An AI-based motor regulation system and method

Abstract

The present invention discloses an AI-based motor regulation system and method, which relates to the technical field of motor regulation. The system includes: a motor monitoring module, a process monitoring module, a water tank monitoring module, a database, and a regulation module. The real-time motor operation state data is collected by the motor monitoring module; the equipment data and pipeline data at the rear end of the motor are collected by the process monitoring module; the water level height and water quality data of the water tank at the rear end of the motor are collected by the water tank monitoring module; the current motor usage data and the usage data of the equipment at the rear end of the motor are stored in the database; and the regulation module integrates the data, analyzes the requirements of the water treatment system, and adjusts the motor state in real time. The present invention comprehensively considers the motor's own state and the system operation state, conducts refined management on the RO water pump in the water treatment system, effectively reduces the number of motor starts and stops, enhances the linkage of the entire system, and ensures the safety and accuracy of adjusting the motor state.

Description

Technical Field

[0001] The present invention relates to the technical field of motor regulation, and specifically to a motor regulation system and method based on artificial intelligence. Background Art

[0002] Through artificial intelligence, it is possible to control a variety of devices over a wide range, and the data call is basically synchronized. At the same time, with the rise of the water treatment industry in China, the types and quantities of equipment in the water treatment system are becoming more complex. The system often uses artificial intelligence to ensure the safe and stable operation of the system through established programs. For example, in the reverse osmosis membrane filtration part of the water treatment system, the system usually sets the start and stop of the RO water pump according to the water levels in the front and rear water tanks of the RO water pump.

[0003] Since the water consumption of users changes at any time, when it is difficult to determine the consumption rate of the water production of the RO membrane, it may cause the motor in the RO water pump to start and stop frequently, which is extremely harmful to the control period and motor loss, and increases unnecessary safety risks. On the one hand, when setting the established program for the water treatment system, it is difficult to consider the actual condition of the water pump motor, and the regulation and protection of the motor are not refined enough. On the other hand, the system often reaches the established frequency directly when starting the water pump motor to supply water, which may cause problems such as excessive pressure in the subsequent pipeline and RO membrane. When starting the motor at high frequency, it may also cause an excess of water production in the RO part, and the water quality may be reduced due to the long-term non-circulation of the filtered water in the water tank.

[0004] Therefore, people need a motor regulation system and method based on artificial intelligence to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a motor regulation system and method based on artificial intelligence to solve the problems raised in the above background art.

[0006] To solve the above technical problems, the present invention provides the following technical solution: A motor regulation system based on artificial intelligence, the system includes: a motor monitoring module, a process monitoring module, a water tank monitoring module, a database, and a regulation module;

[0007] Collect real-time motor operation state data through the motor monitoring module;

[0008] Collect real-time device data and pipeline data at the rear end of the motor through the process monitoring module;

[0009] Collect real-time water level height and water quality data of the water tank at the rear end of the motor through the water tank monitoring module;

[0010] Store the current motor usage data and the usage data of the equipment at the rear end of the motor through the database;

[0011] Integrate data through the adjustment module, analyze the requirements of the water treatment system, and adjust the motor status in real time.

[0012] Further, the motor monitoring module collects the real-time frequency of the motor, collects the real-time amplitude data of the motor through a vibration sensor, and collects the real-time temperature data of the motor through a temperature sensor. The motor is the RO motor in the RO water pump at the front end of the RO membrane in the water treatment system. The motor monitoring module and the sensors are connected by a shielded control cable.

[0013] Further, the process monitoring module collects the equipment data and pipeline data at the rear end of the motor in real time through a pressure sensor. The equipment data is the pressure difference between the front and rear ends of the RO membrane, and the pipeline data is the pipeline pressure of the pipeline at the rear end of the motor.

[0014] Further, the water tank monitoring module collects the water level height of the water tank through a water level sensor and collects the water quality data of the water tank through a resistivity sensor.

[0015] Further, the motor usage data includes the maximum life and usage duration of the motor. The equipment usage data includes the optimal pressure difference affecting water quality of the RO membrane, the maximum life of the RO membrane, and the water passing duration. Adjusting the motor status includes adjusting the motor frequency and shutting down the motor.

[0016] An AI-based motor adjustment method includes the following steps:

[0017] S1: Collect the real-time motor operation status data, collect the equipment data and pipeline data at the rear end of the motor in real time, and collect the water level height and water quality data of the water tank at the rear end of the motor in real time;

[0018] S2: Monitor the water quality of the water tank, alarm when the water quality of the water tank is poor, analyze the water level height of the water tank, and calculate the change speed of the water level height;

[0019] S3: When the water level height rises, judge the motor frequency decrease requirement according to the water level rise speed. When the decreased motor frequency is lower than the minimum motor frequency, the system shuts down the motor; when the decreased motor frequency is higher than the minimum motor frequency, adjust the motor frequency;

[0020] S4: When the water level height drops, judge the motor frequency increase requirement according to the water level drop speed, comprehensively analyze the motor data, RO membrane data, and pipeline data, and calculate the motor frequency increase value; when the motor is not started, start the motor and raise it to the minimum motor frequency; when the motor has been started, raise the motor frequency according to the motor frequency increase value.

[0021] Further, in step S1, collect the operating state data of the motor: the real-time frequency of the motor is X1, the real-time amplitude data of the motor is Y1, and the real-time temperature data of the motor is Z1; collect the device data at the rear end of the motor in real time: the pressure difference between the front and rear ends of the RO membrane is P1; collect the real-time pipeline data: the pipeline pressure of the pipeline at the rear end of the motor is F1; collect the water quality data in the water tank: the water quality resistivity is R1; collect the real-time water level height in the water tank as H1; use artificial intelligence technology to collect system information and provide a database for adjusting the motor state in real time and accurately.

[0022] Further, in step S2: call the water level height before time t when collecting the real-time water level height H1, denoted as H2, and the water level change speed is V1:

[0023] V1 = (H1 - H2) / t;

[0024] where t is the set time interval for collecting the water level height. When the water quality resistivity R1 in the water tank < R, the system determines that the water quality in the water tank is poor, and R is the minimum water quality requirement required by the user; the reason for using the resistivity value to judge the water quality: Resistivity is the most important requirement for water quality judgment in pure water preparation and wastewater recycling, and is used as the judgment data for the quality of the RO membrane in the RO reverse osmosis filtration process.

[0025] Further, when V1 is greater than 0, the system determines that the water level is rising, and the system enters step S3. According to the water level change speed V1, score the motor frequency reduction requirement B. When V1 ≥ m1, the motor frequency reduction requirement score B = B1; when m1 > V1 ≥ m2, the motor frequency reduction requirement score B = B2; when V1 < m2, the motor frequency reduction requirement B = B3, B1 > B2 > B3, and calculate the motor frequency X1_1 after reducing the frequency:

[0026]

[0027] where a is the minimum frequency of the motor set by the system, m1 is the first threshold for reducing the motor frequency, and m2 is the second threshold for reducing the motor frequency. When the motor frequency X1_1 = 0, the system shuts down the motor. After adjusting the motor state, return to step S2, and calculate the water level change situation in the water tank with the current water level height as the initial water level height. Here, it is considered that starting the motor when the motor frequency is too small may cause waste of resources. When it is predicted that the motor frequency will be too small after reduction, the motor is shut down. When the water production consumption speed is slightly less than the production speed, the water level in the water tank rises slowly, and the system will reduce the motor frequency to ensure the stable operation of the system and also avoid frequent start and stop of the motor.

[0028] Further, when V1 is less than 0, the system determines that the water level is decreasing, and the system enters step S4. The demand Q for reducing the motor frequency is scored according to the water level change speed V1. When |V1| ≥ M1, the demand score Q for increasing the motor frequency is Q = Q1; when M1 > |V1| ≥ M2, the demand score Q for increasing the motor frequency is Q = Q2; when |V1| < M2, the demand Q for increasing the motor frequency is Q = Q3, where M1 is the first threshold for the system to increase the frequency, M2 is the second threshold for the system to increase the frequency, Q1 > Q2 > Q3, and the support degree k1 of the water quality of the water tank for increasing the motor frequency is calculated:

[0029] k1 = (R1 - R) * u1;

[0030] where u1 is the set weight of the water quality of the water tank on the increase of the motor frequency, and the support degree k2 of the real-time amplitude data Y1 of the motor for increasing the motor frequency is calculated:

[0031] k2 = Y2 - u2 * Y1;

[0032] where u2 is the set weight of the real-time amplitude of the motor on the increase of the motor frequency, Y2 is the maximum value of the support degree of the real-time amplitude data Y1 of the motor for increasing the motor frequency, and the support degree k3 of the maximum life n of the RO membrane and the water passing time N of the RO membrane for increasing the motor frequency is calculated:

[0033]

[0034] where u3 is the set weight of the maximum life of the RO membrane and the water passing time of the RO membrane on the increase of the motor frequency, and the inhibition degree K1 of the real-time temperature data Z1 of the motor on the increase of the motor frequency is calculated:

[0035] K1 = U1 * Z1;

[0036] where U1 is the set weight of the real-time temperature of the motor on the increase of the motor frequency, and the inhibition degree K2 of the front and rear end pressure difference P1 of the RO membrane on the increase of the motor frequency is calculated:

[0037] K2 = U2 * (P1 - P);

[0038] where U2 is the set weight of the front and rear end pressure difference of the RO membrane on the increase of the motor frequency, P is the standard pressure difference when the filtration effect of any type of RO membrane is the best, and the inhibition degree K3 of the pipeline pressure F1 at the rear end of the motor on the increase of the motor frequency is calculated:

[0039] K3 = U3 * F1;

[0040] where U3 is the set weight of the pipeline pressure at the rear end of the motor on the increase of the motor frequency. Based on the above data, the influence coefficient G1 on the increase of the motor frequency is calculated:

[0041] G1 = 1 + k1 + k2 + k3 - K1 - K2 - K3;

[0042] Calculate the motor frequency X1_2 after increasing the frequency:

[0043]

[0044] Where A is the maximum motor frequency set by the system. After adjusting the motor state, return to step S2, and calculate the change of the water tank water level with the current water level height as the initial water level height; during the process of adjusting the motor state, it avoids the damage to the RO membrane caused by excessive increase of the motor frequency, and ensures the water production quality by analyzing the pressure difference before and after the RO membrane, so as to reasonably adjust the motor state.

[0045] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: Through artificial intelligence technology, the information of the motor, RO membrane and equipment in the system can be obtained in real time. On the one hand, the present invention takes into account the change of water production, conducts refined management of the motor frequency adjustment, reduces the number of motor starts and stops when it is difficult to determine the consumption speed of the RO membrane water production due to the change of the user's water consumption at any time, and accurately adjusts the motor state to protect the motor safety. On the other hand, the present invention takes into account the state of the motor itself, considers the motor temperature and amplitude, and limits the motor frequency adjustment to protect the motor safety; on the other hand, the present invention takes into account the water tank water quality data, the optimal pressure difference of the RO membrane affecting water quality, the maximum life of the RO membrane and the water passing time of the RO membrane, calculates the motor frequency increase value based on the motor frequency increase demand, enhances the linkage of the entire system, provides more accurate data guarantee for adjusting the motor state, and ensures the safety and accuracy of adjusting the motor state. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, but do not constitute a limitation to the present invention. In the drawings:

[0047] Figure 1 is the structural diagram of a motor adjustment system based on artificial intelligence according to the present invention;

[0048] Figure 2 is the flowchart of a motor adjustment method based on artificial intelligence according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0050] Please refer to Figure 1 and Figure 2, the present invention provides a technical solution: an artificial intelligence-based motor regulation system, the system includes: a motor monitoring module, a process monitoring module, a water tank monitoring module, a database, and a regulation module;

[0051] Collect real-time motor operation status data through the motor monitoring module;

[0052] Collect real-time equipment data and pipeline data at the rear end of the motor through the process monitoring module;

[0053] Collect the water level height and water quality data of the water tank at the rear end of the motor in real time through the water tank monitoring module;

[0054] Store the current motor usage data and the usage data of the equipment at the rear end of the motor through the database;

[0055] Integrate data through the regulation module, analyze the requirements of the water treatment system, and adjust the motor status in real time.

[0056] The motor monitoring module collects the real-time frequency of the motor, collects the real-time amplitude data of the motor through a vibration sensor, and collects the real-time temperature data of the motor through a temperature sensor. The motor is the RO motor in the RO water pump at the front end of the RO membrane in the water treatment system. The motor monitoring module is connected to the sensor through a shielded control cable.

[0057] The process monitoring module collects real-time equipment data and pipeline data at the rear end of the motor through a pressure sensor. The equipment data is the pressure difference between the front and rear ends of the RO membrane, and the pipeline data is the pipeline pressure of the pipeline at the rear end of the motor.

[0058] The water tank monitoring module collects the water tank water level height through a water level sensor and collects the water tank water quality data through a resistivity sensor.

[0059] The motor usage data includes the maximum life and usage duration of the motor. The equipment usage data includes the optimal pressure difference of the RO membrane affecting water quality, the maximum life of the RO membrane, and the water passing duration. Adjusting the motor status includes adjusting the motor frequency and shutting down the motor.

[0060] An artificial intelligence-based motor regulation method includes the following steps:

[0061] S1: Collect real-time motor operation status data, collect real-time equipment data and pipeline data at the rear end of the motor, and collect the water level height and water quality data of the water tank at the rear end of the motor;

[0062] S2: Monitor the water quality of the water tank, alarm when the water quality of the water tank is poor, analyze the water level height of the water tank, and calculate the change speed of the water level height;

[0063] S3: When the water level height rises, judge the demand for motor frequency decrease according to the rising speed of the water level. When the motor frequency after the decrease is lower than the minimum motor frequency, the system turns off the motor; when the motor frequency after the decrease is higher than the minimum motor frequency, adjust the motor frequency.

[0064] S4: When the water level height drops, judge the demand for motor frequency increase according to the dropping speed of the water level, comprehensively analyze the motor data, RO membrane data and pipeline data, and calculate the motor frequency increase value; when the motor is not turned on, then turn on the motor and raise it to the minimum motor frequency; when the motor is already turned on, then raise the motor frequency according to the motor frequency increase value.

[0065] In step S1, collect the motor operation state data: the real-time motor frequency is X1, the real-time motor amplitude data is Y1, and the real-time temperature data of the motor is Z1; collect the equipment data at the rear end of the motor in real time: the pressure difference between the front and rear ends of the RO membrane is P1; collect the real-time pipeline data: the pipeline pressure of the pipeline at the rear end of the motor is F1; collect the water quality data in the water tank: the water quality resistivity is R1; collect the real-time water level height in the water tank as H1.

[0066] In step S2: Call the water level height before time t when collecting the real-time water level height H1, denoted as H2, and the water level change speed is V1:

[0067] V1 = (H1 - H2) / t;

[0068] Where t is the set time interval for collecting the water level height. When the water quality resistivity R1 in the water tank < R, the system judges that the water quality in the water tank is poor, and R is the minimum water quality requirement required by the user.

[0069] When V1 is greater than 0, the system judges that the water level rises, and the system enters step S3. Score the motor frequency decrease demand B according to the water level change speed V1. When V1 ≥ m1, the motor frequency decrease demand score B = B1; when m1 > V1 ≥ m2, the motor frequency decrease demand score B = B2; when V1 < m2, the motor frequency decrease demand B = B3, B1 > B2 > B3, and calculate the motor frequency X1_1 after reducing the frequency.

[0070]

[0071] Where a is the minimum frequency of the motor set by the system, m1 is the first threshold for motor frequency reduction, m2 is the second threshold for motor frequency reduction. When the motor frequency X1_1 = 0, the system turns off the motor. After adjusting the motor state, return to step S2, and calculate the water level change situation in the water tank with the current water level height as the initial water level height.

[0072] When V1 is less than 0, the system determines that the water level is decreasing, and the system enters step S4. The demand Q for reducing the motor frequency is scored according to the water level change speed V1. When |V1| ≥ M1, the demand score Q for increasing the motor frequency is Q = Q1; when M1 > |V1| ≥ M2, the demand score Q for increasing the motor frequency is Q = Q2; when |V1| < M2, the demand Q for increasing the motor frequency is Q = Q3, where M1 is the first threshold for the system to increase the frequency, M2 is the second threshold for the system to increase the frequency, Q1 > Q2 > Q3, and the support degree k1 of the water quality of the water tank for increasing the motor frequency is calculated:

[0073] k1 = (R1 - R) * u1;

[0074] where u1 is the set weight of the water quality of the water tank on the increase of the motor frequency, and the support degree k2 of the real-time amplitude data Y1 of the motor for increasing the motor frequency is calculated:

[0075] k2 = Y2 - u2 * Y1;

[0076] where u2 is the set weight of the real-time amplitude of the motor on the increase of the motor frequency, Y2 is the maximum value of the support degree of the real-time amplitude data Y1 of the motor for increasing the motor frequency, and the support degree k3 of the maximum life n of the RO membrane and the water passing time N of the RO membrane for increasing the motor frequency is calculated:

[0077]

[0078] where u3 is the set weight of the maximum life of the RO membrane and the water passing time of the RO membrane on the increase of the motor frequency, and the suppression degree K1 of the real-time temperature data Z1 of the motor on the increase of the motor frequency is calculated:

[0079] K1 = U1 * Z1;

[0080] where U1 is the set weight of the real-time temperature of the motor on the increase of the motor frequency, and the suppression degree K2 of the front and rear end pressure difference P1 of the RO membrane on the increase of the motor frequency is calculated:

[0081] K2 = U2 * (P1 - P);

[0082] where U2 is the set weight of the front and rear end pressure difference of the RO membrane on the increase of the motor frequency, P is the standard pressure difference when the filtering effect of any type of RO membrane is the best, and the suppression degree K3 of the pipeline pressure F1 at the rear end of the motor on the increase of the motor frequency is calculated:

[0083] K3 = U3 * F1;

[0084] where U3 is the set weight of the pipeline pressure at the rear end of the motor on the increase of the motor frequency. Based on the above data, the influence coefficient G1 on the increase of the motor frequency is calculated:

[0085] G1 = 1 + k1 + k2 + k3 - K1 - K2 - K3;

[0086] Calculate the motor frequency X after increasing the frequency 1_2 :

[0087]

[0088] where A is the maximum motor frequency set by the system. After adjusting the motor state, return to step S2, and calculate the change in the water tank level with the current water level height as the initial water level height.

[0089] Embodiment 1: When the system monitors that V1 = -5 and V1 < 0, the system determines that the water level is decreasing. The system enters step S4 and evaluates the motor frequency reduction requirement Q based on the water level change speed V1. When |V1| ≥ M1, the motor frequency increase requirement score Q = Q1; when M1 > |V1| ≥ M2, the motor frequency increase requirement score Q = Q2; when |V1| < M2, the motor frequency increase requirement Q = Q3, where M1 is the first threshold for the system to increase the frequency, M2 is the second threshold for the system to increase the frequency, where M1 = 15, M2 = 10, M1 = 5. In summary, the motor frequency reduction requirement Q = 10, and calculate the support degree k1 of the water tank water quality for the motor to increase the frequency:

[0090] k = (R1 - R) * 1 = (10 - 8) * 0.1 = 0.2;

[0091] where u1 = 0.1 is the weight of the water tank water quality on the motor frequency increase, and calculate the support degree k2 of the motor real-time amplitude data Y1 for the motor frequency increase:

[0092] k2 = Y2 - u2 * Y1 = 0.3 - 0.1 * 1 = 0.2;

[0093] where u2 = 0.1 is the weight of the motor real-time amplitude on the motor frequency increase, Y2 is the maximum value of the support degree of the motor real-time amplitude data Y1 for the motor frequency increase, and calculate the support degree k3 of the maximum life n of the RO membrane and the water passing duration N of the RO membrane for the motor frequency increase:

[0094]

[0095] where u3 = 0.1 is the weight of the maximum life of the RO membrane and the water passing duration of the RO membrane on the motor frequency increase, and calculate the inhibition degree K1 of the motor real-time temperature data Z1 on the motor frequency increase:

[0096] K1 = U1 * Z1 = 0.005 * 50 = 0.25;

[0097] where U1 = 0.005 is the weight of the motor real-time temperature on the motor frequency increase, and calculate the inhibition degree K2 of the pressure difference P1 between the front and rear ends of the RO membrane on the motor frequency increase:

[0098] K2 = U2 * (P1 - P) = 0.5 * (0.6 - 0.3) = 0.15;

[0099] Where U2 = 0.5 is the weight of the pressure difference between the front and rear ends of the RO membrane on the motor's increased frequency, and P is the standard pressure difference when the filtration effect of any type of RO membrane is the best. Calculate the degree of inhibition K3 of the pipeline pressure F1 at the rear end of the motor on the motor's increased frequency:

[0100] K3 = U3 * F1 = 0.5 * 0.8 = 0.4;

[0101] Where U1 = 0.3 is the weight of the pipeline pressure at the rear end of the motor on the motor's increased frequency. Calculate the motor's increased frequency G1:

[0102] G1 = 1 + k1 + k2 + k3 - K1 - K2 - K3 = 0.8;

[0103] Substitute and calculate the motor frequency X1_2 after the increased frequency:

[0104]

[0105] Obtain X 1_2 = X1 + 8. After adjusting the motor state, return to step S2, and calculate the change in the water tank level with the current water level height as the initial water level height.

[0106] Finally, it should be noted that the above are only preferred examples of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A motor adjustment method based on artificial intelligence, characterized by: The following steps are involved: S1: Collect real-time motor operating status data, real-time equipment data and pipeline data at the rear end of the motor, and real-time water level and water quality data at the rear end of the motor water tank; S2: Monitor the water quality of the water tank, alarm when the water quality is poor, analyze the water level of the water tank, and calculate the speed of change of the water level; S3: When the water level rises, the need to reduce the motor frequency is determined based on the speed of the water level increase. When the motor frequency after the decrease is lower than the minimum motor frequency, the system turns off the motor; when the motor frequency after the decrease is higher than the minimum motor frequency, the motor frequency is adjusted. S4: When the water level drops, the demand for increasing the motor frequency is determined based on the speed of the water level drop. The motor data, RO membrane data, and pipeline data are comprehensively analyzed. The frequency increase influence coefficient is calculated by considering the support degree of the water quality in the water tank for the motor frequency increase, the support degree of the motor real-time amplitude data for the motor frequency increase, the support degree of the maximum life of the RO membrane and the water flow time of the RO membrane for the motor frequency increase, the suppression degree of the motor frequency increase by the real-time temperature data of the motor, the suppression degree of the motor frequency increase by the pressure difference between the front and rear ends of the RO membrane, and the suppression degree of the motor frequency increase by the pipeline pressure of the pipeline behind the motor, so as to obtain the motor frequency increase value. When the motor is not turned on, the motor is turned on to increase the frequency to the minimum motor frequency. When the motor is turned on, the motor frequency is increased according to the motor frequency increase value.

2. The motor adjustment method based on artificial intelligence according to claim 1, characterized in that: In step S1, the motor operating status data is collected: the real-time frequency of the motor is X1, the real-time amplitude data of the motor is Y1, and the real-time temperature data of the motor is Z1; the real-time equipment data at the rear end of the motor is collected: the pressure difference between the front and rear ends of the RO membrane is P1; the real-time pipeline data is collected: the pipeline pressure of the pipeline at the rear end of the motor is F1; the water quality data in the water tank is collected: the water quality resistivity is R1; and the real-time water level height of the water tank is collected as H1.

3. The motor adjustment method based on artificial intelligence according to claim 2, characterized in that: In step S2: the water level before the real-time water level H1 is collected is called, recorded as H2, and the water level change speed is V1: ； Where t is the set time interval for collecting water level height. When the water resistivity R1 in the water tank is less than R, the system determines that the water quality in the water tank is poor. R is the minimum water quality requirement required by the user.

4. The motor adjustment method based on artificial intelligence according to claim 3, characterized in that: When V1 is greater than 0, the system determines that the water level is rising, and the system enters step S3. According to the water level change speed V1, the motor frequency reduction demand B is scored. When V1 ≥ m1, the motor frequency reduction demand score B = B1; when m1 > V1 ≥ m2, the motor frequency reduction demand score B = B2; when V1 < m2, the motor frequency reduction demand B = B3, B1 > B2 > B3, and the motor frequency after the frequency reduction is calculated. 1_1 : ； Where a is the minimum frequency of the motor set by the system, m1 is the first threshold for the motor to reduce the frequency, and m2 is the second threshold for the motor to reduce the frequency. 1_1 =0, the system turns off the motor, adjusts the motor state, and returns to step S2 to calculate the water level change in the water tank with the current water level as the initial water level.

5. The motor adjustment method based on artificial intelligence according to claim 4, characterized in that: When V1 is less than 0, the system determines that the water level has dropped, and the system enters step S4. The motor frequency increase demand Q is scored according to the water level change speed V1. When |V1| ≥ M1, the motor frequency increase demand score Q = Q1; when M1 > |V1| ≥ M2, the motor frequency increase demand score Q = Q2; when |V1| < M2, the motor frequency increase demand Q = Q3, where M1 is the first threshold for the system frequency increase, M2 is the second threshold for the system frequency increase, Q1 > Q2 > Q3, and the water tank water quality support for the motor frequency increase k1 is calculated: ； Where u1 is the weight of the influence of the water quality in the water tank on the motor frequency increase, and the support degree k2 of the motor real-time amplitude data Y1 on the motor frequency increase is calculated: ； Among them, u2 is the influence weight of the set motor real-time amplitude on the motor frequency increase, Y2 is the maximum support degree of the motor real-time amplitude data Y1 for the motor frequency increase, and the support degree k3 of the RO membrane maximum life n and RO membrane water flow time N on the motor frequency increase is calculated: ； Among them, u3 is the weight of the influence of the set maximum life of the RO membrane and the length of time the RO membrane is flowing through the water on the motor frequency increase, and the degree of suppression of the motor frequency increase by the real-time temperature data Z1 of the motor is calculated as K1: ； Where U1 is the influence weight of the set real-time temperature of the motor on the motor frequency increase, and the suppression degree K2 of the motor frequency increase by the pressure difference P1 between the front and rear ends of the RO membrane is calculated: ； Where U2 is the weight of the influence of the pressure difference between the front and rear ends of the RO membrane on the motor frequency increase, P is the standard pressure difference when any type of RO membrane has the best filtration effect, and the degree of suppression of the motor frequency increase by the pipeline pressure F1 at the rear end of the motor is calculated as K3: ； Among them, U3 is the influence weight of the pipeline pressure of the motor rear end pipeline on the motor rising frequency. Based on the above data, the influence coefficient G1 on the motor rising frequency is calculated: ； Calculate the motor frequency X after the frequency is increased 1_2 : ； Where A is the maximum frequency of the motor set by the system. After adjusting the motor state, return to step S2 and calculate the water level change of the water tank with the current water level as the initial water level.

6. An artificial intelligence-based motor adjustment system, wherein the system is applied to the artificial intelligence-based motor adjustment method according to any one of claims 1 to 5, characterized in that: The system includes: a motor monitoring module, a process monitoring module, a water tank monitoring module, a database and a regulation module; Collecting real-time motor operating status data through the motor monitoring module; The process monitoring module collects real-time equipment data and pipeline data of the motor backend; The water tank monitoring module collects real-time water level and water quality data of the water tank at the rear end of the motor; Storing current motor usage data and motor back-end device usage data in the database; The adjustment module integrates data, analyzes water treatment system requirements, and adjusts motor status in real time.

7. The motor adjustment system based on artificial intelligence according to claim 6, characterized in that: The motor monitoring module collects the real-time frequency of the motor, collects the real-time amplitude data of the motor through the vibration sensor, and collects the real-time temperature data of the motor through the temperature sensor. The motor is an RO motor in the RO water pump at the front end of the RO membrane in the water treatment system. The motor monitoring module is connected to the sensor through a shielded control cable.

8. The motor adjustment system based on artificial intelligence according to claim 7, characterized in that: The process monitoring module collects real-time equipment data and pipeline data at the rear end of the motor through a pressure sensor. The equipment data is the pressure difference between the front and rear ends of the RO membrane, and the pipeline data is the pipeline pressure of the pipeline at the rear end of the motor.

9. The motor adjustment system based on artificial intelligence according to claim 8, characterized in that: The water tank monitoring module collects the water level data of the water tank through the water level sensor and collects the water quality data of the water tank through the resistivity sensor.

10. The motor adjustment system based on artificial intelligence according to claim 9, characterized in that: The motor usage data includes the maximum life and usage time of the motor, the equipment usage data includes the optimal pressure difference of the RO membrane affecting water quality, the maximum life and water flow time of the RO membrane, and the adjustment of the motor state includes adjusting the motor frequency and shutting down the motor.

Images