CN114400928B - Cloud-control intelligent brushless motor driving system - Google Patents
Cloud-control intelligent brushless motor driving system Download PDFInfo
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- CN114400928B CN114400928B CN202210012458.3A CN202210012458A CN114400928B CN 114400928 B CN114400928 B CN 114400928B CN 202210012458 A CN202210012458 A CN 202210012458A CN 114400928 B CN114400928 B CN 114400928B
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- 238000004458 analytical method Methods 0.000 claims abstract description 103
- 230000007613 environmental effect Effects 0.000 claims abstract description 28
- 238000004364 calculation method Methods 0.000 claims abstract description 26
- 238000007405 data analysis Methods 0.000 claims abstract description 19
- 238000000034 method Methods 0.000 claims abstract description 5
- 239000012080 ambient air Substances 0.000 claims description 14
- 239000003570 air Substances 0.000 claims description 11
- 238000012937 correction Methods 0.000 claims description 5
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- 230000002159 abnormal effect Effects 0.000 claims description 3
- 230000001276 controlling effect Effects 0.000 claims description 3
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
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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
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
Abstract
The invention relates to a cloud control intelligent brushless motor driving system, in particular to the technical field of data processing of the Internet of things, which comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring operation parameters and environmental parameters of a brushless motor in real time; the analysis module is used for carrying out data analysis on the operation parameters and the environment parameters, is connected with the acquisition module and is internally provided with an operation state coefficient H and a calculation formula thereof; the judging module is used for judging the operation safety of the brushless motor according to the data analysis result and is connected with the analysis module; the storage module is used for storing the operation parameters, the environment parameters, the data analysis results and the safety judgment results which are acquired in real time and is connected with the acquisition module, the analysis module and the judgment module; and the control module is used for controlling the operation process of the brushless motor according to the safety judgment result and is connected with the judgment module. The invention effectively improves the safe operation efficiency of the brushless motor of the Internet of things.
Description
Technical Field
The invention relates to the technical field of data processing of the Internet of things, in particular to a cloud control intelligent brushless motor driving system.
Background
The internet of things technology is an emerging technology, and the internet of things can be understood as an extension of the internet, and the internet of things is connected with the internet, so that communication between a far end and an internet of things terminal is realized, and control and information sharing of the internet of things are realized. Compared with the Internet age, the Internet of things has more various communication protocols, the fragmentation of the objects is very serious, the importance of the gateway is also highlighted, the Internet of things gateway can integrate information collected by different objects and transmit the information to the next level, and therefore, the information can be mutually transmitted between all parts. With the rise of the internet of things technology, the gradually increased internet of things terminal equipment can generate more and more information which can be widely used for data analysis, model training and the like, and the huge amount of information is processed, so that the support of the big data technology is required.
In the prior art, a brushless motor controlled through the Internet of things cannot accurately control the running state of the brushless motor in most running processes, so that the phenomenon that faults of the brushless motor occur frequently is caused, and the problem that the running safety of the brushless motor of the Internet of things is low is caused.
Disclosure of Invention
Therefore, the invention provides a cloud control intelligent brushless motor driving system which is used for solving the problem of low operation efficiency of a brushless motor caused by incapability of carrying out safety monitoring according to the real-time operation state of the brushless motor in the prior art.
In order to achieve the above object, the present invention provides a cloud-controlled intelligent brushless motor driving system, comprising,
The acquisition module is used for acquiring the operation parameters and the environment parameters of the brushless motor in real time;
The analysis module is used for carrying out data analysis on the operation parameters and the environment parameters, is connected with the acquisition module, is internally provided with an operation state coefficient H and a calculation formula thereof, wherein the calculation factor of the operation state coefficient H comprises motor frequency, motor temperature and motor operation time, the analysis module is also used for determining the value of a frequency influence coefficient according to the motor frequency A of the brushless motor acquired in real time, regulating the value of the frequency influence coefficient according to the historical operation maximum frequency Aa, determining the value of the temperature influence coefficient according to the motor temperature C of the brushless motor acquired in real time, regulating the value of the temperature influence coefficient according to the historical operation maximum temperature Ca, correcting the calculated operation state coefficient H according to the environment humidity G of the working environment, and compensating the corrected operation state coefficient according to the environment air pressure K of the working environment;
the judging module is used for judging the operation safety of the brushless motor according to the data analysis result, and is connected with the analysis module, and when the judging module judges the operation safety, the judging module judges the operation safety of the brushless motor according to the compensated operation state coefficient H';
the storage module is used for storing the operation parameters, the environment parameters, the data analysis results and the safety judgment results which are acquired in real time and is connected with the acquisition module, the analysis module and the judgment module;
and the control module is used for controlling the operation process of the brushless motor according to the safety judgment result and is connected with the judgment module.
Further, the analysis module is provided with an operation state coefficient H when data analysis is performed, H=M+N+Ta/T is set, wherein M is a frequency influence coefficient, N is a temperature influence coefficient, ta is the motor operated time, T is a preset motor operation period, the analysis module compares the motor frequency A of the brushless motor acquired in real time with each preset motor frequency when the value of the frequency influence coefficient is set, and sets the value of the frequency influence coefficient according to the comparison result,
When A is smaller than A1, the analysis module judges that the motor frequency is low, sets the value of a frequency influence coefficient as M2, and sets M2=M1+M1× (A1-A)/A, wherein when A is smaller than Amin, the control module blocks the operation of the brushless motor, and Amin is a preset minimum motor frequency;
when A1 is more than or equal to A2, the analysis module judges that the frequency of the motor is normal, and sets the value of the frequency influence coefficient as M1, wherein M1 is a preset value;
When A2 is smaller than A, the analysis module judges that the motor frequency is high, sets the value of a frequency influence coefficient as M3, and sets M3=M1+M1× (a-A2)/A2, wherein when A is larger than Amax, the control module blocks the operation of the brushless motor, and Amax is the preset maximum motor frequency;
wherein A1 is a first preset motor frequency, A2 is a second preset motor frequency, and A1 is less than A2.
Further, when the analysis module adjusts the frequency influence coefficient, the analysis module obtains the historical operation maximum frequency Aa of the brushless motor, compares the historical operation maximum frequency Aa with the motor frequency A detected in real time, adjusts the value Mi of the frequency influence coefficient according to the comparison result, sets i=1, 2,
When A is less than or equal to Aa, the analysis module does not adjust;
when A > Aa, the analysis module adjusts the value of the frequency influence coefficient to Mi ', and sets Mi' =Mi+Mix (a-Aa)/A.
Further, the analysis module compares the motor temperature C of the brushless motor acquired in real time with a preset standard motor temperature C0 when setting the value of the temperature influence coefficient, and sets the value of the temperature influence coefficient according to the comparison result,
When C is less than or equal to C0, the analysis module judges that the temperature of the motor is normal, and sets the value of the temperature influence coefficient as N1;
When C0 is more than C and less than or equal to Cmax, the analysis module judges that the temperature of the motor is higher, sets the value of the temperature influence coefficient as N2, and sets N2=N1+N1× (C-C0)/C0, wherein Cmax is the preset maximum motor temperature;
When C > Cmax, the analysis module judges that the temperature of the motor is too high, and the control module blocks the operation of the brushless motor.
Further, when the analysis module adjusts the temperature influence coefficient, the analysis module also obtains the historical running highest temperature Ca of the brushless motor, compares the historical running highest temperature Ca with the motor temperature C detected in real time, adjusts the value Ni of the temperature influence coefficient according to the comparison result, sets i=1, 2,
When C is less than or equal to Ca, the analysis module does not adjust;
When C > Ca, the analysis module adjusts the value of the temperature influence coefficient to Ni ', and sets Ni' =ni+ni× (C-Ca)/C.
Further, after the calculation of the running state coefficient H is completed, the analysis module obtains the environmental humidity G of the working environment of the brushless motor, compares the environmental humidity G with the preset environmental humidity G0, corrects the calculated running state coefficient H according to the comparison result,
When G is less than or equal to G0, the analysis module judges that the ambient humidity is normal and does not correct;
When G > G0, the analysis module corrects the running state coefficient to H ', setting H' =h+h× (G-G0)/G.
Further, after the correction of the running state coefficient H is completed, the analysis module obtains the ambient air pressure K of the working environment of the brushless motor, compares the ambient air pressure K with the preset ambient air pressure K0, compensates the corrected running state coefficient according to the comparison result,
When K < K0, the analysis module compensates the running state coefficient to H ", setting H" =h '+h' × (K0-K)/K0;
when K is more than or equal to K0, the analysis module judges that the ambient air pressure is normal and does not compensate.
Further, when the analysis module determines the operation safety of the brushless motor, the determination module compares the compensated operation state coefficient H″ with each preset operation state coefficient, and determines the operation safety of the brushless motor according to the comparison result,
When H is less than or equal to H1, the judging module judges that the running state of the brushless motor is normal, and the control module maintains the running of the brushless motor to a period T;
When H1 is less than H and less than or equal to H2, the judging module judges that the running state of the brushless motor is at risk, and the control module shortens the running period T of the brushless motor;
when H2 is less than H', the judging module judges that the running state of the brushless motor is abnormal, and the control module blocks the running of the brushless motor;
Wherein H1 is a first preset running state coefficient, H2 is a second preset running state coefficient, and H1 is less than H2.
Further, when the control module shortens the operation period of the brushless motor, the control module sets the shortened operation period of the brushless motor as T ', sets T ' =T-T× (H ' -H1)/H ', takes T ' =Tmin when T ' =Tmin, and takes Tmin as a preset minimum operation period, the control module compares the shortened operation period T ' of the brushless motor with the motor operated time Ta, and controls the operation state of the motor according to the comparison result,
When T' is less than or equal to Ta, the control module blocks the operation of the brushless motor;
When T '> Ta, the control module maintains the brushless motor running for a period T'.
Further, the operating parameters include motor frequency and motor temperature, and the environmental parameters include ambient humidity and ambient air pressure.
Compared with the prior art, the brushless motor control system has the beneficial effects that when the analysis module performs data analysis, the judgment module judges the operation safety of the brushless motor by setting the operation state coefficient H so as to achieve the real-time monitoring of the brushless motor, thereby improving the operation efficiency of the brushless motor and improving the operation safety, the calculation result of the operation state coefficient H is influenced by the motor frequency, the motor temperature and the operation time, when the analysis module determines the value of the frequency influence coefficient, the analysis module compares the motor frequency A of the brushless motor acquired in real time with a preset value, if the analysis module selects a fixed value as the frequency influence coefficient in a preset range, otherwise, the calculation is performed by a difference value so as to ensure the accuracy of the value of the frequency influence coefficient, and the analysis module precisely controls the value of the frequency influence coefficient so as to improve the accuracy of the calculation of the operation state coefficient H, thereby improving the safety monitoring efficiency of the brushless motor, further improving the operation efficiency of the brushless motor, and when the motor frequency A is smaller than a minimum value or larger than a maximum value, the control module timely blocks the operation of the brushless motor so as to ensure the safety of the operation of the brushless motor.
In particular, after determining the value of the frequency influence coefficient, the analysis module adjusts the frequency influence coefficient according to the maximum frequency Aa of the historical operation of the brushless motor, and the analysis module can effectively improve the accuracy of the value of the frequency influence coefficient by adjusting the frequency influence coefficient according to the historical operation data of the brushless motor, so that the accuracy of the calculation of the operation state coefficient H is further ensured, the safety monitoring efficiency of the brushless motor is improved, and the operation efficiency of the brushless motor is further improved.
In particular, when the analysis module is used for setting the value of the temperature influence coefficient, the motor temperature C of the brushless motor acquired in real time is compared with a preset value to determine the value of the temperature influence coefficient, if the motor temperature C is within the preset value, a fixed value is used as the temperature influence coefficient, if the motor temperature C is greater than the preset value, the calculation is performed according to the difference value between the motor temperature C and the preset value, so that the accuracy of the value of the temperature influence coefficient is ensured, the accuracy of calculation of the running state coefficient H is improved, the safety monitoring efficiency of the brushless motor is improved, the running efficiency of the brushless motor is further improved, and the analysis module is used for adjusting the temperature influence coefficient according to the highest temperature Ca of the historical running of the brushless motor, so that the accuracy of the value of the temperature influence coefficient can be effectively improved, the accuracy of calculation of the running state coefficient H is further ensured, and the running efficiency of the brushless motor is further improved.
In particular, after the calculation of the operation state coefficient H is completed, the analysis module corrects the operation state coefficient H according to the environmental humidity G of the working environment of the brushless motor, so as to reduce the influence of the environmental humidity on the operation state of the motor, if the environmental humidity G is greater than a preset value, the corrected operation state coefficient is calculated according to the difference between the environmental humidity G and the preset value, so as to improve the accuracy of the calculation of the operation state coefficient H, thereby improving the safety monitoring efficiency of the brushless motor, further improving the operation efficiency of the brushless motor, and after the correction is completed, the analysis module compensates the operation state coefficient according to the environmental air pressure K of the working environment of the brushless motor, so as to reduce the influence of the environmental air pressure on the operation state of the motor, and if the environmental air pressure K is smaller than the preset value, the compensated operation state coefficient is calculated according to the difference between the environmental air pressure K and the preset value, so as to improve the accuracy of the calculation of the operation state coefficient H, thereby improving the safety monitoring efficiency of the brushless motor, and further improving the operation efficiency of the brushless motor.
In particular, when the judging module judges the operation safety of the brushless motor, the control module timely adjusts the operation state of the brushless motor by comparing the compensated operation state coefficient H 'with a preset value so as to improve the safety monitoring efficiency of the brushless motor, thereby further improving the operation efficiency of the brushless motor, when the compensated operation state coefficient H' is within the preset value, the judging module judges that the operation state is not normally adjusted, if the operation safety risk is proved to exist between the preset values, the operation period is reduced by reducing the risk that the brushless motor breaks down, if the operation safety is greater than the preset value, the operation of the brushless motor is directly blocked so as to ensure the operation safety of the brushless motor, and meanwhile, when the operation period is reduced, the control module calculates the reduced operation period according to the difference value between the operation state coefficient H 'and the preset value so as to ensure the operation safety of the brushless motor, and simultaneously compares the shortened operation period T' of the brushless motor with the motor operated time Ta so as to block the operation of the brushless motor in time so as to further improve the operation safety of the brushless motor.
Drawings
Fig. 1 is a schematic structural diagram of a cloud-controlled intelligent brushless motor driving system according to the present embodiment.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1, a schematic diagram of a cloud-controlled intelligent brushless motor driving system according to the present embodiment is shown, where the system includes,
The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring operation parameters and environment parameters of the brushless motor in real time, the operation parameters comprise motor frequency and motor temperature, and the environment parameters comprise environment humidity and environment air pressure;
the analysis module is used for carrying out data analysis on the operation parameters and the environment parameters and is connected with the acquisition module;
the judging module is used for judging the operation safety of the brushless motor according to the data analysis result and is connected with the analysis module;
the storage module is used for storing the operation parameters, the environment parameters, the data analysis results and the safety judgment results which are acquired in real time and is connected with the acquisition module, the analysis module and the judgment module;
And the control module is used for controlling the running process of the motor according to the safety judgment result and is connected with the judgment module.
Specifically, in this embodiment, brushless motor passes through the gateway access network and is connected with the high in the clouds, actuating system carries out operation data and historical data analysis through the brushless motor to the access network to the security of real-time supervision brushless motor operation, thereby improves the safe operating efficiency of brushless motor.
Specifically, the analysis module is provided with an operation state coefficient H when data analysis is performed, H=M+N+Ta/T is set, wherein M is a frequency influence coefficient, N is a temperature influence coefficient, ta is the motor operated time, T is a preset motor operation period, the analysis module compares the motor frequency A of the brushless motor acquired in real time with each preset motor frequency when the value of the frequency influence coefficient is set, and sets the value of the frequency influence coefficient according to the comparison result,
When A is smaller than A1, the analysis module judges that the motor frequency is low, sets the value of a frequency influence coefficient as M2, and sets M2=M1+M1× (A1-A)/A, wherein when A is smaller than Amin, the control module blocks the operation of the brushless motor, and Amin is a preset minimum motor frequency;
when A1 is more than or equal to A2, the analysis module judges that the frequency of the motor is normal, and sets the value of the frequency influence coefficient as M1, wherein M1 is a preset value;
When A2 is smaller than A, the analysis module judges that the motor frequency is high, sets the value of a frequency influence coefficient as M3, and sets M3=M1+M1× (a-A2)/A2, wherein when A is larger than Amax, the control module blocks the operation of the brushless motor, and Amax is the preset maximum motor frequency;
wherein A1 is a first preset motor frequency, A2 is a second preset motor frequency, and A1 is less than A2.
Specifically, in this embodiment, when data analysis is performed by the analysis module, the operation state coefficient H is set so that the judgment module judges the operation safety of the brushless motor, so as to achieve real-time monitoring of the brushless motor, thereby improving the operation efficiency of the brushless motor, and improving the operation safety, the calculation result of the operation state coefficient H is influenced by the motor frequency, the motor temperature and the operation time, when the value of the frequency influence coefficient is determined by the analysis module, the motor frequency a of the brushless motor acquired in real time is compared with a preset value by the analysis module, if the fixed value is selected as the frequency influence coefficient in the preset range by the analysis module, otherwise, the calculation is performed by the difference value, so as to ensure the accuracy of the value of the frequency influence coefficient, and the accuracy of the calculation of the operation state coefficient H is improved by the analysis module, so that the safety monitoring efficiency of the brushless motor is improved, and the operation efficiency of the brushless motor is further improved.
Specifically, the analysis module obtains the historical operation maximum frequency Aa of the brushless motor when calculating the operation state coefficient H, compares the historical operation maximum frequency Aa with the motor frequency a detected in real time, adjusts the value Mi of the frequency influence coefficient according to the comparison result, and sets i=1, 2,
When A is less than or equal to Aa, the analysis module does not adjust;
when A > Aa, the analysis module adjusts the value of the frequency influence coefficient to Mi ', and sets Mi' =Mi+Mix (a-Aa)/A.
Specifically, in this embodiment, after determining the value of the frequency influence coefficient, the analysis module adjusts the frequency influence coefficient according to the maximum frequency Aa of the historical operation of the brushless motor, and the analysis module can effectively improve the accuracy of the value of the frequency influence coefficient by adjusting the frequency influence coefficient according to the historical operation data of the brushless motor, so as to further ensure the accuracy of the calculation of the operation state coefficient H, further improve the safety monitoring efficiency of the brushless motor, and further improve the operation efficiency of the brushless motor.
Specifically, when the analysis module sets the value of the temperature influence coefficient, the motor temperature C of the brushless motor acquired in real time is compared with the preset standard motor temperature C0, and the value of the temperature influence coefficient is set according to the comparison result,
When C is less than or equal to C0, the analysis module judges that the temperature of the motor is normal, and sets the value of the temperature influence coefficient as N1;
When C0 is more than C and less than or equal to Cmax, the analysis module judges that the temperature of the motor is higher, sets the value of the temperature influence coefficient as N2, and sets N2=N1+N1× (C-C0)/C0, wherein Cmax is the preset maximum motor temperature;
When C > Cmax, the analysis module judges that the temperature of the motor is too high, and the control module blocks the operation of the brushless motor.
Specifically, the analysis module also obtains the historical running highest temperature Ca of the brushless motor when calculating the running state coefficient H, compares the historical running highest temperature Ca with the motor temperature C detected in real time, adjusts the value Ni of the temperature influence coefficient according to the comparison result, sets i=1, 2,
When C is less than or equal to Ca, the analysis module does not adjust;
When C > Ca, the analysis module adjusts the value of the temperature influence coefficient to Ni ', and sets Ni' =ni+ni× (C-Ca)/C.
Specifically, in this embodiment, when the value of the temperature influence coefficient is set by the analysis module, the motor temperature C of the brushless motor collected in real time is compared with a preset value to determine the value of the temperature influence coefficient, if the motor temperature C is within the preset value, a fixed value is used as the temperature influence coefficient, if the motor temperature C is greater than the preset value, the calculation is performed according to the difference between the motor temperature C and the preset value, so as to ensure the accuracy of the value of the temperature influence coefficient, thereby improving the accuracy of the calculation of the running state coefficient H, thereby improving the safety monitoring efficiency of the brushless motor, further improving the running efficiency of the brushless motor, and the analysis module adjusts the temperature influence coefficient according to the highest temperature Ca of the historical running of the brushless motor, so that the accuracy of the value of the temperature influence coefficient can be effectively improved, thereby further ensuring the accuracy of the calculation of the running state coefficient H, further improving the safety monitoring efficiency of the brushless motor, and further improving the running efficiency of the brushless motor.
Specifically, the analysis module obtains the environmental humidity G of the working environment of the brushless motor after the calculation of the running state coefficient H is completed, compares the environmental humidity G with the preset environmental humidity G0, corrects the calculated running state coefficient H according to the comparison result,
When G is less than or equal to G0, the analysis module judges that the ambient humidity is normal and does not correct;
When G > G0, the analysis module corrects the running state coefficient to H ', setting H' =h+h× (G-G0)/G.
Specifically, the analysis module acquires the ambient air pressure K of the working environment of the brushless motor after finishing the correction of the running state coefficient H, compares the ambient air pressure K with the preset ambient air pressure K0, compensates the corrected running state coefficient according to the comparison result,
When K < K0, the analysis module compensates the running state coefficient to H ", setting H" =h '+h' × (K0-K)/K0;
when K is more than or equal to K0, the analysis module judges that the ambient air pressure is normal and does not compensate.
Specifically, after the calculation of the operation state coefficient H is completed, the analysis module corrects the operation state coefficient H according to the environmental humidity G of the working environment of the brushless motor, so as to reduce the influence of the environmental humidity on the operation state of the motor, and if the environmental humidity G is greater than a preset value, the corrected operation state coefficient is calculated according to the difference between the environmental humidity G and the preset value, so as to improve the accuracy of calculating the operation state coefficient H, thereby improving the safety monitoring efficiency of the brushless motor, further improving the operation efficiency of the brushless motor, and after the correction is completed, the analysis module compensates the operation state coefficient according to the environmental air pressure K of the working environment of the brushless motor, so as to reduce the influence of the environmental air pressure on the operation state of the motor, and if the environmental air pressure K is less than the preset value, calculate the compensated operation state coefficient according to the difference between the environmental air pressure K and the preset value, so as to improve the accuracy of calculating the operation state coefficient H, thereby improving the safety monitoring efficiency of the brushless motor, and further improving the operation efficiency of the brushless motor.
Specifically, after the compensation of the running state coefficient is completed, the judging module compares the compensated running state coefficient H' with each preset running state coefficient and judges the running safety of the brushless motor according to the comparison result,
When H is less than or equal to H1, the judging module judges that the running state of the brushless motor is normal, and the control module maintains the running of the brushless motor to a period T;
When H1 is less than H and less than or equal to H2, the judging module judges that the running state of the brushless motor is at risk, and the control module shortens the running period T of the brushless motor;
when H2 is less than H', the judging module judges that the running state of the brushless motor is abnormal, and the control module blocks the running of the brushless motor;
Wherein H1 is a first preset running state coefficient, H2 is a second preset running state coefficient, and H1 is less than H2.
Specifically, when the control module shortens the operation period of the brushless motor, the control module sets the shortened operation period of the brushless motor as T ', sets T ' =T-T× (H ' -H1)/H ', takes T ' =Tmin when T ' < Tmin, and takes Tmin as a preset minimum operation period, the control module compares the shortened operation period T ' of the brushless motor with the motor operated time Ta, and controls the operation state of the motor according to the comparison result,
When T' is less than or equal to Ta, the control module blocks the operation of the brushless motor;
When T '> Ta, the control module maintains the brushless motor running for a period T'.
Specifically, in this embodiment, when the judging module determines the operation safety of the brushless motor, the control module adjusts the operation state of the brushless motor in time by comparing the compensated operation state coefficient h″ with a preset value, so as to improve the safety monitoring efficiency of the brushless motor, thereby further improving the operation efficiency of the brushless motor, when the compensated operation state coefficient h″ is within the preset value, the judging module judges that the operation state is not normally adjusted, if the risk of operation safety is proved to exist between the preset values, the operation period is reduced by reducing the risk of failure of the brushless motor, if the operation period is greater than the preset value, the operation of the brushless motor is directly blocked to ensure the operation safety of the brushless motor, and meanwhile, when the operation period is reduced, the control module calculates the reduced operation period according to the difference value between the operation state coefficient h″ and the preset value, so as to ensure the operation safety of the brushless motor, and meanwhile, compares the shortened operation period T' of the brushless motor with the motor operated time Ta, so as to block the operation in time, thereby further improving the operation safety of the brushless motor.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
Claims (9)
1.A cloud-controlled intelligent brushless motor driving system is characterized by comprising,
The acquisition module is used for acquiring the operation parameters and the environment parameters of the brushless motor in real time;
The analysis module is used for carrying out data analysis on the operation parameters and the environment parameters, is connected with the acquisition module, is internally provided with an operation state coefficient H and a calculation formula thereof, wherein the calculation factor of the operation state coefficient H comprises motor frequency, motor temperature and motor operation time, the analysis module is also used for determining the value of a frequency influence coefficient according to the motor frequency A of the brushless motor acquired in real time, regulating the value of the frequency influence coefficient according to the historical operation maximum frequency Aa, determining the value of the temperature influence coefficient according to the motor temperature C of the brushless motor acquired in real time, regulating the value of the temperature influence coefficient according to the historical operation maximum temperature Ca, correcting the calculated operation state coefficient H according to the environment humidity G of the working environment, and compensating the corrected operation state coefficient according to the environment air pressure K of the working environment;
the judging module is used for judging the operation safety of the brushless motor according to the data analysis result, and is connected with the analysis module, and when the judging module judges the operation safety, the judging module judges the operation safety of the brushless motor according to the compensated operation state coefficient H';
the storage module is used for storing the operation parameters, the environment parameters, the data analysis results and the safety judgment results which are acquired in real time and is connected with the acquisition module, the analysis module and the judgment module;
The control module is used for controlling the operation process of the brushless motor according to the safety judgment result and is connected with the judgment module;
The analysis module is provided with an operation state coefficient H when data analysis is carried out, H=M+N+Ta/T is set, wherein M is a frequency influence coefficient, N is a temperature influence coefficient, ta is the running time of the motor, T is a preset motor running period, the analysis module compares the motor frequency A of the brushless motor acquired in real time with each preset motor frequency when the value of the frequency influence coefficient is set, and sets the value of the frequency influence coefficient according to the comparison result,
When A is smaller than A1, the analysis module judges that the motor frequency is low, sets the value of a frequency influence coefficient as M2, and sets M2=M1+M1× (A1-A)/A, wherein when A is smaller than Amin, the control module blocks the operation of the brushless motor, and Amin is a preset minimum motor frequency;
when A1 is more than or equal to A2, the analysis module judges that the frequency of the motor is normal, and sets the value of the frequency influence coefficient as M1, wherein M1 is a preset value;
When A2 is smaller than A, the analysis module judges that the motor frequency is high, sets the value of a frequency influence coefficient as M3, and sets M3=M1+M1× (a-A2)/A2, wherein when A is larger than Amax, the control module blocks the operation of the brushless motor, and Amax is the preset maximum motor frequency;
wherein A1 is a first preset motor frequency, A2 is a second preset motor frequency, and A1 is less than A2.
2. The cloud-controlled intelligent brushless motor driving system according to claim 1, wherein the analysis module obtains a historical operation maximum frequency Aa of the brushless motor when adjusting the frequency influence coefficient, compares the historical operation maximum frequency Aa with a motor frequency a detected in real time, adjusts a value Mi of the frequency influence coefficient according to a comparison result, sets i=1, 2,
When A is less than or equal to Aa, the analysis module does not adjust;
when A > Aa, the analysis module adjusts the value of the frequency influence coefficient to Mi ', and sets Mi' =Mi+Mix (a-Aa)/A.
3. The cloud-controlled intelligent brushless motor driving system according to claim 1, wherein the analysis module compares a motor temperature C of the brushless motor acquired in real time with a preset standard motor temperature C0 when setting a value of a temperature influence coefficient, and sets the value of the temperature influence coefficient according to the comparison result,
When C is less than or equal to C0, the analysis module judges that the temperature of the motor is normal, and sets the value of the temperature influence coefficient as N1;
When C0 is more than C and less than or equal to Cmax, the analysis module judges that the temperature of the motor is higher, sets the value of the temperature influence coefficient as N2, and sets N2=N1+N1× (C-C0)/C0, wherein Cmax is the preset maximum motor temperature;
When C > Cmax, the analysis module judges that the temperature of the motor is too high, and the control module blocks the operation of the brushless motor.
4. The cloud-controlled intelligent brushless motor driving system according to claim 3, wherein the analysis module further obtains a historical operation maximum temperature Ca of the brushless motor when adjusting the temperature influence coefficient, compares the historical operation maximum temperature Ca with a motor temperature C detected in real time, adjusts a value Ni of the temperature influence coefficient according to a comparison result, sets i=1, 2,
When C is less than or equal to Ca, the analysis module does not adjust;
When C > Ca, the analysis module adjusts the value of the temperature influence coefficient to Ni ', and sets Ni' =ni+ni× (C-Ca)/C.
5. The cloud-controlled intelligent brushless motor driving system according to claim 1, wherein the analysis module obtains an environmental humidity G of the working environment of the brushless motor after the calculation of the operation state coefficient H is completed, compares the environmental humidity G with a preset environmental humidity G0, and corrects the calculated operation state coefficient H according to the comparison result,
When G is less than or equal to G0, the analysis module judges that the ambient humidity is normal and does not correct;
When G > G0, the analysis module corrects the running state coefficient to H ', setting H' =h+h× (G-G0)/G.
6. The cloud-controlled intelligent brushless motor driving system according to claim 5, wherein the analysis module obtains an ambient air pressure K of the working environment of the brushless motor after finishing the correction of the operation state coefficient H, compares the ambient air pressure K with a preset ambient air pressure K0, compensates the corrected operation state coefficient according to the comparison result, wherein,
When K < K0, the analysis module compensates the running state coefficient to H ", setting H" =h '+h' × (K0-K)/K0;
when K is more than or equal to K0, the analysis module judges that the ambient air pressure is normal and does not compensate.
7. The cloud-controlled intelligent brushless motor driving system according to claim 6, wherein the analysis module compares the compensated operation state coefficient H' with each preset operation state coefficient and determines the operation safety of the brushless motor according to the comparison result when determining the operation safety of the brushless motor, wherein,
When H is less than or equal to H1, the judging module judges that the running state of the brushless motor is normal, and the control module maintains the running of the brushless motor to a period T;
When H1 is less than H and less than or equal to H2, the judging module judges that the running state of the brushless motor is at risk, and the control module shortens the running period T of the brushless motor;
when H2 is less than H', the judging module judges that the running state of the brushless motor is abnormal, and the control module blocks the running of the brushless motor;
Wherein H1 is a first preset running state coefficient, H2 is a second preset running state coefficient, and H1 is less than H2.
8. The cloud-controlled intelligent brushless motor driving system according to claim 7, wherein the control module sets the shortened operation cycle of the brushless motor to be T ', sets T ' =t-t× (H "-H1)/H", and takes T ' =tmin as a preset minimum operation cycle when T ' =tmin, when the operation cycle of the brushless motor is shortened, compares the shortened operation cycle T ' of the brushless motor with the motor operated time Ta, and controls the operation state of the motor according to the comparison result,
When T' is less than or equal to Ta, the control module blocks the operation of the brushless motor;
When T '> Ta, the control module maintains the brushless motor running for a period T'.
9. The cloud-controlled intelligent brushless motor drive system of claim 1, wherein said operating parameters comprise motor frequency and motor temperature, and said environmental parameters comprise ambient humidity and ambient air pressure.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013219846A (en) * | 2012-04-04 | 2013-10-24 | Fanuc Ltd | Motor control device for protecting motor from overheating using estimated motor temperature |
| CN104702196A (en) * | 2013-12-04 | 2015-06-10 | 大连东浦机电有限公司 | Motor operation monitoring system |
| WO2018032512A1 (en) * | 2016-08-19 | 2018-02-22 | 绿仕科技控股有限公司 | Control system |
| CA3068669A1 (en) * | 2019-01-29 | 2020-07-29 | The Boeing Company | Aircraft auxiliary power unit (apu) control system having speed compensation |
| CN113809954A (en) * | 2021-09-10 | 2021-12-17 | 常州市武进亚太机电配件有限公司 | Direct current brushless motor drive control system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150294689A1 (en) * | 2014-04-10 | 2015-10-15 | Seagate Technology Llc | Motor based failure prediction |
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- 2022-01-06 CN CN202210012458.3A patent/CN114400928B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013219846A (en) * | 2012-04-04 | 2013-10-24 | Fanuc Ltd | Motor control device for protecting motor from overheating using estimated motor temperature |
| CN104702196A (en) * | 2013-12-04 | 2015-06-10 | 大连东浦机电有限公司 | Motor operation monitoring system |
| WO2018032512A1 (en) * | 2016-08-19 | 2018-02-22 | 绿仕科技控股有限公司 | Control system |
| CA3068669A1 (en) * | 2019-01-29 | 2020-07-29 | The Boeing Company | Aircraft auxiliary power unit (apu) control system having speed compensation |
| CN113809954A (en) * | 2021-09-10 | 2021-12-17 | 常州市武进亚太机电配件有限公司 | Direct current brushless motor drive control system |
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