New energy automobile compressor remote regulation and control method based on self-learning

Abstract

The invention relates to the technical field of automobile remote regulation and control, in particular to a new energy automobile compressor remote regulation and control method based on self-learning, and the method comprises the steps: firstly drawing a plan curve according to the required temperature and the temperature regulation duration in historical data, and then periodically obtaining the environment temperature in an automobile; preliminarily analyzing whether the operation of the compressor is qualified or not according to the absolute value of the difference value between the current temperature in the vehicle and the preset temperature in the plan curve, judging that the operation of the compressor is qualified when the absolute value is small, judging that the operation of the compressor is unqualified when the absolute value is large, and further determining a processing mode for the operation parameters of the compressor; the self-learning model is constructed according to the operation parameter data of the compressor, so that self-adaptive adjustment is carried out according to the real-time operation condition of the vehicle, the environment change, the performance degradation of the compressor and the like, and the use convenience and the maintenance efficiency of the vehicle are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile remote control, and in particular to a remote control method for a compressor of a new energy automobile based on self-learning. Background Art

[0002] In the air-conditioning system of new energy vehicles, the compressor is a core component, and its operating status directly affects the vehicle's energy consumption and cooling / heating effects. The existing technology uses components such as charging pile temperature sensors connected to mobile phone app software to allow users to view the ambient temperature outside the car, the ambient temperature inside the car, and set the temperature inside the car through an interface, thereby realizing remote and precise control of the car's air-conditioning system using a mobile phone app. However, traditional compressor control methods are often based on fixed control strategies and are unable to make adaptive adjustments based on the vehicle's real-time operating conditions, environmental changes, and the compressor's own performance degradation. Car owners or maintenance personnel are unable to monitor and adjust the compressor status in a timely manner during vehicle operation, reducing the vehicle's ease of use and maintenance efficiency.

[0003] Chinese patent application number: CN201810108872.8 discloses an energy-saving device and method for remotely starting the air conditioner of an electric vehicle during charging, a charging pile temperature sensor, a receiving and transmitting mainboard connected to a mobile phone app software, the charging pile temperature sensor is connected to the battery management system through the charging pile, the receiving and transmitting mainboards are respectively connected to the battery management system and the in-car temperature sensor, the battery management system and the in-car temperature sensor are both connected to the air conditioning controller, and the air conditioning controller is respectively connected to the PTC heating module and the electric compressor module. When the electric vehicle is charging, the battery management system sends a command to the mobile phone app software to activate the air conditioning controller, and the ambient temperature outside the car and the ambient temperature inside the car can be viewed through the interface, and the temperature inside the car can be set. The present invention realizes that when the electric vehicle is charging, the temperature values ​​inside and outside the car can be monitored in real time, and the air conditioning system of the car can be remotely and accurately controlled using a mobile phone app.

[0004] However, the prior art still has the following problems: Traditional compressor control methods are often based on fixed control strategies and are unable to make adaptive adjustments based on the vehicle's real-time operating conditions, environmental changes, and the compressor's own performance degradation. Vehicle owners or maintenance personnel are unable to monitor and adjust the compressor status in a timely manner during vehicle operation, reducing the vehicle's ease of use and maintenance efficiency. Summary of the invention

[0005] To this end, the present invention provides a remote control method for a new energy vehicle compressor based on self-learning, so as to overcome the problem that the traditional compressor control method in the prior art is often based on a fixed control strategy and cannot be adaptively adjusted according to the real-time operating conditions of the vehicle, environmental changes, and the performance degradation of the compressor itself. The owner or maintenance personnel cannot monitor and adjust the compressor status in time during the operation of the vehicle, which reduces the convenience of use and maintenance efficiency of the vehicle.

[0006] To achieve the above-mentioned purpose, the present invention provides a remote control method for a new energy vehicle compressor based on self-learning. The method comprises: Step S1, receiving instruction information, determining demand information based on the instruction information, and drawing a planning curve according to the demand information, wherein the demand information includes: demand temperature and demand time; Step S2, periodically obtaining the ambient temperature inside the vehicle, substituting the current time node and the measured current temperature and the planned curve into the same coordinate system, determining the preset temperature of the corresponding time node in the planned curve, calculating the absolute value of the difference between the current temperature and the preset temperature, and analyzing whether the operation of the compressor is qualified based on the absolute value. When it is preliminarily determined that the operation of the compressor is unqualified, a secondary determination is made on whether the operation of the compressor is qualified based on the absolute value of the difference between the current temperature and the temperature measured in the previous period, or a processing method for the compressor operation parameters is determined; Step S3, continuously record the operating parameter data of the compressor, take the operating parameter data as input, use a neural network algorithm to output the optimal control parameters of the compressor, and build a compressor performance prediction self-learning model.

[0007] Further, in step S2, analyzing whether the operation of the compressor is qualified based on the absolute value includes: If the absolute value is less than or equal to a first preset absolute value, it is determined that the operation of the compressor is qualified; If the absolute value is greater than the first preset absolute value and less than or equal to the second preset absolute value, it is preliminarily determined that the operation of the compressor is unqualified, and a secondary determination is made on whether the operation of the compressor is qualified based on the absolute value of the difference between the current temperature and the temperature measured in the previous cycle; If the absolute value is greater than the second preset absolute value, it is determined that the operation of the compressor is unqualified, and a processing method for the compressor operation parameters is determined.

[0008] Furthermore, in step S2, a secondary determination is made on whether the operation of the compressor is qualified based on the absolute value of the difference between the current temperature and the temperature measured in the previous cycle, including: Calculate the absolute value of the difference between the current temperature and the temperature measured in the previous cycle to obtain the first deviation. Calculate the absolute value of the difference between the initial ambient temperature in the vehicle and the required temperature to obtain a second deviation. Calculate the ratio of the first deviation to the second deviation to obtain the deviation ratio. If the deviation ratio is greater than or equal to the preset deviation ratio, it is determined that the operation of the compressor is qualified, and the first preset absolute value is adjusted based on the deviation ratio; If the deviation ratio is less than the preset deviation ratio, it is determined that the operation of the compressor is unqualified, and a processing method for the compressor operating parameters is determined.

[0009] Further, adjusting the first preset absolute value based on the deviation ratio includes: Calculating the difference between the deviation ratio and the preset deviation ratio to obtain the deviation ratio difference, The increase range of the first preset absolute value is positively correlated with the deviation ratio difference.

[0010] Furthermore, in step S2, determining a processing method for the compressor operating parameters includes: calculating the ratio of the absolute value to the second preset absolute value, If the ratio is less than or equal to the preset ratio, it is determined to adjust the operating power of the compressor based on the temperature change; If the ratio is greater than the preset ratio, it is determined that the pressure of the compressor is detected.

[0011] Furthermore, when determining to adjust the operating power of the compressor based on the temperature change, the absolute value of the difference between the current temperature and the initial temperature of the environment is calculated, and the increase in the operating power of the compressor is positively correlated with the absolute value of the difference.

[0012] Further, under the condition of determining the pressure of the detected compressor, a processing method for the compressor operating parameters is analyzed based on the measured pressure. If the pressure is greater than or equal to the preset pressure, it is determined that the refrigerant flow rate is adjusted based on the exhaust volume; If the pressure is less than the preset pressure, it is determined to adjust the operating power of the compressor based on the temperature change.

[0013] Furthermore, the refrigerant flow rate is adjusted based on the exhaust volume, wherein the increase amplitude of the refrigerant flow rate is positively correlated with the exhaust volume.

[0014] Compared with the prior art, the beneficial effect of the present invention lies in that, in the present invention, a plan curve is first drawn according to the required temperature and the temperature adjustment time in the historical data, and then the ambient temperature in the vehicle is periodically obtained. The operation of the compressor is preliminarily analyzed based on the absolute value of the difference between the current temperature in the vehicle and the preset temperature in the plan curve. When the absolute value is small, the operation of the compressor is determined to be qualified. When the absolute value is large, the operation of the compressor is determined to be unqualified, and the processing method for the operating parameters of the compressor is further determined, so as to timely adjust the parameters of the compressor, and construct a self-learning model according to the operating parameter data of the compressor, so as to realize adaptive adjustment according to the real-time operating conditions of the vehicle, environmental changes and the performance degradation of the compressor itself, thereby improving the use convenience and maintenance efficiency of the vehicle.

[0015] Furthermore, in the present invention, the absolute value of the difference between the current temperature inside the vehicle and the temperature measured in the previous cycle is first calculated, and then the absolute value of the difference between the initial ambient temperature and the required temperature is determined, and the temperature change is determined according to the ratio of the absolute values ​​of the difference, and then the operation of the compressor is secondary analyzed based on the change of the ambient temperature inside the vehicle to determine whether it is qualified, thereby improving the control accuracy of the ambient temperature change inside the vehicle, and timely adjusting the parameters of the compressor.

[0016] Furthermore, the present invention adjusts the operating parameters of the compressor according to the temperature changes inside the vehicle, and then constructs a self-learning model based on the operating parameter data of the compressor, thereby achieving adaptive adjustment based on the real-time operating conditions of the vehicle, environmental changes, and the performance degradation of the compressor itself, thereby improving the vehicle's ease of use and maintenance efficiency.

[0017] Furthermore, the present invention takes into account that the exhaust volume determines the refrigerant circulation rate, which greatly affects the refrigeration efficiency. Pressure imbalance will cause compressor overload or reduced refrigeration capacity. Therefore, the refrigerant flow rate is adjusted according to the exhaust volume, thereby improving the control accuracy for changes in the internal ambient temperature of the vehicle, and realizing adaptive adjustment based on the real-time operating conditions of the vehicle, environmental changes, and the performance decline of the compressor itself, thereby improving the vehicle's ease of use and maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a remote control method for a new energy vehicle compressor based on self-learning according to the present invention; Figure 2 A flow chart for analyzing whether the operation of the compressor is qualified; Figure 3 A flow chart for determining whether the operation of the compressor is qualified for the second time; Figure 4 A decision flow chart for determining a treatment method for compressor operating parameters. DETAILED DESCRIPTION

[0019] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0020] It should be pointed out that the data in this embodiment are obtained by comprehensive analysis and evaluation of the historical data of the system of the present invention in the six months before this determination and the corresponding historical determination results. It can be understood by those skilled in the art that the determination method of the system of the present invention for a single parameter mentioned above can be to select the value with the highest proportion as the preset standard parameter according to the data distribution, use weighted summation to use the obtained value as the preset standard parameter, substitute each historical data into a specific formula and use the value obtained by the formula as the preset standard parameter or other selection methods, as long as the system of the present invention can clearly define different specific situations in the single determination process through the obtained values.

[0021] The 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 used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0022] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0023] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] See also Figure 1 As shown, it is a flow chart of the remote control method of the new energy vehicle compressor based on self-learning of the present invention.

[0025] The remote control method of a new energy vehicle compressor based on self-learning provided in this embodiment includes: Step S1, receiving instruction information, determining demand information based on the instruction information, and drawing a planning curve according to the demand information, wherein the demand information includes: demand temperature and demand time; Step S2, periodically obtaining the ambient temperature inside the vehicle, substituting the current time node and the measured current temperature and the planned curve into the same coordinate system, determining the preset temperature of the corresponding time node in the planned curve, calculating the absolute value of the difference between the current temperature and the preset temperature, and analyzing whether the operation of the compressor is qualified based on the absolute value. When it is preliminarily determined that the operation of the compressor is unqualified, a secondary determination is made on whether the operation of the compressor is qualified based on the absolute value of the difference between the current temperature and the temperature measured in the previous period, or a processing method for the compressor operation parameters is determined; Step S3, continuously record the operating parameter data of the compressor, take the operating parameter data as input, use a neural network algorithm to output the optimal control parameters of the compressor, and build a compressor performance prediction self-learning model.

[0026] Specifically, in this embodiment, the user or maintenance personnel sends a compressor monitoring request to the cloud server through the user terminal. After receiving the request, the cloud server obtains the instruction information and returns it to the user terminal for display, analyzes the operating status of the compressor, and determines the control parameters for the compressor according to the operating status. The parameters are encrypted by the cloud server and sent to the vehicle-mounted end through the communication network. The communication module of the vehicle-mounted end receives the control instruction, and the self-learning control unit adjusts the control parameters of the compressor drive module according to the instruction, thereby realizing remote control of the compressor operating status. At the same time, the self-learning control unit continues to collect compressor operating data, evaluates the control effect, and uploads new data to the cloud server to further optimize the compressor performance prediction self-learning model.

[0027] Specifically, in this embodiment, the required temperature is the target temperature expected to be reached after adjustment, and the required time is obtained in advance. Data information of several qualified compressors is obtained, and the change data information of the same initial temperature and the required temperature is extracted. The temperature change duration is determined, and the average temperature change duration is solved to obtain the required time. The temperature change data with the temperature change time being the required time is obtained, and the planned curve is drawn using the temperature change data. If there are two or more curves, the average of the temperature values ​​in each curve at a single time node is calculated, and the average is used as the temperature change data for the time node.

[0028] In the present invention, a plan curve is first drawn according to the required temperature and the temperature adjustment time in the historical data, and then the ambient temperature in the vehicle is periodically obtained. The operation of the compressor is preliminarily analyzed based on the absolute value of the difference between the current temperature in the vehicle and the preset temperature in the plan curve. When the absolute value is small, the operation of the compressor is determined to be qualified. When the absolute value is large, the operation of the compressor is determined to be unqualified, and the processing method for the operating parameters of the compressor is further determined, so as to timely adjust the parameters of the compressor. A self-learning model is constructed according to the operating parameter data of the compressor, so as to achieve adaptive adjustment according to the real-time operating conditions of the vehicle, environmental changes and the performance degradation of the compressor itself, thereby improving the use convenience and maintenance efficiency of the vehicle.

[0029] See also Figure 2 As shown, it is a flow chart for analyzing whether the operation of the compressor is qualified.

[0030] Specifically, the step S2 of analyzing whether the operation of the compressor is qualified based on the absolute value includes: If the absolute value is less than or equal to a first preset absolute value, it is determined that the operation of the compressor is qualified; If the absolute value is greater than the first preset absolute value and less than or equal to the second preset absolute value, it is preliminarily determined that the operation of the compressor is unqualified, and a secondary determination is made on whether the operation of the compressor is qualified based on the absolute value of the difference between the current temperature and the temperature measured in the previous cycle; If the absolute value is greater than the second preset absolute value, it is determined that the operation of the compressor is unqualified, and a processing method for the compressor operation parameters is determined.

[0031] Specifically, in this embodiment, the first preset absolute value is selected within the interval [2.5° C., 3° ​​C.], and the second preset absolute value is selected within the interval [7° C., 8° C.].

[0032] See also Figure 3 As shown, it is a flow chart for secondary determination of whether the operation of the compressor is qualified.

[0033] Specifically, in step S2, a secondary determination is made on whether the operation of the compressor is qualified based on the absolute value of the difference between the current temperature and the temperature measured in the previous cycle, including: Calculate the absolute value of the difference between the current temperature and the temperature measured in the previous cycle to obtain the first deviation. Calculate the absolute value of the difference between the initial ambient temperature in the vehicle and the required temperature to obtain a second deviation. Calculate the ratio of the first deviation to the second deviation to obtain the deviation ratio. If the deviation ratio is greater than or equal to the preset deviation ratio, it is determined that the operation of the compressor is qualified, and the first preset absolute value is adjusted based on the deviation ratio; If the deviation ratio is less than the preset deviation ratio, it is determined that the operation of the compressor is unqualified, and a processing method for the compressor operating parameters is determined.

[0034] Specifically, in this embodiment, the preset deviation ratio is selected between the interval [0.2, 0.3].

[0035] In the present invention, the absolute value of the difference between the current temperature inside the vehicle and the temperature measured in the previous cycle is calculated first, and then the absolute value of the difference between the initial ambient temperature and the required temperature is determined. The temperature change is determined according to the ratio of the absolute values ​​of the difference. Then, according to the change of the ambient temperature inside the vehicle, a secondary analysis is performed to determine whether the operation of the compressor is qualified, thereby improving the control accuracy for the change of the ambient temperature inside the vehicle, and thus timely adjusting the parameters of the compressor.

[0036] Specifically, adjusting the first preset absolute value based on the deviation ratio includes: Calculating the difference between the deviation ratio and the preset deviation ratio to obtain the deviation ratio difference, The increase range of the first preset absolute value is positively correlated with the deviation ratio difference.

[0037] In this embodiment, optionally, The deviation ratio difference is compared with the first preset deviation ratio difference and the second preset deviation ratio difference, If the deviation ratio difference is less than or equal to the first preset deviation ratio difference, adjusting the first preset absolute value to 1.05 times the initial first preset absolute value; If the deviation ratio difference is greater than the first preset deviation ratio difference and less than or equal to the second preset deviation ratio difference, adjusting the first preset absolute value to 1.1 times the initial first preset absolute value; If the deviation ratio difference is greater than the second preset deviation ratio difference, adjusting the first preset absolute value to 1.15 times the initial first preset absolute value; The first preset deviation ratio difference is 0.15 times the preset deviation ratio, and the second preset deviation ratio difference is 0.2 times the preset deviation ratio.

[0038] See also Figure 4 As shown, it is a decision flow chart for determining a processing method for compressor operating parameters.

[0039] Specifically, in step S2, determining a processing method for the compressor operating parameters includes: calculating the ratio of the absolute value to the second preset absolute value, If the ratio is less than or equal to the preset ratio, it is determined to adjust the operating power of the compressor based on the temperature change; If the ratio is greater than the preset ratio, it is determined that the pressure of the compressor is detected.

[0040] Specifically, in this embodiment, the preset ratio is selected in the interval [1.15, 1.3].

[0041] In the present invention, the operating parameters of the compressor are adjusted according to the temperature changes inside the vehicle, and then a self-learning model is constructed according to the operating parameter data of the compressor, so as to realize adaptive adjustment according to the real-time operating conditions of the vehicle, environmental changes and the performance degradation of the compressor itself, thereby improving the convenience of use and maintenance efficiency of the vehicle.

[0042] Specifically, when determining to adjust the operating power of the compressor based on the temperature change, the absolute value of the difference between the current temperature and the initial temperature of the environment is calculated, and the increase in the operating power of the compressor is positively correlated with the absolute value of the difference.

[0043] In this embodiment, optionally, The absolute value of the difference is compared with the first preset absolute value of the difference and the second preset absolute value of the difference, If the absolute value of the difference is less than or equal to the first preset absolute value of the difference, adjusting the operating power of the compressor to 1.1 times the initial operating power; If the absolute value of the difference is greater than the first preset absolute value of the difference and less than or equal to the second preset absolute value of the difference, adjusting the operating power of the compressor to 1.15 times the initial operating power; If the absolute value of the difference is greater than the second preset absolute value of the difference, adjusting the operating power of the compressor to 1.25 times the initial operating power; The absolute value of the first preset difference is 0.2 to 0.25 times of the current temperature, and the absolute value of the second preset difference is 0.3 to 0.35 times of the current temperature.

[0044] Specifically, under the condition of determining the pressure of the detected compressor, the processing method for the compressor operating parameters is analyzed based on the measured pressure. If the pressure is greater than or equal to the preset pressure, it is determined that the refrigerant flow rate is adjusted based on the exhaust volume; If the pressure is less than the preset pressure, it is determined to adjust the operating power of the compressor based on the temperature change.

[0045] Specifically, in this embodiment, the preset pressure is obtained by pre-measurement, and the operating parameters of several compressors with qualified operating conditions are obtained to obtain sample data. The operating parameters of the current compressor are compared with the sample data of each compressor, and the pressure of the compressor with the highest data similarity is recorded as the preset pressure.

[0046] Specifically, the refrigerant flow rate is adjusted based on the exhaust volume, wherein the increase in the refrigerant flow rate is positively correlated with the exhaust volume.

[0047] In this embodiment, optionally, The exhaust volume is compared with the first preset exhaust volume and the second preset exhaust volume, If the exhaust volume is less than or equal to the first preset exhaust volume, the refrigerant flow rate is increased to 1.2 times the initial flow rate; If the exhaust volume is greater than the first preset exhaust volume and less than or equal to the second preset exhaust volume, increasing the refrigerant flow rate to 1.25 times the initial flow rate; If the exhaust volume is greater than the second preset exhaust volume, increasing the refrigerant flow rate to 1.3 times the initial flow rate; Among them, the first preset exhaust volume and the second preset exhaust volume are obtained in advance, and the operating data of several compressors of the same model in qualified operating status are obtained, the exhaust volume data of the same temperature change are obtained, and the mean exhaust volume is solved. The first preset exhaust volume is 0.95~1.05 times the mean exhaust volume, and the second preset exhaust volume is 1.15~1.2 times the mean exhaust volume.

[0048] The present invention takes into account that the exhaust volume determines the refrigerant circulation rate and greatly affects the refrigeration efficiency. Pressure imbalance will cause compressor overload or reduced refrigeration capacity. Therefore, the refrigerant flow rate is adjusted according to the exhaust volume, thereby improving the control accuracy for changes in the internal ambient temperature of the vehicle, and realizing adaptive adjustment according to the real-time operating conditions of the vehicle, environmental changes, and the performance decline of the compressor itself, thereby improving the vehicle's ease of use and maintenance efficiency.

[0049] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0050] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A remote control method for a new energy vehicle compressor based on self-learning, characterized in that: include: Step S1, receiving instruction information, determining demand information based on the instruction information, and drawing a planning curve according to the demand information, wherein the demand information includes: demand temperature and demand time; Step S2, periodically obtaining the ambient temperature inside the vehicle, substituting the current time node and the measured current temperature and the planned curve into the same coordinate system, determining the preset temperature of the corresponding time node in the planned curve, calculating the absolute value of the difference between the current temperature and the preset temperature, and analyzing whether the operation of the compressor is qualified based on the absolute value. When it is preliminarily determined that the operation of the compressor is unqualified, a secondary determination is made on whether the operation of the compressor is qualified based on the absolute value of the difference between the current temperature and the temperature measured in the previous period, or a processing method for the compressor operation parameters is determined; Step S3, continuously record the operating parameter data of the compressor, take the operating parameter data as input, use a neural network algorithm to output the optimal control parameters of the compressor, and build a compressor performance prediction self-learning model.

2. The remote control method for a new energy vehicle compressor based on self-learning according to claim 1 is characterized in that: The step S2 of analyzing whether the operation of the compressor is qualified based on the absolute value includes: If the absolute value is less than or equal to a first preset absolute value, it is determined that the operation of the compressor is qualified; If the absolute value is greater than the first preset absolute value and less than or equal to the second preset absolute value, it is preliminarily determined that the operation of the compressor is unqualified, and a secondary determination is made on whether the operation of the compressor is qualified based on the absolute value of the difference between the current temperature and the temperature measured in the previous cycle; If the absolute value is greater than the second preset absolute value, it is determined that the operation of the compressor is unqualified, and a processing method for the compressor operation parameters is determined.

3. The remote control method for a new energy vehicle compressor based on self-learning according to claim 1 is characterized in that: In step S2, a secondary determination is made on whether the operation of the compressor is qualified based on the absolute value of the difference between the current temperature and the temperature measured in the previous cycle, including: Calculate the absolute value of the difference between the current temperature and the temperature measured in the previous cycle to obtain the first deviation. Calculate the absolute value of the difference between the initial ambient temperature in the vehicle and the required temperature to obtain a second deviation. Calculate the ratio of the first deviation to the second deviation to obtain the deviation ratio. If the deviation ratio is greater than or equal to the preset deviation ratio, it is determined that the operation of the compressor is qualified, and the first preset absolute value is adjusted based on the deviation ratio; If the deviation ratio is less than the preset deviation ratio, it is determined that the operation of the compressor is unqualified, and a processing method for the compressor operating parameters is determined.

4. The remote control method for a new energy vehicle compressor based on self-learning according to claim 3 is characterized in that: The adjusting the first preset absolute value based on the deviation ratio comprises: Calculating the difference between the deviation ratio and the preset deviation ratio to obtain the deviation ratio difference, The increase range of the first preset absolute value is positively correlated with the deviation ratio difference.

5. The remote control method for a new energy vehicle compressor based on self-learning according to claim 2 is characterized in that: In step S2, determining a processing method for the compressor operating parameters includes: calculating the ratio of the absolute value to the second preset absolute value, If the ratio is less than or equal to the preset ratio, it is determined to adjust the operating power of the compressor based on the temperature change; If the ratio is greater than the preset ratio, it is determined that the pressure of the compressor is detected.

6. The remote control method for a new energy vehicle compressor based on self-learning according to claim 5 is characterized in that: When determining to adjust the operating power of the compressor based on the temperature change, the absolute value of the difference between the current temperature and the initial temperature of the environment is calculated, and the increase in the operating power of the compressor is positively correlated with the absolute value of the difference.

7. The remote control method for a new energy vehicle compressor based on self-learning according to claim 5 is characterized in that: Under the condition of determining the pressure of the detected compressor, the processing method for the compressor operating parameters is analyzed based on the measured pressure. If the pressure is greater than or equal to the preset pressure, it is determined that the refrigerant flow rate is adjusted based on the exhaust volume; If the pressure is less than the preset pressure, it is determined to adjust the operating power of the compressor based on the temperature change.

8. The remote control method for a new energy vehicle compressor based on self-learning according to claim 7 is characterized in that: The refrigerant flow rate is adjusted based on the exhaust volume, wherein the increase range of the refrigerant flow rate is positively correlated with the exhaust volume.

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