CN114400928A - Cloud control intelligent brushless motor driving system - Google Patents
Cloud control intelligent brushless motor driving system Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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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 data processing module and a data processing module, wherein the acquisition module is used for acquiring the operating parameters and the 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 real-time collected operation parameters and environment parameters, data analysis results and safety judgment results and is connected with the collection 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 safe operation efficiency of the brushless motor of the Internet of things is effectively improved.
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 technology of the internet of things is an emerging technology, and the internet of things can be understood as an extension of the internet, and the internet of things enables 'things and things' to be accessed to the internet, so that communication between a remote end and a terminal of the internet of things is achieved, and control, information sharing and the like of the things are achieved. Compared with the internet era, the internet of things has more various communication protocols, the fragmentation of objects is very serious, the importance of the gateway is highlighted, and the internet of things gateway can integrate the information collected by different objects and transmit the information to the next level, so that the information can be transmitted among all the parts. With the rise of the internet of things technology, the gradually-increased internet of things terminal devices 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 needed.
In the prior art, the brushless motor controlled through the internet of things cannot accurately control the running state of the brushless motor when the brushless motor runs mostly, 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 the fact that safety monitoring cannot be carried out 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 operating parameters and the environmental parameters of the brushless motor in real time;
an analysis module, which is used for analyzing the data of the operation parameters and the environmental parameters and is connected with the acquisition module, an operation state coefficient H and a calculation formula thereof are arranged in the analysis module, the calculation factors of the operation state coefficient H comprise motor frequency, motor temperature and motor operation time, the analysis module is also used for determining the value of the frequency influence coefficient according to the motor frequency A of the brushless motor acquired in real time and adjusting the value of the frequency influence coefficient according to the historical operation maximum frequency Aa, the analysis module is also used for determining the value of the temperature influence coefficient according to the motor temperature C of the brushless motor acquired in real time and adjusting the value of the temperature influence coefficient according to the historical operation maximum temperature Ca, and the analysis module is also used for correcting the calculated operation state coefficient H according to the environmental humidity G of the working environment, compensating the corrected running state coefficient according to the environmental 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 analyzing 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 real-time collected operation parameters and environment parameters, data analysis results and safety judgment results and is connected with the collection 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 and sets H to M + N + Ta/T when analyzing data, wherein M is a frequency influence coefficient, N is a temperature influence coefficient, Ta is a motor operation time, and T is a preset motor operation period, the analysis module compares a motor frequency a of the brushless motor collected in real time with each preset motor frequency when setting a value of the frequency influence coefficient, and sets a value of the frequency influence coefficient according to a comparison result, wherein,
when A is less than A1, the analysis module judges that the motor frequency is low, and sets the value of the frequency influence coefficient as M2, and sets M2 to M1+ M1 x (A1-A)/A, wherein when A is less than Amin, the control module blocks the operation of the brushless motor, and Amin is a preset minimum motor frequency;
when A is not less than A1 and not more than A2, the analysis module judges that the motor frequency is normal, and the value of the frequency influence coefficient is set as M1, and M1 is a preset value;
when A2 is less than A, the analysis module judges that the motor frequency is high, the value of the frequency influence coefficient is set as M3, and M3 is set as M1+ M1 x (A-A2)/A2, wherein when A is greater than Amax, the control module blocks the operation of the brushless motor, and Amax is a preset maximum motor frequency;
wherein A1 is the first preset motor frequency, A2 is the second preset motor frequency, A1 is less than A2.
Further, when adjusting the frequency influence coefficient, the analysis module obtains the maximum historical operating frequency Aa of the brushless motor, compares the maximum historical operating 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 to 1 and 2,
when A is less than or equal to Aa, the analysis module does not adjust;
when A is larger than Aa, the analysis module adjusts the value of the frequency influence coefficient to Mi ', and Mi' ═ Mi + Mix (A-Aa)/A is set.
Further, when setting the value of the temperature influence coefficient, the analysis module compares the motor temperature C of the brushless motor collected in real time with a preset standard motor temperature C0, and sets the value of the temperature influence coefficient according to the comparison result, wherein,
when C is less than or equal to C0, the analysis module judges that the temperature of the motor is normal, and the value of the temperature influence coefficient is set to be N1;
when C is greater than C0 and less than or equal to Cmax, the analysis module judges that the temperature of the motor is higher, and sets the value of the temperature influence coefficient to be N2, sets N2 to be N1+ N1 x (C-C0)/C0, and the Cmax is the preset maximum motor temperature;
when C is larger than Cmax, the analysis module judges that the temperature of the motor is too high, and the control module stops the operation of the brushless motor.
Further, when the analysis module adjusts the temperature influence coefficient, the analysis module further obtains a historical operating highest temperature Ca of the brushless motor, compares the 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, and sets i to 1, 2, wherein,
when C is less than or equal to Ca, the analysis module does not adjust;
when C is larger than 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 operation state coefficient H is calculated, the analysis module obtains the environmental humidity G of the brushless motor working environment, compares the environmental humidity G with the preset environmental humidity G0, and corrects the calculated operation state coefficient H according to the comparison result, wherein,
when G is less than or equal to G0, the analysis module judges that the environmental humidity is normal and does not perform correction;
when G > G0, the analysis module corrects the operating condition coefficient to H ', sets H' ═ H + hx (G-G0)/G.
Further, after the operation state coefficient H is corrected, the analysis module obtains the environmental air pressure K of the brushless motor working environment, compares the environmental air pressure K with a preset environmental air pressure K0, and compensates the corrected operation state coefficient according to the comparison result, wherein,
when K < K0, the analysis module compensates the operating condition coefficient to H ", setting H" ═ H '+ H' × (K0-K)/K0;
and when K is larger than or equal to K0, the analysis module judges that the ambient air pressure is normal and does not perform compensation.
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, 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 that the brushless motor runs to a period T;
when H1 is greater than H and less than or equal to H2, the judging module judges that the running state of the brushless motor has 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;
h1 is a first preset operation state coefficient, H2 is a second preset operation 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 operation period of the shortened brushless motor to T ', sets T ' ═ T × (H ″ -H1)/H ", and when T ' < Tmin, sets T ' ═ Tmin, where Tmin is a preset minimum operation period, compares the operation period T ' of the shortened brushless motor with the motor operation time Ta, and controls the operation state of the motor according to the comparison result, wherein,
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 to 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 invention has the advantages that when the analysis module analyzes data, the operation safety of the brushless motor is judged by the judgment module through setting the operation state coefficient H, so that the brushless motor is monitored in real time, the operation efficiency of the brushless motor is improved, the operation safety is improved, 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 frequency influence coefficient is within a preset range, the analysis module selects a fixed value as the frequency influence coefficient, otherwise, the frequency influence coefficient is calculated through a difference value, so as to ensure the accuracy of the value of the frequency influence coefficient, and the analysis module accurately controls the value of the frequency influence coefficient, the accuracy of calculation of the running state coefficient H is improved, so that the safety monitoring efficiency of the brushless motor is improved, the running efficiency of the brushless motor is further improved, and when the motor frequency A is smaller than a minimum value or larger than a maximum value, the control module timely blocks the running of the brushless motor, the running safety of the motor is guaranteed, and the running efficiency of the brushless motor is improved.
Particularly, after the value of the frequency influence coefficient is determined, the analysis module adjusts the frequency influence coefficient according to the maximum frequency Aa of the brushless motor in historical operation, and the analysis module adjusts the frequency influence coefficient according to historical operation data of the brushless motor, so that the accuracy of the value of the frequency influence coefficient can be effectively improved, the calculation accuracy 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 sets the value of the temperature influence coefficient, the analysis module compares the motor temperature C of the brushless motor collected in real time 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 analysis module calculates according to the difference between the motor temperature C and the preset value to ensure the accuracy of the value of the temperature influence coefficient, thereby improving the calculation accuracy 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 further adjusting the temperature influence coefficient according to the highest temperature Ca of the brushless motor in historical operation, and effectively improving the accuracy of the value of the temperature influence coefficient by adjusting the temperature influence coefficient according to the historical operation data of the brushless motor, therefore, the calculation accuracy of the running state coefficient H is further ensured, the safety monitoring efficiency of the brushless motor is improved, and the running efficiency of the brushless motor is further improved.
In particular, after the analysis module completes the calculation of the operation state coefficient H, the analysis module corrects the operation state coefficient H according to the environmental humidity G of the brushless motor working environment, the influence of the environmental humidity on the motor operation state is reduced through correction, if the environmental humidity G is larger than a preset value, the corrected operation state coefficient is calculated according to the difference value between the environmental humidity G and the preset value, the calculation accuracy of the operation state coefficient H is improved, the safety monitoring efficiency of the brushless motor is improved, the operation efficiency of the brushless motor is further improved, after the correction is completed, the analysis module compensates the operation state coefficient according to the environmental air pressure K of the brushless motor working environment, the influence of the environmental air pressure on the motor operation state is reduced through compensation, if the environmental air pressure K is smaller than the preset value, the compensated operation state coefficient is calculated according to the difference value between the environmental air pressure K and the preset value, the accuracy of calculation of the running state coefficient H is improved, so that the safety monitoring efficiency of the brushless motor is improved, and the running efficiency of the brushless motor is further improved.
In particular, when the judging module judges the operation safety of the brushless motor, the control module compares the compensated operation state coefficient H ' with a preset value to enable the control module to adjust the operation state of the brushless motor in time 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 operation state is judged to be normal and not adjusted, if the operation safety risk is proved to exist between the preset values, the failure risk of the brushless motor is reduced by reducing the operation period, if the operation period is larger than the preset value, the operation of the brushless motor is directly blocked so as to ensure the operation safety of the brushless motor, meanwhile, when the operation period is reduced, the reduced operation period is calculated according to the difference value of the operation state coefficient H ' and the preset value, the operation safety of the brushless motor is ensured, meanwhile, the shortened operation period T' of the brushless motor is compared with the operation time Ta of the motor, and the operation of the brushless motor is blocked in time, so that the operation safety of the brushless motor is further improved, and the operation efficiency of the brushless motor is improved.
Drawings
Fig. 1 is a schematic structural diagram of a cloud-controlled intelligent brushless motor driving system according to the embodiment.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described below with reference to examples; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit 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 only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, it is a schematic structural diagram of a cloud-controlled intelligent brushless motor driving system according to the present embodiment, 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 environmental 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 real-time collected operation parameters and environment parameters, data analysis results and safety judgment results and is connected with the collection module, the analysis module and the judgment module;
and the control module is used for controlling the operation process of the motor according to the safety judgment result and is connected with the judgment module.
Specifically, in this embodiment, the brushless motor is connected to the cloud through the gateway access network, and the driving system analyzes the operation data and the historical data of the brushless motor accessed to the network, so as to monitor the safety of the operation of the brushless motor in real time, thereby improving the safe operation efficiency of the brushless motor.
Specifically, the analysis module is provided with an operation state coefficient H and sets H to M + N + Ta/T when performing data analysis, where M is a frequency influence coefficient, N is a temperature influence coefficient, Ta is a motor operation time, and T is a preset motor operation period, and when setting a value of the frequency influence coefficient, the analysis module compares a motor frequency a of the brushless motor collected in real time with each preset motor frequency and sets the value of the frequency influence coefficient according to a comparison result, where,
when A is less than A1, the analysis module judges that the motor frequency is low, and sets the value of the frequency influence coefficient as M2, and sets M2 to M1+ M1 x (A1-A)/A, wherein when A is less than Amin, the control module blocks the operation of the brushless motor, and Amin is a preset minimum motor frequency;
when A is not less than A1 and not more than A2, the analysis module judges that the motor frequency is normal, and the value of the frequency influence coefficient is set as M1, and M1 is a preset value;
when A2 is less than A, the analysis module judges that the motor frequency is high, the value of the frequency influence coefficient is set as M3, and M3 is set as M1+ M1 x (A-A2)/A2, wherein when A is greater than Amax, the control module blocks the operation of the brushless motor, and Amax is a preset maximum motor frequency;
wherein A1 is the first preset motor frequency, A2 is the second preset motor frequency, A1 is less than A2.
Specifically, in this embodiment, when the analysis module performs data analysis, the operation state coefficient H is set to enable the determination module to determine the operation safety of the brushless motor, so as to monitor the brushless motor in real time, thereby improving the operation efficiency of the brushless motor and improving the operation safety, the calculation result of the operation state coefficient H is affected 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 collected in real time with a preset value, if the value is within a preset range, the analysis module selects a fixed value as the frequency influence coefficient, otherwise, the analysis module performs calculation 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 to improve the accuracy of the calculation of the operation state coefficient H, therefore, the safety monitoring efficiency of the brushless motor is improved, the operating efficiency of the brushless motor is further improved, and when the motor frequency A is smaller than the minimum value or larger than the maximum value, the control module timely blocks the operation of the brushless motor to ensure the safety of the operation of the motor, and the operating efficiency of the brushless motor is improved.
Specifically, when the operation state coefficient H is calculated, the analysis module obtains the maximum historical operation frequency Aa of the brushless motor, compares the maximum historical operation 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 to 1 and 2,
when A is less than or equal to Aa, the analysis module does not adjust;
when A is larger than Aa, the analysis module adjusts the value of the frequency influence coefficient to Mi ', and Mi' ═ Mi + Mix (A-Aa)/A is set.
Specifically, in this embodiment, the analysis module further adjusts the frequency influence coefficient according to the maximum frequency Aa of the brushless motor in the historical operation after determining the value of the frequency influence coefficient, and the analysis module adjusts the frequency influence coefficient according to the historical operation data of the brushless motor, so that the accuracy of the value of the frequency influence coefficient can be effectively improved, 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.
Specifically, when setting the value of the temperature influence coefficient, the analysis module compares the motor temperature C of the brushless motor collected in real time with a preset standard motor temperature C0, and sets the value of the temperature influence coefficient according to the comparison result, wherein,
when C is less than or equal to C0, the analysis module judges that the temperature of the motor is normal, and the value of the temperature influence coefficient is set to be N1;
when C is greater than C0 and less than or equal to Cmax, the analysis module judges that the temperature of the motor is higher, and sets the value of the temperature influence coefficient to be N2, sets N2 to be N1+ N1 x (C-C0)/C0, and the Cmax is the preset maximum motor temperature;
when C is larger than Cmax, the analysis module judges that the temperature of the motor is too high, and the control module stops the operation of the brushless motor.
Specifically, when the operation state coefficient H is calculated, the analysis module further obtains a historical operation highest temperature Ca of the brushless motor, compares the historical operation 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, and sets i to 1, 2, wherein,
when C is less than or equal to Ca, the analysis module does not adjust;
when C is larger than Ca, the analysis module adjusts the value of the temperature influence coefficient to Ni', and sets Ni ═ Ni + Ni × (C-Ca)/C.
Specifically, in the embodiment, when setting the value of the temperature influence coefficient, the analysis module compares the motor temperature C of the brushless motor collected in real time with a preset value to determine the value of the temperature influence coefficient, and if the motor temperature C is within the preset value, a fixed value is used as the temperature influence coefficient, and if the motor temperature C is greater than the preset value, the analysis module performs calculation according to a difference between the motor temperature C and the preset value to ensure accuracy of the value of the temperature influence coefficient, so as to improve accuracy of calculation of the operating state coefficient H, thereby improving safety monitoring efficiency of the brushless motor, and further improving operating efficiency of the brushless motor, and further adjusts the temperature influence coefficient according to the highest temperature Ca of the brushless motor in historical operation, and adjusts the temperature influence coefficient according to historical operating data of the brushless motor, the accuracy of the value of the temperature influence coefficient can be effectively improved, so that the calculation accuracy of the running state coefficient H is further ensured, the safety monitoring efficiency of the brushless motor is improved, and the running efficiency of the brushless motor is further improved.
Specifically, after the operation state coefficient H is calculated, the analysis module obtains the environmental humidity G of the brushless motor working environment, compares the environmental humidity G with the preset environmental humidity G0, and corrects the calculated operation state coefficient H according to the comparison result, wherein,
when G is less than or equal to G0, the analysis module judges that the environmental humidity is normal and does not perform correction;
when G > G0, the analysis module corrects the operating condition coefficient to H ', sets H' ═ H + hx (G-G0)/G.
Specifically, after the operation state coefficient H is corrected, the analysis module obtains the environmental air pressure K of the brushless motor working environment, compares the environmental air pressure K with a preset environmental air pressure K0, and compensates the corrected operation state coefficient according to the comparison result, wherein,
when K < K0, the analysis module compensates the operating condition coefficient to H ", setting H" ═ H '+ H' × (K0-K)/K0;
and when K is larger than or equal to K0, the analysis module judges that the ambient air pressure is normal and does not perform compensation.
Specifically, in this embodiment, the analysis module corrects the operating state coefficient H according to the ambient humidity G of the operating environment of the brushless motor after the operating state coefficient H is calculated, so as to reduce the influence of the ambient humidity on the operating state of the brushless motor through correction, and if the ambient humidity G is greater than the preset value, calculates the corrected operating state coefficient according to the difference between the ambient humidity G and the preset value, so as to improve the accuracy of calculating the operating state coefficient H, thereby improving the safety monitoring efficiency of the brushless motor, so as to further improve the operating efficiency of the brushless motor, and after the correction is completed, the analysis module further compensates the operating state coefficient according to the ambient air pressure K of the operating environment of the brushless motor, so as to reduce the influence of the ambient air pressure on the operating state of the brushless motor through compensation, and if the ambient air pressure K is less than the preset value, calculates the compensated operating state coefficient according to the difference between the ambient air pressure K and the preset value, the accuracy of calculation of the running state coefficient H is improved, so that the safety monitoring efficiency of the brushless motor is improved, and the running efficiency of the brushless motor is further improved.
Specifically, after the analysis module completes compensation of the operation state coefficient, the judgment module compares the compensated operation state coefficient H ″ with each preset operation state coefficient, and judges the operation safety of the brushless motor according to the comparison result, 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 that the brushless motor runs to a period T;
when H1 is greater than H and less than or equal to H2, the judging module judges that the running state of the brushless motor has 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;
h1 is a first preset operation state coefficient, H2 is a second preset operation 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 operation period of the shortened brushless motor as T ', sets T ' ═ T × (H ″ -H1)/H ", and when T ' < Tmin, sets T ' ═ Tmin, where Tmin is a preset minimum operation period, compares the operation period T ' of the shortened brushless motor with an already-operated motor time Ta, and controls the operation state of the motor according to the comparison result, wherein,
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 to a period T'.
Specifically, in the embodiment, when the determining module determines the operation safety of the brushless motor, the control module compares the compensated operation state coefficient H ″ with the preset value to enable the control module to adjust the operation state of the brushless motor in time, 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, it is determined that the operation state is normal without adjustment, if the operation safety risk is proved between the preset values, the operation period is reduced to reduce the failure risk 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 between the operation state coefficient H ″ and the preset value, the operation safety of the brushless motor is ensured, meanwhile, the shortened operation period T' of the brushless motor is compared with the operation time Ta of the motor, and the operation of the brushless motor is blocked in time, so that the operation safety of the brushless motor is further improved, and the operation efficiency of the brushless motor is improved.
So far, the technical solutions of the present invention have 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 the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
Claims (10)
1. A cloud-control intelligent brushless motor driving system is characterized by comprising,
the acquisition module is used for acquiring the operating parameters and the environmental parameters of the brushless motor in real time;
an analysis module, which is used for analyzing the data of the operation parameters and the environmental parameters and is connected with the acquisition module, an operation state coefficient H and a calculation formula thereof are arranged in the analysis module, the calculation factors of the operation state coefficient H comprise motor frequency, motor temperature and motor operation time, the analysis module is also used for determining the value of the frequency influence coefficient according to the motor frequency A of the brushless motor acquired in real time and adjusting the value of the frequency influence coefficient according to the historical operation maximum frequency Aa, the analysis module is also used for determining the value of the temperature influence coefficient according to the motor temperature C of the brushless motor acquired in real time and adjusting the value of the temperature influence coefficient according to the historical operation maximum temperature Ca, and the analysis module is also used for correcting the calculated operation state coefficient H according to the environmental humidity G of the working environment, compensating the corrected running state coefficient according to the environmental 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 analyzing 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 real-time collected operation parameters and environment parameters, data analysis results and safety judgment results and is connected with the collection 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.
2. The cloud-controlled intelligent brushless motor driving system according to claim 1, wherein the analysis module is configured to perform data analysis by setting an operating state coefficient H, where H is M + N + Ta/T, where M is a frequency influence coefficient, N is a temperature influence coefficient, Ta is a motor operated time, and T is a preset motor operating period, and when setting a value of the frequency influence coefficient, the analysis module compares a motor frequency a of the brushless motor collected in real time with each preset motor frequency, and sets a value of the frequency influence coefficient according to a comparison result, where,
when A is less than A1, the analysis module judges that the motor frequency is low, and sets the value of the frequency influence coefficient as M2, and sets M2 to M1+ M1 x (A1-A)/A, wherein when A is less than Amin, the control module blocks the operation of the brushless motor, and Amin is a preset minimum motor frequency;
when A is not less than A1 and not more than A2, the analysis module judges that the motor frequency is normal, and the value of the frequency influence coefficient is set as M1, and M1 is a preset value;
when A2 is less than A, the analysis module judges that the motor frequency is high, the value of the frequency influence coefficient is set as M3, and M3 is set as M1+ M1 x (A-A2)/A2, wherein when A is greater than Amax, the control module blocks the operation of the brushless motor, and Amax is a preset maximum motor frequency;
wherein A1 is the first preset motor frequency, A2 is the second preset motor frequency, A1 is less than A2.
3. The cloud-controlled intelligent brushless motor driving system according to claim 2, wherein the analysis module obtains a maximum frequency Aa of the brushless motor during the adjustment of the frequency influence coefficient, compares the maximum frequency Aa with a motor frequency A detected in real time, and adjusts a value Mi of the frequency influence coefficient according to a comparison result, and sets i to 1, 2,
when A is less than or equal to Aa, the analysis module does not adjust;
when A is larger than Aa, the analysis module adjusts the value of the frequency influence coefficient to Mi ', and Mi' ═ Mi + Mix (A-Aa)/A is set.
4. The cloud-controlled intelligent brushless motor driving system according to claim 2, wherein the analysis module compares the real-time collected motor temperature C of the brushless motor 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, wherein,
when C is less than or equal to C0, the analysis module judges that the temperature of the motor is normal, and the value of the temperature influence coefficient is set to be N1;
when C is greater than C0 and less than or equal to Cmax, the analysis module judges that the temperature of the motor is higher, and sets the value of the temperature influence coefficient to be N2, sets N2 to be N1+ N1 x (C-C0)/C0, and the Cmax is the preset maximum motor temperature;
when C is larger than Cmax, the analysis module judges that the temperature of the motor is too high, and the control module stops the operation of the brushless motor.
5. The cloud-controlled intelligent brushless motor driving system according to claim 4, wherein the analysis module further obtains a historical operating highest temperature Ca of the brushless motor when adjusting the temperature influence coefficient, compares the temperature Ca with a motor temperature C detected in real time, and adjusts a value Ni of the temperature influence coefficient according to a comparison result, and sets i to 1, 2,
when C is less than or equal to Ca, the analysis module does not adjust;
when C is larger than Ca, the analysis module adjusts the value of the temperature influence coefficient to Ni', and sets Ni ═ Ni + Ni × (C-Ca)/C.
6. The cloud-controlled intelligent brushless motor driving system according to claim 2, wherein the analysis module obtains the environmental humidity G of the brushless motor working environment after the operation state coefficient H is calculated, compares the environmental humidity G with a preset environmental humidity G0, and corrects the calculated operation state coefficient H according to the comparison result, wherein,
when G is less than or equal to G0, the analysis module judges that the environmental humidity is normal and does not perform correction;
when G > G0, the analysis module corrects the operating condition coefficient to H ', sets H' ═ H + hx (G-G0)/G.
7. The system according to claim 6, wherein the analysis module obtains an ambient air pressure K of the brushless motor working environment after completing the correction of the operating state coefficient H, compares the ambient air pressure K with a preset ambient air pressure K0, and compensates the corrected operating state coefficient according to the comparison result, wherein,
when K < K0, the analysis module compensates the operating condition coefficient to H ", setting H" ═ H '+ H' × (K0-K)/K0;
and when K is larger than or equal to K0, the analysis module judges that the ambient air pressure is normal and does not perform compensation.
8. The cloud-controlled intelligent brushless motor driving system according to claim 7, wherein the analysis module compares the compensated operating state coefficient H "with each preset operating state coefficient when determining the operating safety of the brushless motor, and determines the operating safety of the brushless motor according to the comparison result, 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 that the brushless motor runs to a period T;
when H1 is greater than H and less than or equal to H2, the judging module judges that the running state of the brushless motor has 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;
h1 is a first preset operation state coefficient, H2 is a second preset operation state coefficient, and H1 is less than H2.
9. The cloud-controlled intelligent brushless motor driving system according to claim 8, wherein the control module sets the operation period of the shortened brushless motor to T ', sets T ' ═ T × (H ″ -H1)/H "when the operation period of the brushless motor is shortened, sets T ' ═ Tmin when T ' < Tmin, Tmin is a preset minimum operation period, 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, wherein,
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 to a period T'.
10. The cloud-controlled intelligent brushless motor drive system of claim 1, wherein the operating parameters include motor frequency and motor temperature, and the environmental parameters include ambient humidity and ambient air pressure.
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