Stress control method for air conditioning pipeline, computer program product and electronic device

Abstract

The application provides a stress control method of an air conditioner pipeline, a computer program product and an electronic device. The air conditioner comprises a pipeline structure, and a piezoelectric device is arranged on the pipeline structure. The method comprises the following steps: obtaining an operating condition parameter and an environmental parameter of the air conditioner, and obtaining a current generated by the piezoelectric device; inputting the operating condition parameter and the environmental parameter into a neural network model, so that the neural network model analyzes the operating condition parameter and the environmental parameter to obtain a reverse current required to be applied to the piezoelectric device; determining a difference between the current and the reverse current as a target difference, and determining whether the target difference is less than a current threshold; and in the case that the target difference is less than the current threshold, applying the reverse current to the piezoelectric device. The application solves the problem that it is difficult to simultaneously ensure that the pipeline has high reliability and the user has high comfort in the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of air conditioners, in particular to an air conditioner pipeline stress control method, a computer readable storage medium, a computer program product and an electronic device. BACKGROUND

[0002] The air conditioner pipeline structure is an important component in the air conditioner system, and its stress reliability is also an important factor affecting the reliability of the whole machine. In order to improve the noise comfort of the user, the pipeline structure usually has high flexibility to attenuate the vibration transmitted by the compressor, which also leads to the fact that the modal of the pipeline structure is relatively low frequency and dense, and the operating frequency of the air conditioner compressor is relatively wide, which is prone to cause resonance between the pipeline modal and the compressor structure, resulting in excessive stress of the pipeline, and the risk of pipeline breakage. The traditional control method is to avoid the resonance point by experiment. However, during the actual operation of the air conditioner, the installation environment, environmental cold and hot shock, long-term use aging, external force impact damage and the like will aggravate the damage of the air conditioner pipeline, and there is a great difference from the state in the laboratory, and the shielding frequency point in the laboratory may not cover the problem point in the actual working environment. And when there are many shielding points, the number of compressor operating points is reduced, which may cause a large span of the operating frequency of the compressor, affecting the comfort of the user. SUMMARY

[0003] The main purpose of the present application is to provide an air conditioner pipeline stress control method, a computer readable storage medium, a computer program product and an electronic device, so as to at least solve the problem that it is difficult to simultaneously ensure high pipeline reliability and good user comfort in the prior art.

[0004] In order to achieve the above-mentioned purpose, according to one aspect of the present application, an air conditioner pipeline stress control method is provided, the air conditioner comprising a pipeline structure, a piezoelectric device being arranged on the pipeline structure, the method comprising: acquiring operating condition parameters and environmental parameters of the air conditioner, and acquiring a current generated by the piezoelectric device; inputting the operating condition parameters and the environmental parameters into a neural network model, so that the neural network model analyzes the operating condition parameters and the environmental parameters to obtain a reverse current needed to be applied to the piezoelectric device, the neural network model being trained by using a plurality of groups of data through a neural network, each group of data in the plurality of groups of data comprising: sample operating condition parameters, sample environmental parameters and sample reverse current; determining a difference between the current and the reverse current as a target difference, and determining whether the target difference is less than a current threshold; in the case where the target difference is less than the current threshold, applying the reverse current to the piezoelectric device.

[0005] Optionally, the method further comprises: in a case where the target difference is not less than the current threshold, adjusting the counter current so that a difference between the current and the adjusted counter current is less than the current threshold; and applying the adjusted counter current to the piezoelectric device.

[0006] Optionally, the air conditioner further comprises a compressor and a fan, and the method of obtaining the operating condition parameters and the environmental parameters of the air conditioner comprises: obtaining indoor temperature and outdoor temperature at a current time to obtain the environmental parameters; and obtaining an exhaust temperature, an inner tube temperature and a rotating speed of the fan of the air conditioner at the current time, and obtaining an operating frequency of the compressor at the current time to obtain the operating condition parameters.

[0007] Optionally, before obtaining the operating frequency of the compressor at the current time, the method further comprises: obtaining the indoor temperature and the outdoor temperature of the air conditioner at a previous time to obtain initial environmental parameters, the previous time being before the current time; obtaining the exhaust temperature, the inner tube temperature, the rotating speed of the fan and the operating frequency of the compressor of the air conditioner at the previous time to obtain initial operating condition parameters; determining an optimal operating frequency of the compressor according to a target parameter, the initial environmental parameters and the initial operating condition parameters, the target parameter representing a preset indoor temperature set by a user according to his own demand or preference; and controlling the compressor to operate at the optimal operating frequency.

[0008] Optionally, the method further comprises: in a case where the target difference is less than the current threshold, training the neural network model according to the operating condition parameters, the environmental parameters and the counter current; and in a case where the target difference is not less than the current threshold, training the neural network model according to the operating condition parameters, the environmental parameters and the adjusted counter current.

[0009] Optionally, before obtaining the indoor temperature and the outdoor temperature of the air conditioner at the previous time, the method further comprises: in a case where a start-up instruction is received, controlling the air conditioner to start up.

[0010] Optionally, the method of obtaining the operating condition parameters and the environmental parameters of the air conditioner comprises: obtaining the operating condition parameters and the environmental parameters by using a sensor.

[0011] According to another aspect of the present application, a computer readable storage medium is provided, which comprises a stored program, wherein the computer readable storage medium controls a device where the computer readable storage medium is located to execute any one of the stress control methods of the air conditioner pipeline when the program is executed.

[0012] According to another aspect of this application, a computer program product is provided, including computer instructions that, when executed by a processor, implement any of the stress control methods for the air conditioning pipeline described above.

[0013] According to another aspect of this application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing stress control of any of the described air conditioning ducts.

[0014] Using the technical solution of this application, piezoelectric devices are arranged on the pipeline structure. First, the operating condition parameters and environmental parameters of the air conditioner are obtained, and the current generated by the piezoelectric device is obtained. Then, the operating condition parameters and environmental parameters are input into the neural network model to obtain the reverse current that needs to be applied to the piezoelectric device. Then, the difference between the current and the reverse current is determined to be the target difference value. Finally, when the target difference value is less than the current threshold, the reverse current is applied to the piezoelectric device. Compared to existing technologies that struggle to simultaneously ensure high pipeline reliability and user comfort, this application incorporates piezoelectric devices into the pipeline structure. By real-time monitoring of the air conditioner's operating parameters and environmental parameters, and analyzing these parameters using a neural network model, the reverse current to be applied to the piezoelectric devices is determined. When the target difference is less than the current threshold, it indicates that the current generated by the deformation of the piezoelectric devices minus the reverse current is within a safe range. Consequently, the corresponding pipeline structure is also within a safe range. This means that an appropriate reverse current can be predicted and applied to counteract the current generated by the piezoelectric devices, thereby reducing excessive stress on the pipeline structure caused by resonance or other reasons, lowering the risk of pipeline damage, and ensuring pipeline reliability without changing the compressor frequency. This also avoids the compressor frequency shielding point affecting comfort and performance. Attached Figure Description

[0015] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0016] Figure 1 A hardware structure block diagram of a mobile terminal for performing a stress control method for air conditioning pipes according to an embodiment of this application is shown.

[0017] Figure 2 A schematic flowchart of a stress control method for an air conditioning pipeline according to an embodiment of this application is shown.

[0018] Figure 3 A structural block diagram of a pipeline reliability control module provided according to an embodiment of this application is shown;

[0019] Figure 4 A flowchart of a process of establishing a neural network model is shown according to an embodiment of the present application.

[0020] Figure 5 A structural diagram of a neural network model is shown according to an embodiment of the present application.

[0021] Figure 6 A flowchart of a specific stress control method of an air conditioner pipeline is shown according to an embodiment of the present application.

[0022] Among the above drawings, the following reference signs are included:

[0023] 102, processor; 104, memory; 106, transmission device; 108, input / output device. DETAILED DESCRIPTION

[0024] It should be noted that the embodiments and features of the embodiments in the present application can be combined with each other without conflict. The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0025] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0026] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units need not be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0027] As introduced in the background, it is difficult to simultaneously ensure high pipeline reliability and good user comfort in the prior art. To solve the above problems, the embodiments of the present application provide a stress control method of an air conditioner pipeline, a computer readable storage medium, a computer program product and an electronic device.

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0029] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a stress control method for air conditioning pipes according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0030] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the stress control method for air conditioning pipes in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0031] A stress control method of an air conditioner pipeline running on a mobile terminal, a computer terminal or the like is provided in the embodiment. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical sequence is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0032] Figure 2 is a flowchart of a stress control method of an air conditioner pipeline according to an embodiment of the present application. The air conditioner comprises a pipeline structure, and a piezoelectric device is arranged on the pipeline structure, as shown in Figure 2 The method comprises the following steps:

[0033] In step S201, the operating condition parameters and the environmental parameters of the air conditioner are obtained, and the current generated by the piezoelectric device is obtained.

[0034] In step S202, the operating condition parameters and the environmental parameters are input into a neural network model, so that the neural network model analyzes the operating condition parameters and the environmental parameters to obtain a counter current to be applied to the piezoelectric device. The neural network model is trained by using a plurality of sets of data through neural network learning. Each set of data in the plurality of sets of data comprises sample operating condition parameters, sample environmental parameters and sample counter current.

[0035] In step S203, the difference between the current and the counter current is determined as a target difference, and it is determined whether the target difference is less than a current threshold.

[0036] In step S204, in the case where the target difference is less than the current threshold, the counter current is applied to the piezoelectric device.

[0037] Through the above embodiment, the piezoelectric device is arranged on the pipeline structure, first, the running condition parameters and the environmental parameters of the air conditioner are acquired, and the current generated by the piezoelectric device is acquired, then the running condition parameters and the environmental parameters are input into the neural network model to obtain the counter current required to be applied to the piezoelectric device, and then the difference between the current and the counter current is determined as the target difference, and finally when the target difference is less than the current threshold, the counter current is applied to the piezoelectric device. Compared with the prior art which is difficult to simultaneously ensure high pipeline reliability and good user comfort, the piezoelectric device is arranged on the pipeline structure in the present application, the running condition parameters and the environmental parameters of the air conditioner are monitored in real time, and the neural network model is used to analyze these parameters to obtain the counter current required to be applied to the piezoelectric device. When the target difference is less than the current threshold, it indicates that the value of the current generated by the deformation of the piezoelectric device minus the counter current is within a safe range, and the corresponding pipeline structure is also within the safe range, that is, the appropriate counter current can be predicted and applied to offset the current generated by the piezoelectric device, thereby reducing the excessive stress generated by the pipeline structure due to resonance or other reasons, reducing the risk of pipeline damage, ensuring the reliability of the pipeline without changing the compressor frequency, and avoiding the influence of the compressor frequency shielding point on comfort and performance.

[0038] Specifically, the current threshold is the current threshold corresponding to the piezoelectric device. In actual application, the current threshold can be set according to the experience value, or can be obtained through multiple experiments, and the present application does not make specific limitations thereto.

[0039] In an optional solution, the method further comprises: in the case that the target difference is not less than the current threshold, adjusting the counter current so that the difference between the current and the adjusted counter current is less than the current threshold; and applying the adjusted counter current to the piezoelectric device. In this embodiment, by adjusting the counter current, it is ensured that the difference between the current and the counter current is less than the preset current threshold, and the reliability of the pipeline structure is further ensured.

[0040] According to some example embodiments of the present application, the air conditioner further comprises a compressor and a fan, and the running condition parameters and the environmental parameters of the air conditioner are acquired by: acquiring the indoor temperature and the outdoor temperature at the current time to obtain the environmental parameters; acquiring the exhaust temperature, the inner tube temperature of the air conditioner and the rotating speed of the fan at the current time, and acquiring the operating frequency of the compressor at the current time to obtain the running condition parameters. In this embodiment, by simultaneously acquiring the indoor temperature, the outdoor temperature, the exhaust temperature, the inner tube temperature, the fan rotating speed and the compressor operating frequency, it is ensured that the obtained running condition parameters and environmental parameters are more accurate, providing accurate data support for subsequent counter current.

[0041] In other embodiments, before the current time of the compressor is obtained, the method further comprises: obtaining the indoor temperature and the outdoor temperature of the air conditioner at a previous time, to obtain initial environmental parameters, the previous time being before the current time; obtaining the discharge temperature, the inner tube temperature, the rotational speed of the fan and the operating frequency of the compressor of the air conditioner at the previous time, to obtain initial operating condition parameters; determining the optimal operating frequency of the compressor according to the target parameter, the initial environmental parameters and the initial operating condition parameters, the target parameter representing a preset indoor temperature set by a user according to his own needs or preferences; and controlling the compressor to operate at the optimal operating frequency. In this embodiment, by obtaining the indoor and outdoor temperatures, the discharge temperature, the inner tube temperature, the rotational speed of the fan and the operating frequency of the compressor at the previous time, the current operating state of the air conditioner can be more accurately understood, and the optimal operating frequency of the compressor can be calculated in combination with the target temperature (i.e. the indoor temperature desired by the user) set by the user, so as to meet the user's comfort while improving energy efficiency.

[0042] According to some example embodiments of the present application, the method further comprises: in the case that the target difference is less than the current threshold, training the neural network model according to the operating condition parameters, the environmental parameters and the counter current; and in the case that the target difference is not less than the current threshold, training the neural network model according to the operating condition parameters, the environmental parameters and the adjusted counter current. In this embodiment, by using the operating condition parameters, the environmental parameters and the counter current / adjusted counter current collected during actual operation for training the neural network model, the model can more accurately learn and simulate the relationship between these parameters and the counter current, improve the accuracy of model prediction and control, and more effectively determine the counter current applied to the piezoelectric device as the model is continuously optimized, so as to more accurately control the stress of the air conditioner pipeline and reduce the risk of damage caused by resonance and other problems.

[0043] In some optional solutions of the present application, before the indoor temperature and the outdoor temperature of the air conditioner at the previous time are obtained, the method further comprises: in the case that a start-up instruction is received, controlling the air conditioner to start up. In this embodiment, by starting up the air conditioner before obtaining the parameters, the system can immediately perform accurate temperature control after collecting environmental data, avoiding long-term purposeless operation and thus reducing energy waste.

[0044] In some optional solutions of the present application, the operation condition parameters and the environmental parameters of the air conditioner are obtained by using sensors.

[0045] Specifically, the pipeline reliability control module in the present application includes a sensor module and a data analysis module, as shown in Figure 3 The data analysis module includes a neural network model, and the pipeline reliability control module can send instructions to the controller to regulate the operation parameters (i.e., operation condition parameters) of the air conditioning system.

[0046] Specifically, Figure 4 The establishment process of the neural network model in the present application is shown in the flowchart, Figure 5 The structure of the neural network model in the present application is shown in the schematic diagram. As shown in Figure 4 and Figure 5 The sample current values generated by the pipeline piezoelectric structure (i.e., piezoelectric device) under the corresponding working conditions of different system parameters (indoor temperature, outdoor temperature, exhaust temperature, inner tube temperature, fan speed, compressor operating frequency, etc.) measured in the laboratory are preprocessed as network training data to determine the structure of the neural network, including the number of layers and the number of neurons in each layer. During the actual operation of the air conditioning system, the actual operation data is input into the trained neural network, and the inverse current of the piezoelectric structure is controlled through the output to control the air conditioning pipeline to meet the reliability requirements. At the same time, the actual operating parameters can also be used as data to further train and optimize the model to ensure the accuracy of the control. The input parameters of the neural network model are indoor temperature, outdoor temperature, exhaust temperature, inner tube temperature, fan speed, compressor operating frequency, etc. There is a current preset value (i.e., current threshold) for the current generated by the piezoelectric structure in the system. When the current generated by the piezoelectric structure is less than the current preset value, the pipeline structure is safe. When the current is greater than the current preset value, the pipeline vibration stress is too large, and there is a risk of pipe breakage. At this time, the inverse current of the piezoelectric structure needs to be applied to ensure that the current value on the piezoelectric structure is less than the preset value. The inverse current of the piezoelectric structure is the output parameter of the neural network model.

[0047] In order to enable those skilled in the art to more clearly understand the technical solutions of the present application, the implementation process of the stress control method of the air conditioning pipeline of the present application will be described in detail below with reference to specific embodiments.

[0048] The present embodiment relates to a specific stress control method for an air conditioning pipeline. The air conditioner includes a pipeline structure, and a piezoelectric device is arranged on the pipeline structure, as shown in Figure 6As shown, it includes the following steps:

[0049] Step S1: The user sends a command to power on;

[0050] Step S2: After the sensor is turned on, the sensor module acquires the initial operating condition parameters and initial environmental parameters of the air conditioning system under actual working conditions. At this time, the initial operating condition parameters include parameters such as exhaust temperature, inner pipe temperature, fan speed, and compressor operating frequency, and the initial environmental parameters include indoor temperature and outdoor temperature.

[0051] Step S3: After the sensor module acquires the relevant parameters, it sends the relevant parameters to the controller. The controller determines the optimal compressor operating frequency based on the user settings (i.e., target parameters) and the relevant parameters, and controls the air conditioner to operate at the optimal compressor operating frequency. It also uses the sensor to acquire the operating condition parameters and environmental parameters of the air conditioner at this time, and further sends the operating condition parameters, environmental parameters and the optimal compressor operating frequency to the data analysis module.

[0052] Step S4: The data analysis module uses a neural network model to provide the magnitude of the reverse current that needs to be output to the piezoelectric structure (i.e., the piezoelectric device) at this time;

[0053] Step S5: At this time, the compressor's piezoelectric structure deforms due to pipeline vibration, which in turn causes it to generate current. The difference between the generated current and the reverse current is calculated as the target difference.

[0054] Step S6: Compare the target difference with the preset difference value (i.e., the current threshold);

[0055] Step S7: When the value of the current generated by the deformation of the piezoelectric structure minus the reverse current (i.e., the target difference) is smaller than the preset difference value, it is used as an allowable parameter to enter the neural network model for training. The compressor is then operated at its current operating frequency without any other adjustments.

[0056] Step S8: When the ratio of the current generated by the deformation of the piezoelectric structure (i.e., the target difference) to the reverse current is greater than or equal to the preset difference value, the reverse current output to the piezoelectric structure is further increased, so that the value of the current of the piezoelectric structure minus the reverse current (i.e., the target difference) is less than the preset difference value. At this time, the compressor operates at the predetermined operating frequency without changing the operating frequency of the compressor, which can meet the reliability requirements of the pipeline. Furthermore, the operating condition parameters, environmental parameters, and applied reverse current value at this time are used as allowable parameters to be fed into the neural network model for training. At the same time, the previously input environmental parameters and reverse current parameters are removed to ensure that the neural network model is optimized according to the environmental conditions applied by the user.

[0057] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0058] The embodiment of the present application provides a computer readable storage medium, the computer readable storage medium comprises a stored program, wherein the program controls a device where the computer readable storage medium is located to execute the stress control method of the air conditioner pipeline when the program runs.

[0059] Specifically, the air conditioner comprises a pipeline structure, and a piezoelectric device is arranged on the pipeline structure, and the stress control method of the air conditioner pipeline comprises the following steps.

[0060] In step S201, the operating condition parameters and the environmental parameters of the air conditioner are obtained, and the current generated by the piezoelectric device is obtained.

[0061] In step S202, the operating condition parameters and the environmental parameters are input into a neural network model, so that the neural network model analyzes the operating condition parameters and the environmental parameters to obtain a counter current required to be applied to the piezoelectric device, and the neural network model is trained by using a plurality of groups of data through a neural network, and each group of data in the plurality of groups of data comprises sample operating condition parameters, sample environmental parameters and sample counter current.

[0062] In step S203, a difference between the current and the counter current is determined as a target difference, and whether the target difference is less than a current threshold is determined.

[0063] In step S204, in the case that the target difference is less than the current threshold, the counter current is applied to the piezoelectric device.

[0064] Optionally, the method further comprises: in the case that the target difference is not less than the current threshold, adjusting the counter current so that a difference between the current and the adjusted counter current is less than the current threshold; and applying the adjusted counter current to the piezoelectric device.

[0065] Optionally, the air conditioner further comprises a compressor and a fan, and the operating condition parameters and the environmental parameters of the air conditioner are obtained by: obtaining indoor temperature and outdoor temperature at a current moment to obtain the environmental parameters; obtaining discharge temperature, inner tube temperature of the air conditioner and rotating speed of the fan at the current moment, and obtaining operating frequency of the compressor at the current moment to obtain the operating condition parameters.

[0066] Optionally, before the acquiring the operating frequency of the compressor at the current time, the method further comprises: acquiring the indoor temperature and the outdoor temperature of the air conditioner at a previous time to obtain initial environment parameters, the previous time being before the current time; acquiring the discharge temperature, the inner tube temperature, the rotating speed of the fan and the operating frequency of the compressor of the air conditioner at the previous time to obtain initial operating condition parameters; determining the optimal operating frequency of the compressor according to the target parameter, the initial environment parameters and the initial operating condition parameters, the target parameter representing a preset indoor temperature set by a user according to his own needs or preferences; controlling the compressor to operate at the optimal operating frequency.

[0067] Optionally, the method further comprises: in the case that the target difference is less than the current threshold, training the neural network model according to the operating condition parameters, the environment parameters and the counter current; in the case that the target difference is not less than the current threshold, training the neural network model according to the operating condition parameters, the environment parameters and the adjusted counter current.

[0068] Optionally, before the acquiring the indoor temperature and the outdoor temperature of the air conditioner at the previous time, the method further comprises: in the case that a start-up instruction is received, controlling the air conditioner to start up.

[0069] Optionally, the acquiring the operating condition parameters and the environment parameters of the air conditioner comprises: acquiring the operating condition parameters and the environment parameters by using a sensor.

[0070] The application further provides a computer program product comprising computer instructions, which, when executed by a processor, implement at least the following method steps: step S201, acquiring operating condition parameters and environment parameters of the air conditioner and acquiring a current generated by the piezoelectric device; step S202, inputting the operating condition parameters and the environment parameters into a neural network model, so that the neural network model analyzes the operating condition parameters and the environment parameters to obtain a counter current required to be applied to the piezoelectric device, the neural network model being trained by using a plurality of sets of data through a neural network, each set of data in the plurality of sets of data comprising sample operating condition parameters, sample environment parameters and a sample counter current; step S203, determining a difference between the current and the counter current as a target difference, and determining whether the target difference is less than a current threshold; step S204, in the case that the target difference is less than the current threshold, applying the counter current to the piezoelectric device.

[0071] Optionally, the method further comprises: in a case where the target difference is not less than the current threshold, adjusting the counter current so that a difference between the current and the adjusted counter current is less than the current threshold; and applying the adjusted counter current to the piezoelectric device.

[0072] Optionally, the air conditioner further comprises a compressor and a fan, and the operation condition parameters and the environmental parameters of the air conditioner are obtained by: obtaining indoor temperature and outdoor temperature at a current time to obtain the environmental parameters; obtaining discharge temperature, inner tube temperature of the air conditioner at the current time, and a rotating speed of the fan, and obtaining an operating frequency of the compressor at the current time to obtain the operation condition parameters.

[0073] Optionally, before obtaining the operating frequency of the compressor at the current time, the method further comprises: obtaining the indoor temperature and the outdoor temperature of the air conditioner at a previous time to obtain initial environmental parameters, the previous time being before the current time; obtaining the discharge temperature, the inner tube temperature of the air conditioner at the previous time, the rotating speed of the fan, and the operating frequency of the compressor at the previous time to obtain initial operation condition parameters; determining an optimal operating frequency of the compressor according to a target parameter, the initial environmental parameters, and the initial operation condition parameters, the target parameter representing a preset indoor temperature set by a user according to his / her own demand or preference; and controlling the compressor to operate at the optimal operating frequency.

[0074] Optionally, the method further comprises: in a case where the target difference is less than the current threshold, training the neural network model according to the operation condition parameters, the environmental parameters, and the counter current; and in a case where the target difference is not less than the current threshold, training the neural network model according to the operation condition parameters, the environmental parameters, and the adjusted counter current.

[0075] Optionally, before obtaining the indoor temperature and the outdoor temperature of the air conditioner at the previous time, the method further comprises: in a case where a start-up instruction is received, controlling the air conditioner to start up.

[0076] Optionally, the operation condition parameters and the environmental parameters of the air conditioner are obtained by: using a sensor to obtain the operation condition parameters and the environmental parameters.

[0077] The application also provides an electronic device, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs comprise a program for executing any one of the stress control methods of the air conditioner pipeline.

[0078] It should be apparent to those skilled in the art that the modules or steps of the application described above can be implemented with general computing devices, which can be centralized on a single computing device or distributed across a network of multiple computing devices, which can be implemented with program code executable by a computing device, which can be stored in a storage device for execution by a computing device, and in some cases, the steps shown or described can be performed in a different order than shown, or can be implemented as separate integrated circuit modules, or as a single integrated circuit module, and thus the application is not limited to any particular combination of hardware and software.

[0079] Those skilled in the art will appreciate that embodiments of the application can be provided as a method, system, or computer program product. Accordingly, the application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can be embodied in the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk memory, CD-ROMs, optical storage media, etc.) having computer usable program code embodied thereon.

[0080] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks.

[0081] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks.

[0082] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1

[0083] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0084] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) about which the processor can execute instructions. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, or other memory technologies, CD-ROM, digital versatile disc (DVD), or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information for access by a computing device. In no case does the medium include a transitory signal per se.

[0085] Computer readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology for storing information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carriers.

[0086] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover non-exclusive inclusion, such that processes, methods, articles or devices that include a series of elements not only include those elements, but also include other elements not explicitly listed or inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or device including the element.

[0087] From the above description, it can be seen that the above-mentioned embodiments of the present application achieve the following technical effects:

[0088] ​​In the stress control method of the air conditioner pipeline of the present application, the pipeline structure is arranged with a piezoelectric device. Firstly, the operating condition parameters and environmental parameters of the air conditioner are acquired, and the current generated by the piezoelectric device is acquired. Then, the operating condition parameters and environmental parameters are input into a neural network model to obtain the reverse current required to be applied to the piezoelectric device. Then, the difference between the current and the reverse current is determined as the target difference. Finally, when the target difference is less than the current threshold, the reverse current is applied to the piezoelectric device. Compared with the problem in the prior art that it is difficult to simultaneously ensure high pipeline reliability and good user comfort, the piezoelectric device is arranged on the pipeline structure in the present application. By real-time monitoring of the operating condition parameters and environmental parameters of the air conditioner, and by analyzing these parameters using a neural network model, the reverse current required to be applied to the piezoelectric device is obtained. When the target difference is less than the current threshold, it indicates that the value of the current generated by the deformation of the piezoelectric device minus the reverse current is within a safe range, and the corresponding pipeline structure is also within the safe range. That is, the appropriate reverse current can be predicted and applied to offset the current generated by the piezoelectric device, thereby reducing the excessive stress generated by the pipeline structure due to resonance or other reasons, reducing the risk of pipeline damage, ensuring the reliability of the pipeline under the condition of not changing the compressor frequency, and avoiding the influence of the compressor frequency shielding point on comfort and performance.

[0089] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Those skilled in the art can make various modifications and changes to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A stress control method for air conditioning pipes, characterized in that, The air conditioner includes a piping structure on which piezoelectric devices are arranged, and the method includes: The operating parameters and environmental parameters of the air conditioner are obtained, and the current generated by the piezoelectric device is also obtained. The operating condition parameters and the environmental parameters are input into the neural network model so that the neural network model can analyze the operating condition parameters and the environmental parameters to obtain the reverse current that needs to be applied to the piezoelectric device. The neural network model is trained by learning through a neural network using multiple sets of data. Each set of data includes: sample operating condition parameters, sample environmental parameters, and sample reverse current. The difference between the current and the reverse current is determined to be a target difference, and it is determined whether the target difference is less than a current threshold. When the target difference is less than the current threshold, the reverse current is applied to the piezoelectric device.

2. The stress control method for air conditioning pipelines according to claim 1, characterized in that, The method further includes: If the target difference is not less than the current threshold, the reverse current is adjusted so that the difference between the current and the adjusted reverse current is less than the current threshold. The adjusted reverse current is applied to the piezoelectric device.

3. The stress control method for air conditioning pipelines according to claim 1, characterized in that, The air conditioner also includes a compressor and a fan, and acquires the operating condition parameters and environmental parameters of the air conditioner, including: Obtain the current indoor and outdoor temperatures to obtain the environmental parameters; The operating parameters are obtained by acquiring the exhaust temperature, inner pipe temperature, and fan speed of the air conditioner at the current moment, and the operating frequency of the compressor at the current moment.

4. The stress control method for air conditioning pipelines according to claim 3, characterized in that, Before obtaining the operating frequency of the compressor at the current moment, the method further includes: The indoor temperature and outdoor temperature of the air conditioner at the previous moment are obtained to obtain the initial environmental parameters, wherein the previous moment is before the current moment; The initial operating condition parameters are obtained by acquiring the exhaust temperature, inner pipe temperature, fan speed, and compressor operating frequency of the air conditioner at the previous moment. The optimal operating frequency of the compressor is determined based on the target parameters, the initial environmental parameters, and the initial operating condition parameters. The target parameters represent the preset indoor temperature set by the user according to their own needs or preferences. The compressor is controlled to operate at the optimal operating frequency.

5. The stress control method for air conditioning pipelines according to claim 2, characterized in that, The method further includes: If the target difference is less than the current threshold, the neural network model is trained based on the operating condition parameters, the environmental parameters, and the reverse current. If the target difference is not less than the current threshold, the neural network model is trained based on the operating condition parameters, the environmental parameters, and the adjusted reverse current.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the stress control method for air conditioning pipes as described in any one of claims 1 to 5.

7. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the stress control method for air conditioning pipes as described in any one of claims 1 to 5.

8. An electronic device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including a stress control method for performing an air conditioning duct according to any one of claims 1 to 5.

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