analysis in an embodiment of the invention;
FIG. 5 is a schematic diagram of one embodiment of an artificial intelligence based motor performance testing apparatus in accordance with an embodiment of the present invention;
FIG. 6 is a schematic diagram of one embodiment of an artificial intelligence based motor performance testing apparatus in accordance with an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a motor performance testing method based on artificial intelligence and a related device, which are used for improving the performance testing accuracy of a direct current motor. The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, a specific flow of an embodiment of the present invention is described below with reference to fig. 1, where an embodiment of a method for testing motor performance based on artificial intelligence in an embodiment of the present invention includes:
s101, receiving a direct current motor test request, acquiring test operation data of a target direct current motor from a preset test database according to the direct current motor test request, and performing data preprocessing on the test operation data to obtain standard operation data;
it will be appreciated that the execution subject of the present invention may be an artificial intelligence based motor performance testing device, and may also be a terminal or a server, which is not limited herein. The embodiment of the invention is described by taking a server as an execution main body as an example.
Specifically, the server receives a direct current motor test request, and acquires test operation data of a target direct current motor from a preset test database according to the direct current motor test request, wherein the server acquires the test operation data and a log file corresponding to the target direct current motor, creates a corresponding data cleaning strategy according to the test operation data and the log file of the target direct current motor, and preprocesses the test operation data according to the operation mode of the target direct current motor by the data cleaning strategy to obtain standard operation data.
S102, respectively extracting load power data and operation resistance data in standard operation data, constructing a load power distribution curve according to the load power data, and generating an operation resistance change curve according to the operation resistance data;
specifically, the server extracts load power data and operation resistance data in standard operation data respectively, the load power data is gauged into Gaussian distribution data taking the load coefficient as mathematical expectation, different load coefficients are clustered from the Gaussian distribution data, the server fits a load power distribution curve from historical power data to which the different load coefficients belong by adopting a cubic polynomial, and then the server generates an operation resistance change curve according to the operation resistance data.
S103, extracting curve characteristics of a load power distribution curve and an operation resistance change curve to obtain load power characteristic points and operation resistance characteristic points, and generating a target characteristic point set according to the load power characteristic points and the operation resistance characteristic points;
the server obtains a deviation curve according to the difference between the load power distribution curve and the running resistance change curve, interpolates the load power distribution curve and the running resistance change curve, converts the load power distribution curve and the running resistance change curve into continuous curves, extracts the characteristic of the load power distribution curve and the running resistance change curve at equal intervals, obtains load power characteristic points and running resistance characteristic points, and generates a target characteristic point set according to the load power characteristic points and the running resistance characteristic points.
S104, carrying out vector mapping on the target feature point set to obtain a target feature vector, and inputting the target feature vector into a preset motor energy efficiency analysis model to carry out energy efficiency analysis to obtain a motor energy efficiency analysis result, wherein the motor energy efficiency analysis result comprises: energy efficiency class and maximum load power;
specifically, the server performs discretization processing on the target feature point set, acquires an abscissa set and an ordinate set of the target feature point set, performs data alignment on the abscissa set and the ordinate set to obtain an initial feature sequence, performs vector conversion on the initial feature sequence to obtain a target feature vector, further performs energy efficiency analysis on the target feature vector by inputting the target feature vector into a preset motor energy efficiency analysis model, wherein the server performs vector feature value calculation on the target feature vector, determines a corresponding vector feature value, and further performs energy efficiency matching according to the vector feature value to determine a corresponding motor energy efficiency analysis result, and the motor energy efficiency analysis result comprises: energy efficiency class and maximum load power.
S105, matching a motor management scheme corresponding to the target direct current motor according to a motor energy analysis result.
Specifically, a plurality of candidate management schemes are obtained, matching degree calculation is carried out on a motor energy analysis result and the plurality of candidate management schemes, the matching degree of each candidate management scheme is determined, then the server carries out maximum screening according to the matching degree of each candidate management scheme, the maximum matching degree value is determined, and finally, the server selects a motor management scheme corresponding to the target direct current motor from the plurality of candidate management schemes according to the maximum matching degree value.
In the embodiment of the invention, load power data and operation resistance data in standard operation data are respectively extracted, a load power distribution curve is constructed according to the load power data, and an operation resistance change curve is generated according to the operation resistance data; extracting curve characteristics of a load power distribution curve and an operating resistance change curve to obtain load power characteristic points and operating resistance characteristic points, and generating a target characteristic point set according to the load power characteristic points and the operating resistance characteristic points; vector mapping is carried out on the target feature point set to obtain a target feature vector, and the target feature vector is input into a preset motor energy efficiency analysis model to carry out energy efficiency analysis, so that a motor energy efficiency analysis result is obtained; according to the motor management scheme corresponding to the target direct current motor, the load power and the running resistance of the target direct current motor are intelligently analyzed according to the energy efficiency analysis result, and then the energy efficiency grade and the maximum load power of the target direct current motor are analyzed through the energy efficiency analysis model, so that the performance test accuracy of the direct current motor is improved.
In a specific embodiment, the process of executing step S101 may specifically include the following steps:
(1) Receiving a direct current motor test request sent by a test terminal, and carrying out request analysis on the direct current motor test request to obtain a target motor identifier;
(2) Inquiring a target direct current motor corresponding to the direct current motor test request according to the target motor identification;
(3) Calling a preset test database, and extracting test operation data corresponding to the target direct current motor from the test database;
(4) And performing data cleaning and interpolation filling on the test operation data to obtain standard operation data.
Specifically, the server receives a direct current motor test request sent by the test terminal, analyzes the direct current motor test request to obtain a target motor identifier, queries a target direct current motor corresponding to the direct current motor test request according to the target motor identifier, further, the server calls the test database through the target direct current motor, conducts data traversal on the test database to determine a sub-data set corresponding to the target direct current motor, further, the server extracts test operation data corresponding to the target direct current motor from the test database according to the sub-data set, and finally, the server conducts data cleaning on the test operation data, wherein the data cleaning mainly comprises repeated value deletion, missing value filling and the like on the test operation data, and further, the server conducts interpolation filling on the test operation data to obtain standard operation data.
In a specific embodiment, as shown in fig. 2, the process of executing step S102 may specifically include the following steps:
s201, acquiring a first test keyword and a second test keyword of a target direct current motor based on a pre-constructed keyword list;
s202, extracting load power data in standard operation data according to a first test keyword, and inquiring operation resistance data in the standard operation data according to a second test keyword;
s203, drawing a load power distribution curve corresponding to the load power data according to a preset first curve fitting function;
s204, calculating an operation resistance change curve corresponding to the operation resistance data according to a preset second curve fitting function.
Specifically, the server acquires a first test keyword and a second test keyword of the target direct current motor based on a pre-constructed keyword table, wherein the server acquires the pre-constructed keyword table, performs feature extraction on standard operation data according to the first test keyword of the target direct current motor, determines load power data in the standard operation data, and queries operation resistance data in the standard operation data according to the second test keyword; and drawing a load power distribution curve corresponding to the load power data according to a preset first curve fitting function, wherein the server performs coordinate point mapping on the load power data according to the first curve fitting function, determines a corresponding mapping coordinate point set, further performs curve drawing according to the mapping coordinate point set to obtain the load power distribution curve corresponding to the load power data, and further calculates an operation resistance change curve corresponding to the operation resistance data according to a preset second curve fitting function.
In a specific embodiment, as shown in fig. 3, the process of executing step S103 may specifically include the following steps:
s301, acquiring a standard power distribution curve and a standard resistance change curve according to a target direct current motor;
s302, carrying out feature extraction on a load power distribution curve and a standard power distribution curve to obtain a plurality of first feature values, and carrying out feature comparison on an operation resistance change curve and a standard resistance change curve to obtain a plurality of second feature values;
s303, calculating a first average value corresponding to the first characteristic values and a second average value corresponding to the second characteristic values;
s304, comparing the plurality of first characteristic values with the first average value to obtain a first comparison result, and generating a load power characteristic point according to the first comparison result;
s305, comparing the plurality of second characteristic values with the second average value to obtain a second comparison result, and generating an operation resistance characteristic point according to the second comparison result;
s306, constructing a target characteristic point set according to the load power characteristic points and the running resistance characteristic points.
The method comprises the steps that a server obtains a standard power distribution curve and a standard resistance change curve according to a target direct-current motor, performs feature extraction on a load power distribution curve and the standard power distribution curve to obtain a plurality of first feature values, performs feature comparison on an operation resistance change curve and the standard resistance change curve to obtain a plurality of second feature values, wherein the server performs curve feature point analysis on the load power curve and the standard power curve to determine a curve feature point set to obtain a plurality of first feature values, performs feature comparison on the operation resistance change curve and the standard resistance change curve to obtain a plurality of second feature values, further calculates a first average value corresponding to the plurality of first feature values, calculates a second average value corresponding to the plurality of second feature values, compares the plurality of first feature values with the first average value to obtain a first comparison result, and generates a load power feature point according to the first comparison result, when a difference value between the first feature value and the first comparison result is larger than a preset threshold value, determines a corresponding feature point, and further performs feature comparison between the corresponding feature point and the second average value, and further generates a corresponding feature point when the second feature value is larger than the preset feature point.
In a specific embodiment, as shown in fig. 4, the process of executing step S104 may specifically include the following steps:
s401, discretizing the target feature point set, and acquiring an abscissa set and an ordinate set of the target feature point set;
s402, carrying out data alignment on an abscissa set and an ordinate set to obtain an initial characteristic sequence;
s403, carrying out vector conversion on the initial feature sequence to obtain a target feature vector;
s404, inputting the target feature vector into a preset motor energy efficiency analysis model, and calculating an energy efficiency analysis result of the target direct current motor through the motor energy efficiency analysis model, wherein the motor energy efficiency analysis result comprises: energy efficiency class and maximum load power.
Specifically, the server performs discretization processing on the target feature point set, and acquires an abscissa set and an ordinate set of the target feature point set, wherein the server performs preprocessing on the target feature point set, performs segmentation on the preprocessed target feature point set, further performs classification to form class labels, performs discretization processing on the target feature point set by adopting a chimerger algorithm in combination with the class labels, and acquires the abscissa set and the ordinate set of the target feature point set. Further, the server performs data alignment on the abscissa set and the ordinate set to obtain an initial feature sequence, and when performing data alignment, the server performs data pair matching on the abscissa set and the ordinate set to determine multiple groups of data pairs, and further, the server performs data alignment on the abscissa set and the ordinate set according to the multiple groups of data pairs to obtain the initial feature sequence. Vector conversion is carried out on the initial feature sequence to obtain a target feature vector; inputting the target feature vector into a preset motor energy efficiency analysis model, and calculating a motor energy efficiency analysis result of the target direct current motor through the motor energy efficiency analysis model, wherein the server inputs the target feature vector into the preset motor energy efficiency analysis model to perform motor energy efficiency analysis, wherein the server performs vector feature value calculation on the target feature vector to determine a corresponding vector feature value, and further performs energy efficiency matching according to the vector feature value to determine a corresponding motor energy efficiency analysis result, and the motor energy efficiency analysis result comprises: energy efficiency class and maximum load power.
In a specific embodiment, the process of executing step S105 may specifically include the following steps:
(1) Acquiring a preset management scheme list, and extracting list information from the management scheme list to obtain a plurality of candidate management schemes;
(2) According to the energy efficiency level, matching degree calculation is carried out on the plurality of candidate management schemes, and the matching degree of each candidate management scheme is obtained;
(3) And selecting a motor management scheme corresponding to the target direct current motor according to the matching degree of each candidate management scheme.
Specifically, a preset management scheme list is obtained, list information extraction is carried out on the management scheme list to obtain a plurality of candidate management schemes, matching degree calculation is carried out on the plurality of candidate management schemes according to energy efficiency levels to obtain the matching degree of each candidate management scheme, the plurality of candidate management schemes are obtained, matching degree calculation is carried out on a motor efficiency analysis result and the plurality of candidate management schemes to determine the matching degree of each candidate management scheme, the server carries out maximum screening according to the matching degree of each candidate management scheme to determine the maximum matching degree value, and finally, the server selects a motor management scheme corresponding to a target direct current motor from the plurality of candidate management schemes according to the maximum matching degree value.
In a specific embodiment, the method for testing motor performance based on artificial intelligence further includes the following steps:
(1) Performing information association processing on the motor management scheme and the target direct current motor, and performing noise detection on the target direct current motor to generate a noise detection result;
(2) And optimizing the motor management scheme according to the noise detection result to obtain an optimized motor management scheme, and visually displaying the optimized motor management scheme.
Specifically, the method comprises the steps of carrying out information association processing on a motor management scheme and a target direct current motor, carrying out noise detection on the target direct current motor, and generating a noise detection result, wherein a server identifies a plurality of association objects with association relations, determines different association sources with direct association relations in the plurality of association objects and association information between the association sources, wherein the association relations comprise the motor management scheme and the target direct current motor, carries out noise detection on the target direct current motor, generates the noise detection result, and finally optimizes the motor management scheme according to the noise detection result to obtain an optimized motor management scheme, and visually displays the optimized motor management scheme.
The method for testing the performance of the motor based on the artificial intelligence in the embodiment of the present invention is described above, and the device for testing the performance of the motor based on the artificial intelligence in the embodiment of the present invention is described below, referring to fig. 5, one embodiment of the device for testing the performance of the motor based on the artificial intelligence in the embodiment of the present invention includes:
the acquisition module 501 is configured to receive a dc motor test request, acquire test operation data of a target dc motor from a preset test database according to the dc motor test request, and perform data preprocessing on the test operation data to obtain standard operation data;
the construction module 502 is configured to extract load power data and operating resistance data in the standard operating data, construct a load power distribution curve according to the load power data, and generate an operating resistance change curve according to the operating resistance data;
the extracting module 503 is configured to perform curve feature extraction on the load power distribution curve and the operating resistance change curve to obtain a load power feature point and an operating resistance feature point, and generate a target feature point set according to the load power feature point and the operating resistance feature point;
The analysis module 504 is configured to perform vector mapping on the target feature point set to obtain a target feature vector, and input the target feature vector into a preset motor efficiency analysis model to perform motor efficiency analysis, so as to obtain a motor efficiency analysis result, where the motor efficiency analysis result includes: energy efficiency class and maximum load power;
and the matching module 505 is configured to match a motor management scheme corresponding to the target dc motor according to the motor energy efficiency analysis result.
Respectively extracting load power data and operating resistance data in standard operating data through the cooperative cooperation of the components, constructing a load power distribution curve according to the load power data, and generating an operating resistance change curve according to the operating resistance data; extracting curve characteristics of a load power distribution curve and an operating resistance change curve to obtain load power characteristic points and operating resistance characteristic points, and generating a target characteristic point set according to the load power characteristic points and the operating resistance characteristic points; vector mapping is carried out on the target feature point set to obtain a target feature vector, and the target feature vector is input into a preset motor energy efficiency analysis model to carry out energy efficiency analysis, so that a motor energy efficiency analysis result is obtained; according to the motor management scheme corresponding to the target direct current motor, the load power and the running resistance of the target direct current motor are intelligently analyzed according to the energy efficiency analysis result, and then the energy efficiency grade and the maximum load power of the target direct current motor are analyzed through the energy efficiency analysis model, so that the performance test accuracy of the direct current motor is improved.
The above fig. 5 describes the artificial intelligence based motor performance testing apparatus in the embodiment of the present invention in detail from the point of view of the modularized functional entity, and the following describes the artificial intelligence based motor performance testing device in the embodiment of the present invention in detail from the point of view of hardware processing.
Fig. 6 is a schematic structural diagram of an artificial intelligence based motor performance testing apparatus 600 according to an embodiment of the present invention, which may vary widely according to configuration or performance, and may include one or more processors (central processing units, CPU) 610 (e.g., one or more processors) and a memory 620, one or more storage media 630 (e.g., one or more mass storage devices) storing application programs 633 or data 632. Wherein the memory 620 and the storage medium 630 may be transitory or persistent storage. The program stored on the storage medium 630 may include one or more modules (not shown), each of which may include a series of instruction operations in the artificial intelligence based motor performance testing apparatus 600. Still further, the processor 610 may be configured to communicate with the storage medium 630 to execute a series of instruction operations in the storage medium 630 on the artificial intelligence based motor performance testing device 600.
The artificial intelligence based motor performance testing apparatus 600 may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input/output interfaces 660, and/or one or more operating systems 631, such as Windows Serve, mac OS X, unix, linux, freeBSD, and the like. It will be appreciated by those skilled in the art that the artificial intelligence based motor performance test apparatus structure illustrated in fig. 6 is not limiting and may include more or fewer components than illustrated, or may combine certain components, or a different arrangement of components.
The invention also provides an artificial intelligence based motor performance testing device, which comprises a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of the artificial intelligence based motor performance testing method in the above embodiments.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, and may also be a volatile computer readable storage medium, where instructions are stored in the computer readable storage medium, when the instructions are executed on a computer, cause the computer to perform the steps of the method for testing performance of an artificial intelligence-based motor.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
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 technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random acceS memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will 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 invention.
Claims (10)
1. The motor performance testing method based on the artificial intelligence is characterized by comprising the following steps of:
receiving a direct current motor test request, acquiring test operation data of a target direct current motor from a preset test database according to the direct current motor test request, and performing data preprocessing on the test operation data to obtain standard operation data;
load power data and operation resistance data in the standard operation data are respectively extracted, a load power distribution curve is constructed according to the load power data, and an operation resistance change curve is generated according to the operation resistance data;
Extracting curve characteristics of the load power distribution curve and the running resistance change curve to obtain load power characteristic points and running resistance characteristic points, and generating a target characteristic point set according to the load power characteristic points and the running resistance characteristic points;
vector mapping is carried out on the target feature point set to obtain a target feature vector, the target feature vector is input into a preset motor energy efficiency analysis model to carry out motor energy efficiency analysis, and a motor energy efficiency analysis result is obtained, wherein the motor energy efficiency analysis result comprises: energy efficiency class and maximum load power;
and matching a motor management scheme corresponding to the target direct current motor according to the motor energy analysis result.
2. The method for testing motor performance based on artificial intelligence according to claim 1, wherein the receiving a dc motor test request, obtaining test operation data of a target dc motor from a preset test database according to the dc motor test request, and performing data preprocessing on the test operation data to obtain standard operation data, includes:
receiving a direct current motor test request sent by a test terminal, and carrying out request analysis on the direct current motor test request to obtain a target motor identifier;
Inquiring a target direct current motor corresponding to the direct current motor test request according to the target motor identification;
calling a preset test database, and extracting test operation data corresponding to the target direct current motor from the test database;
and performing data cleaning and interpolation filling on the test operation data to obtain standard operation data.
3. The method for testing motor performance based on artificial intelligence according to claim 1, wherein the extracting load power data and operating resistance data in the standard operating data, respectively, and constructing a load power distribution curve according to the load power data, and generating an operating resistance change curve according to the operating resistance data, comprises:
acquiring a first test keyword and a second test keyword of the target direct current motor based on a pre-constructed keyword list;
extracting load power data in the standard operation data according to the first test keywords, and inquiring operation resistance data in the standard operation data according to the second test keywords;
drawing a load power distribution curve corresponding to the load power data according to a preset first curve fitting function;
And calculating an operation resistance change curve corresponding to the operation resistance data according to a preset second curve fitting function.
4. The method for testing motor performance based on artificial intelligence according to claim 1, wherein the performing curve feature extraction on the load power distribution curve and the running resistance change curve to obtain a load power feature point and a running resistance feature point, and generating a target feature point set according to the load power feature point and the running resistance feature point comprises:
obtaining a standard power distribution curve and a standard resistance change curve according to the target direct current motor;
performing feature extraction on the load power distribution curve and the standard power distribution curve to obtain a plurality of first feature values, and performing feature comparison on the running resistance change curve and the standard resistance change curve to obtain a plurality of second feature values;
calculating a first average value corresponding to the first characteristic values and a second average value corresponding to the second characteristic values;
comparing the plurality of first characteristic values with the first average value to obtain a first comparison result, and generating a load power characteristic point according to the first comparison result;
Comparing the plurality of second characteristic values with the second average value to obtain a second comparison result, and generating an operating resistance characteristic point according to the second comparison result;
and constructing a target characteristic point set according to the load power characteristic points and the running resistance characteristic points.
5. The method for testing motor performance based on artificial intelligence according to claim 1, wherein the vector mapping is performed on the target feature point set to obtain a target feature vector, and the target feature vector is input into a preset motor efficiency analysis model for energy efficiency analysis to obtain an energy efficiency analysis result, wherein the energy efficiency analysis result comprises: energy efficiency class and maximum load power, comprising:
discretizing the target feature point set, and acquiring an abscissa set and an ordinate set of the target feature point set;
data alignment is carried out on the abscissa set and the ordinate set, and an initial characteristic sequence is obtained;
vector conversion is carried out on the initial feature sequence to obtain a target feature vector;
inputting the target feature vector into a preset motor energy efficiency analysis model, and calculating an energy efficiency analysis result of the target direct current motor through the motor energy efficiency analysis model, wherein the motor energy efficiency analysis result comprises: energy efficiency class and maximum load power.
6. The method for testing motor performance based on artificial intelligence according to claim 1, wherein the matching the motor management scheme corresponding to the target dc motor according to the motor performance analysis result comprises:
acquiring a preset management scheme list, and extracting list information from the management scheme list to obtain a plurality of candidate management schemes;
according to the energy efficiency level, matching degree calculation is carried out on the plurality of candidate management schemes, and the matching degree of each candidate management scheme is obtained;
and selecting a motor management scheme corresponding to the target direct current motor according to the matching degree of each candidate management scheme.
7. The method for testing motor performance based on artificial intelligence according to claim 1, further comprising:
performing information association processing on the motor management scheme and the target direct current motor, and performing noise detection on the target direct current motor to generate a noise detection result;
and optimizing the motor management scheme according to the noise detection result to obtain an optimized motor management scheme, and visually displaying the optimized motor management scheme.
8. An artificial intelligence based motor performance testing device, which is characterized in that the artificial intelligence based motor performance testing device comprises:
the acquisition module is used for receiving a direct current motor test request, acquiring test operation data of a target direct current motor from a preset test database according to the direct current motor test request, and carrying out data preprocessing on the test operation data to obtain standard operation data;
the construction module is used for respectively extracting load power data and operation resistance data in the standard operation data, constructing a load power distribution curve according to the load power data and generating an operation resistance change curve according to the operation resistance data;
the extraction module is used for extracting curve characteristics of the load power distribution curve and the running resistance change curve to obtain load power characteristic points and running resistance characteristic points, and generating a target characteristic point set according to the load power characteristic points and the running resistance characteristic points;
the analysis module is used for carrying out vector mapping on the target feature point set to obtain a target feature vector, inputting the target feature vector into a preset energy efficiency analysis model to carry out motor efficiency analysis to obtain a motor efficiency analysis result, wherein the motor efficiency analysis result comprises the following steps: energy efficiency class and maximum load power;
And the matching module is used for matching the motor management scheme corresponding to the target direct current motor according to the motor energy efficiency analysis result.
9. An artificial intelligence based motor performance testing apparatus, wherein the artificial intelligence based motor performance testing apparatus comprises: a memory and at least one processor, the memory having instructions stored therein;
the at least one processor invokes the instructions in the memory to cause the artificial intelligence based motor performance testing apparatus to perform the artificial intelligence based motor performance testing method of any one of claims 1-7.
10. A computer readable storage medium having instructions stored thereon, which when executed by a processor, implement the artificial intelligence based motor performance testing method of any one of claims 1-7.
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