Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the disclosed embodiments. However, it will be apparent to one skilled in the art that the present disclosure may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present disclosure with unnecessary detail.
A device calibration method and apparatus according to embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a scene diagram of an application scene of an embodiment of the present disclosure. The application scenario may include terminal devices 1, 2 and 3, a server 4 and a network 5.
The terminal devices 1, 2 and 3 may be hardware or software. When the terminal devices 1, 2 and 3 are hardware, they may be various electronic devices having a display screen and supporting communication with the server 4, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, etc., and when the terminal devices 1, 2 and 3 are software, they may be installed in the above electronic devices. The terminal devices 1, 2 and 3 may be implemented as a plurality of software or software modules, or as a single software or software module, to which the embodiments of the present disclosure are not limited. Further, various applications, such as a data processing application, an instant messaging tool, social platform software, a search class application, a shopping class application, and the like, may be installed on the terminal devices 1, 2, and 3.
The server 4 may be a server that provides various services, for example, a background server that receives a request transmitted from a terminal device with which communication connection is established, and the background server may perform processing such as receiving and analyzing the request transmitted from the terminal device and generate a processing result. The server 4 may be a server, a server cluster formed by a plurality of servers, or a cloud computing service center, which is not limited in the embodiment of the present disclosure.
It should be noted that, the server 4 may be hardware, or may be software. When the server 4 is hardware, it may be various electronic devices that provide various services to the terminal devices 1, 2, and 3. When the server 4 is software, it may be a plurality of software or software modules providing various services to the terminal devices 1, 2, and 3, or may be a single software or software module providing various services to the terminal devices 1, 2, and 3, which is not limited by the embodiments of the present disclosure.
The network 5 may be a wired network using coaxial cable, twisted pair wire, and optical fiber connection, or may be a wireless network that can implement interconnection of various Communication devices without wiring, for example, bluetooth (Bluetooth), near Field Communication (NFC), infrared (Infrared), etc., which are not limited by the embodiments of the present disclosure.
The user can establish a communication connection with the server 4 via the network 5 through the terminal devices 1,2, and 3 to receive or transmit information or the like. It should be noted that the specific types, numbers and combinations of the terminal devices 1,2 and 3, the server 4 and the network 5 may be adjusted according to the actual requirements of the application scenario, which is not limited by the embodiment of the present disclosure.
Fig. 2 is a flow chart of a device calibration method according to an embodiment of the disclosure. The device calibration method of fig. 2 may be performed by the terminal device or the server of fig. 1. As shown in fig. 2, the device calibration method includes:
S201, acquiring training parameters, and acquiring first radio frequency parameters and environment parameters of target equipment;
s202, calibrating training parameters by using calibration equipment to obtain a calibration result;
S203, performing linear regression processing on the training parameters and the calibration results to obtain regression results, and constructing a simulation model according to the regression results;
s204, extracting a first logic diagram from the simulation model, and optimizing the first logic diagram according to a calibration result;
S205, training a simulation model according to the optimized first logic diagram;
s206, inputting the environmental parameters into the simulation model, and outputting second radio frequency parameters;
S207, when the difference value between the second radio frequency parameter and the first radio frequency parameter is larger than a preset threshold value, calibrating the target equipment based on the second radio frequency parameter and the first radio frequency parameter.
The environment parameters of the target device include parameters of the field environment where the target device is located, parameters of the network environment, and the like. The environment parameter is used to describe an environmental condition of an environment to which the target device belongs. The first radio frequency parameter is a current radio frequency parameter of the target device. The second radio frequency parameters of the target device corresponding to different environmental conditions are different, and the second radio frequency parameters are theoretically optimal radio frequency parameters of the target device under the environmental conditions. The target device can be any device in any field, for example, the target device is a measurement and control field, and can be a mobile communication test instrument, a comprehensive tester, a programmable power supply and the like. And calibrating the target equipment, namely updating the existing first radio frequency parameters of the target equipment into the second radio frequency parameters which are optimal in theory.
The training parameters include a plurality of environmental parameters, each of which has been labeled with its corresponding second radio frequency parameter. The calibration of the training parameters by using the calibration device may be to delete or correct some data that does not meet the preset rule in the training parameters, for example, if the second radio frequency parameters corresponding to a certain environmental parameter are not matched, then the second radio frequency parameters corresponding to a certain environmental parameter need to be marked. The linear regression process may be any fitting method commonly used, such as a least squares fitting method, although the linear regression process may be implemented by some software, such as excel or matlab. And inputting the environment parameter and the second radio frequency parameter into the software by taking the environment parameter as an independent variable and taking the second radio frequency parameter as an independent variable, so that a regression result can be output. If the regression result is a function, then a simulation model is constructed according to the regression result, and the simulation model can be constructed by taking the function as a main body and adding other requirements. Other requirements, such as the need to present the data in an image, then the simulation model needs to add the data to image conversion relationship based on the function. The first logic diagram is the logic expressed by the simulation model, and can be understood as a mapping relation or a functional expression equivalent to the simulation model. The simulation model is constructed based on the regression results, and the first logic diagram is extracted from the simulation model. The first logic diagram may be one to which the above "other requirements" class is added based on the regression results. The first logic diagram is optimized according to the calibration result, and the first logic diagram can be corrected according to the calibration result. Training the simulation model according to the optimized first logic diagram can be understood as updating model parameters of the simulation model by a back propagation method according to the optimized first logic diagram.
According to the embodiment of the disclosure, the calibration of the target equipment can be realized by means of the model, and a calibration instrument is not needed, so that the cost of the calibration equipment is reduced, and meanwhile, the accuracy of the calibration equipment can be improved by improving the accuracy of the model.
According to the technical scheme provided by the embodiment of the disclosure, training parameters are acquired, first radio frequency parameters and environment parameters of target equipment are acquired, calibration equipment is utilized to calibrate the training parameters to obtain calibration results, linear regression processing is conducted on the training parameters and the calibration results to obtain regression results, a simulation model is built according to the regression results, a first logic diagram is extracted from the simulation model, the first logic diagram is optimized according to the calibration results, the simulation model is trained according to the optimized first logic diagram, the environment parameters are input into the simulation model, second radio frequency parameters are output, and when the difference value between the second radio frequency parameters and the first radio frequency parameters is larger than a preset threshold value, the target equipment is calibrated based on the second radio frequency parameters and the first radio frequency parameters. By adopting the technical means, the problems of high calibration cost and low accuracy of the equipment in the prior art are solved, the cost of the calibration equipment is further reduced, and the accuracy of the calibration equipment is improved.
The method comprises the steps of obtaining a regression result by carrying out linear regression on training parameters and a calibration result, constructing a simulation model according to the regression result, constructing a simulation network model by using the simulation model and a neural network model, training the simulation network model by using the training parameters, extracting a second logic diagram from the trained simulation network model, optimizing the second logic diagram according to the calibration result, retraining the simulation network model according to the optimized second logic diagram, inputting environment parameters into the simulation network model, and outputting second radio frequency parameters.
And constructing a simulation network model by using the simulation model and the neural network model, wherein the simulation model can be connected with the neural network model. According to the embodiment of the disclosure, the simulation network model is trained twice, so that the accuracy of the trained model is improved. The second logic diagram is similar to the first logic diagram and optimizing the second logic diagram is similar to optimizing the first logic diagram. Because the simulation model constructed by using the mathematical method is difficult to improve after the accuracy reaches a certain degree, but the neural network model does not have the problem. The neural network model can be trained through a large number of machine learning, so that the accuracy of the model can be improved as much as possible. The disclosed embodiments utilize neural network models to improve the accuracy of simulation models. Specifically, in the simulation network model, the output of the simulation model is an input of the neural network model, and the neural network model is the second radio frequency parameter (although the simulation model is established based on a mapping relationship between the environment parameter and the second radio frequency parameter, in practical application, the output of the simulation model may not be the second radio frequency parameter).
It should be noted that the neural network model may be any of the commonly used neural network models, such as Faster-RCNN. The training methods in this disclosure are all methods similar to deep learning training.
The training method comprises the steps of determining a training data set corresponding to training parameters according to an optimized second logic diagram, training the simulation network model by using the training data set to update parameters of the simulation model in the simulation network model under the condition of freezing the simulation network model in the whole training process, training the simulation network model by using the training data set to update parameters of the neural network model in the simulation network model under the condition of freezing the simulation model in the second round, and training the simulation network model by using the training data set to update parameters of the neural network model in the simulation network model in the third round.
According to the optimized second logic diagram, a training data set corresponding to the training parameters is determined, for example, the second radio frequency parameters corresponding to a certain environmental parameter are not right, and then the correct second radio frequency parameters corresponding to the certain environmental parameter can be marked by using the optimized second logic diagram. The training data set can be regarded as updated training parameters. This step is similar to correcting the second radio frequency parameter for which a certain environmental parameter corresponds in calibrating the training parameter using the calibration device. But because the second logic diagram is extracted from the trained simulated network model, the second logic diagram is more optimal than the calibration device.
And retraining the simulation network model according to the optimized second logic diagram, wherein the retraining comprises three rounds of training. The first training round, in the case of freezing a neural network model, trains the simulation network model using the training dataset, which can be understood as training only the simulation model, the step being used to adjust the parameters of the simulation model, the second training round, in the case of freezing a simulation model, trains the simulation network model using the training dataset, which can be understood as training only the neural network model, the step being the most common training of the neural network model, and the third training round, training the simulation network model using the training dataset, which is training both the simulation model and the neural network model, the step being used to fine tune the parameters of the simulation network model.
After retraining the simulation network model according to the optimized second logic diagram, the method further comprises inputting the environmental parameters into a neural network model in the simulation network model, and outputting second radio frequency parameters.
The simulation model in the embodiment of the present disclosure is only used in retraining, and assists in adjusting parameters of the neural network model (the embodiment of the present disclosure is different from the above embodiment in that the neural network model is used as a main body, the simulation model is used as an auxiliary body, the above embodiment is used as an auxiliary body, and the simulation model is used as a main body, so that the method has the advantage of helping the neural network model to converge as soon as possible.
The method comprises the steps of obtaining training parameters, obtaining first radio frequency parameters and environment parameters of target equipment, training a neural network model by using the training parameters, enabling the neural network model to learn and store corresponding relations between the environment parameters and second radio frequency parameters, calibrating the training parameters by using calibration equipment to obtain a calibration result, extracting a third logic diagram from the neural network model, optimizing the third logic diagram according to the calibration result, retraining the neural network model according to the optimized third logic diagram, inputting the environment parameters into the neural network model, outputting the second radio frequency parameters, and calibrating the target equipment based on the second radio frequency parameters and the first radio frequency parameters when the difference value between the second radio frequency parameters and the first radio frequency parameters is larger than a preset threshold value.
The third logic diagram is similar to the first logic diagram and will not be described again. In the prior art, the radio frequency parameters of the target equipment are calibrated through the calibration equipment, the neural network model is introduced for the first time, and the second radio frequency parameters corresponding to the environmental parameters are determined through the trained neural network model, so that the radio frequency parameters of the target equipment are calibrated.
Before training the neural network model by using the training parameters to enable the neural network model to learn and store the corresponding relation between the environmental parameters and the second radio frequency parameters, the method further comprises the steps of obtaining equipment information of a plurality of equipment of the same type in the same application field as the target equipment, and updating the model parameters of the neural network model according to the equipment information of the plurality of equipment.
If training is performed on the neural network model by directly using the training parameters, the training time may be long, and in order to improve the training efficiency, the embodiment of the disclosure updates the model parameters of the neural network model according to the device information of the plurality of devices before training the neural network model. Updating the model parameters of the neural network model according to the device information of the plurality of devices may be understood as updating the values of the model parameters of the neural network model into the intervals corresponding to the target devices. The equipment information comprises information of application fields of equipment, types of the equipment, operating parameters of the equipment and the like, such as a theoretical interval of operating frequency of the equipment. By the technical means, the convergence rate of the neural network model in training can be improved.
In order to improve the accuracy of the trained neural network model, embodiments of the present disclosure retrain the neural network model. The calibration result is a second parameter value recommended by the calibration device.
In an alternative embodiment, the first radio frequency parameters include a first operating frequency, a first signal-to-noise ratio, a first signal gain, a first transmit signal power, a first transmit signal frequency, and a first s-parameter, and the second radio frequency parameters include a second operating frequency, a second signal-to-noise ratio, a second signal gain, a second transmit signal power, a second transmit signal frequency, and a second s-parameter.
S parameters including characteristic impedance, crosstalk, insertion loss, return loss, etc. For example, the embodiments of the present disclosure may adjust the first s-parameter by adjusting the operating circuit of the target device, thereby calibrating the first rf parameter based on the second rf parameter. The target device can select different circuits during working, and then select the circuit with the second s parameter. The signal gains corresponding to the different circuits are different, and the most suitable circuit can be selected according to the second signal gain. The first transmit signal power may also be adjusted based on the second transmit signal power, etc.
It should be noted that any one of the first radio frequency parameter and the second radio frequency parameter in the embodiments of the present disclosure may correspond to a value of one interval. For example, the second transmit signal power may be an interval of transmit signal power.
In an alternative embodiment, the environment parameters of the target device are acquired, the light wave reflection constraint condition, the light wave scattering constraint condition, the light wave refraction constraint condition and the light wave diffraction constraint condition are established based on the light wave reflection principle, the light wave scattering principle, the light wave refraction principle and the light wave diffraction principle respectively, the light wave propagation energy function is established based on the light wave propagation energy loss minimum as a criterion, the mathematical model is constructed based on the light wave reflection constraint condition, the light wave scattering constraint condition, the light wave refraction constraint condition, the light wave diffraction constraint condition and the light wave propagation energy function, the environment parameters are input into the mathematical model, the second working wavelength of the target device is output, the second working frequency of the target device is determined according to the working wavelength, and when the difference value between the second working frequency and the first working frequency is larger than a preset threshold value, the target device is calibrated based on the second working frequency and the first working frequency.
The second radio frequency parameter comprises a second operating frequency, and the first radio frequency parameter comprises a first operating frequency.
Embodiments of the present disclosure pertain to the idea of mathematical modeling. The propagation capabilities of different wavelengths in an environment vary, including reflection, scattering, refraction, diffraction, etc. In order to enhance the propagation capability of working waves of target equipment in the environment and reduce loss in the propagation process, the embodiment of the disclosure establishes a light wave reflection constraint condition based on a light wave reflection principle, establishes a light wave scattering constraint condition based on a light wave scattering principle, establishes a light wave refraction constraint condition based on a light wave refraction principle, and establishes a light wave diffraction constraint condition based on a light wave diffraction principle. The wavelength of the working wave of the target equipment can be controlled within a certain interval through the constraint condition, and then the second working wavelength of the target equipment is solved from the interval according to the light wave propagation energy function. Since the wavelength and the frequency satisfy certain conditions, the second operating frequency of the target device can be determined according to the operating wavelength. The environmental parameters include all information of the environment in which the target device is located, such as which obscuration objects affect the propagation of the light waves are located in the environment in which the target device is located, and the position and size of the obscuration objects.
In an alternative embodiment, the environment parameters of the target device are acquired, the parameters of at least one of the optimal voltage, the optimal current and the anti-interference information of the target device are determined according to the environment parameters, the optimal transmitting signal power of the target device is determined based on the parameters of at least one of the optimal voltage, the optimal current and the anti-interference information, the second radio frequency parameters comprise the optimal transmitting signal power, the dividing value is obtained by dividing the optimal transmitting signal power by the current transmitting signal power, the optimal signal gain is determined according to the dividing value, the second radio frequency parameters comprise the optimal signal gain, the first radio frequency parameters comprise the current transmitting signal power, and the working circuit of the target device is determined according to the optimal signal gain.
The target device may operate in different environments or with different target device operation, and the allowed transmit signal power of the target device may vary, or the optimal transmit signal power of the target device may vary, taking into account the power consumption of the device. In order to solve the above technical problems, embodiments of the present disclosure determine, through environmental parameters, an optimal voltage, or an optimal current, or anti-interference information of a target device. Since the internal resistance of the target device is known, the optimal transmit signal power of the target device can be determined from the relationship of voltage, current, resistance, and power. The immunity information is used to indicate the immunity capability that the transmit signal power of the target device should have. In some cases, if the target device requires stronger immunity to interference, the transmit signal power of the target device should be increased, and if the target device does not require stronger immunity to interference, the transmit signal power of the target device should be appropriately reduced according to the specific circumstances. The optimal transmit signal power of the target device is also determined based on the immunity information.
The embodiment of the disclosure can provide a plurality of gain circuits for the target equipment, so as to provide a plurality of gains for the target equipment. The division value obtained by dividing the optimal transmission signal power by the current transmission signal power can be understood as the gain required by the target device, that is, the optimal signal gain. The current signal gain is the gain being used by the target device at the current time. After the optimal signal gain, the operating circuit of the target device may be determined based on the optimal signal gain. Determining the operational circuitry of the target device is also a method of calibrating the target device.
Any combination of the above optional solutions may be adopted to form an optional embodiment of the present application, which is not described herein.
The following are device embodiments of the present disclosure that may be used to perform method embodiments of the present disclosure. For details not disclosed in the embodiments of the apparatus of the present disclosure, please refer to the embodiments of the method of the present disclosure.
Fig. 3 is a schematic diagram of a device calibration apparatus provided in an embodiment of the present disclosure. As shown in fig. 3, the device calibration apparatus includes:
an acquisition module 301 configured to acquire training parameters, and acquire first radio frequency parameters and environmental parameters of a target device;
The calibration module 302 is configured to calibrate the training parameters by using a calibration device to obtain a calibration result;
the construction module 303 is configured to perform linear regression processing on the training parameters and the calibration result to obtain a regression result, and construct a simulation model according to the regression result;
the extracting module 304 is configured to extract the first logic diagram from the simulation model, and optimize the first logic diagram according to the calibration result;
a training module 305 configured to train the simulation model according to the optimized first logic diagram;
A model module 306 configured to input the environmental parameters into the simulation model and output the second radio frequency parameters;
The calibration module 307 is configured to calibrate the target device based on the second radio frequency parameter and the first radio frequency parameter when the difference between the second radio frequency parameter and the first radio frequency parameter is greater than a preset threshold.
The environment parameters of the target device include parameters of the field environment where the target device is located, parameters of the network environment, and the like. The environment parameter is used to describe an environmental condition of an environment to which the target device belongs. The first radio frequency parameter is a current radio frequency parameter of the target device. The second radio frequency parameters of the target device corresponding to different environmental conditions are different, and the second radio frequency parameters are theoretically optimal radio frequency parameters of the target device under the environmental conditions. The target device can be any device in any field, for example, the target device is a measurement and control field, and can be a mobile communication test instrument, a comprehensive tester, a programmable power supply and the like. And calibrating the target equipment, namely updating the existing first radio frequency parameters of the target equipment into the second radio frequency parameters which are optimal in theory.
The training parameters include a plurality of environmental parameters, each of which has been labeled with its corresponding second radio frequency parameter. The calibration of the training parameters by using the calibration device may be to delete or correct some data that does not meet the preset rule in the training parameters, for example, if the second radio frequency parameters corresponding to a certain environmental parameter are not matched, then the second radio frequency parameters corresponding to a certain environmental parameter need to be marked. The linear regression process may be any fitting method commonly used, such as a least squares fitting method, although the linear regression process may be implemented by some software, such as excel or matlab. And inputting the environment parameter and the second radio frequency parameter into the software by taking the environment parameter as an independent variable and taking the second radio frequency parameter as an independent variable, so that a regression result can be output. If the regression result is a function, then a simulation model is constructed according to the regression result, and the simulation model can be constructed by taking the function as a main body and adding other requirements. Other requirements, such as the need to present the data in an image, then the simulation model needs to add the data to image conversion relationship based on the function. The first logic diagram is the logic expressed by the simulation model, and can be understood as a mapping relation or a functional expression equivalent to the simulation model. The simulation model is constructed based on the regression results, and the first logic diagram is extracted from the simulation model. The first logic diagram may be one to which the above "other requirements" class is added based on the regression results. The first logic diagram is optimized according to the calibration result, and the first logic diagram can be corrected according to the calibration result. Training the simulation model according to the optimized first logic diagram can be understood as updating model parameters of the simulation model by a back propagation method according to the optimized first logic diagram.
According to the embodiment of the disclosure, the calibration of the target equipment can be realized by means of the model, and a calibration instrument is not needed, so that the cost of the calibration equipment is reduced, and meanwhile, the accuracy of the calibration equipment can be improved by improving the accuracy of the model.
According to the technical scheme provided by the embodiment of the disclosure, training parameters are acquired, first radio frequency parameters and environment parameters of target equipment are acquired, calibration equipment is utilized to calibrate the training parameters to obtain calibration results, linear regression processing is conducted on the training parameters and the calibration results to obtain regression results, a simulation model is built according to the regression results, a first logic diagram is extracted from the simulation model, the first logic diagram is optimized according to the calibration results, the simulation model is trained according to the optimized first logic diagram, the environment parameters are input into the simulation model, second radio frequency parameters are output, and when the difference value between the second radio frequency parameters and the first radio frequency parameters is larger than a preset threshold value, the target equipment is calibrated based on the second radio frequency parameters and the first radio frequency parameters. By adopting the technical means, the problems of high calibration cost and low accuracy of the equipment in the prior art are solved, the cost of the calibration equipment is further reduced, and the accuracy of the calibration equipment is improved.
Optionally, the building module 303 is further configured to build a simulation network model using the simulation model and the neural network model, train the simulation network model with training parameters, extract a second logic diagram from the trained simulation network model and optimize the second logic diagram according to the calibration result, retrain the simulation network model according to the optimized second logic diagram, input environmental parameters into the simulation network model, and output second radio frequency parameters.
And constructing a simulation network model by using the simulation model and the neural network model, wherein the simulation model can be connected with the neural network model. According to the embodiment of the disclosure, the simulation network model is trained twice, so that the accuracy of the trained model is improved. The second logic diagram is similar to the first logic diagram and optimizing the second logic diagram is similar to optimizing the first logic diagram. Because the simulation model constructed by using the mathematical method is difficult to improve after the accuracy reaches a certain degree, but the neural network model does not have the problem. The neural network model can be trained through a large number of machine learning, so that the accuracy of the model can be improved as much as possible. The disclosed embodiments utilize neural network models to improve the accuracy of simulation models. Specifically, in the simulation network model, the output of the simulation model is an input of the neural network model, and the neural network model is the second radio frequency parameter (although the simulation model is established based on a mapping relationship between the environment parameter and the second radio frequency parameter, in practical application, the output of the simulation model may not be the second radio frequency parameter).
It should be noted that the neural network model may be any of the commonly used neural network models, such as Faster-RCNN. The training methods in this disclosure are all methods similar to deep learning training.
Optionally, the construction module 303 is further configured to determine a training data set corresponding to the training parameters according to the optimized second logic diagram, to train the simulation network model using the training data set to update parameters of the simulation model in the simulation network model in case of freezing the simulation network model in the first round of training, to train the simulation network model using the training data set to update parameters of the simulation network model in case of freezing the simulation model in the second round of training, and to train the simulation network model using the training data set in the third round of training to update parameters of the simulation model and the simulation network model in the third round of training.
According to the optimized second logic diagram, a training data set corresponding to the training parameters is determined, for example, the second radio frequency parameters corresponding to a certain environmental parameter are not right, and then the correct second radio frequency parameters corresponding to the certain environmental parameter can be marked by using the optimized second logic diagram. The training data set can be regarded as updated training parameters. This step is similar to correcting the second radio frequency parameter for which a certain environmental parameter corresponds in calibrating the training parameter using the calibration device. But because the second logic diagram is extracted from the trained simulated network model, the second logic diagram is more optimal than the calibration device.
And retraining the simulation network model according to the optimized second logic diagram, wherein the retraining comprises three rounds of training. The first training round, in the case of freezing a neural network model, trains the simulation network model using the training dataset, which can be understood as training only the simulation model, the step being used to adjust the parameters of the simulation model, the second training round, in the case of freezing a simulation model, trains the simulation network model using the training dataset, which can be understood as training only the neural network model, the step being the most common training of the neural network model, and the third training round, training the simulation network model using the training dataset, which is training both the simulation model and the neural network model, the step being used to fine tune the parameters of the simulation network model.
Optionally, the building module 303 is further configured to input the environmental parameter into a neural network model in the simulated network model, and output the second radio frequency parameter.
The simulation model in the embodiment of the present disclosure is only used in retraining, and assists in adjusting parameters of the neural network model (the embodiment of the present disclosure is different from the above embodiment in that the neural network model is used as a main body, the simulation model is used as an auxiliary body, the above embodiment is used as an auxiliary body, and the simulation model is used as a main body, so that the method has the advantage of helping the neural network model to converge as soon as possible.
Optionally, the first calibration module 302 is further configured to train the neural network model by using training parameters, so that the neural network model learns and stores a corresponding relation between the environmental parameters and the second radio frequency parameters, calibrate the training parameters by using the calibration device to obtain a calibration result, extract a third logic diagram from the neural network model, optimize the third logic diagram according to the calibration result, retrain the neural network model according to the optimized third logic diagram, input the environmental parameters into the neural network model, output the second radio frequency parameters, and calibrate the target device based on the second radio frequency parameters and the first radio frequency parameters when the difference between the second radio frequency parameters and the first radio frequency parameters is greater than a preset threshold.
The third logic diagram is similar to the first logic diagram and will not be described again. In the prior art, the radio frequency parameters of the target equipment are calibrated through the calibration equipment, the neural network model is introduced for the first time, and the second radio frequency parameters corresponding to the environmental parameters are determined through the trained neural network model, so that the radio frequency parameters of the target equipment are calibrated.
Optionally, the first calibration module 302 is further configured to acquire device information of a plurality of devices of a same type in a same application domain as the target device, and update model parameters of the neural network model according to the device information of the plurality of devices.
If training is performed on the neural network model by directly using the training parameters, the training time may be long, and in order to improve the training efficiency, the embodiment of the disclosure updates the model parameters of the neural network model according to the device information of the plurality of devices before training the neural network model. Updating the model parameters of the neural network model according to the device information of the plurality of devices may be understood as updating the values of the model parameters of the neural network model into the intervals corresponding to the target devices. The equipment information comprises information of application fields of equipment, types of the equipment, operating parameters of the equipment and the like, such as a theoretical interval of operating frequency of the equipment. By the technical means, the convergence rate of the neural network model in training can be improved.
In order to improve the accuracy of the trained neural network model, embodiments of the present disclosure retrain the neural network model. The calibration result is a second parameter value recommended by the calibration device.
In an alternative embodiment, the first radio frequency parameters include a first operating frequency, a first signal-to-noise ratio, a first signal gain, a first transmit signal power, a first transmit signal frequency, and a first s-parameter, and the second radio frequency parameters include a second operating frequency, a second signal-to-noise ratio, a second signal gain, a second transmit signal power, a second transmit signal frequency, and a second s-parameter.
S parameters including characteristic impedance, crosstalk, insertion loss, return loss, etc. For example, the embodiments of the present disclosure may adjust the first s-parameter by adjusting the operating circuit of the target device, thereby calibrating the first rf parameter based on the second rf parameter. The target device can select different circuits during working, and then select the circuit with the second s parameter. The signal gains corresponding to the different circuits are different, and the most suitable circuit can be selected according to the second signal gain. The first transmit signal power may also be adjusted based on the second transmit signal power, etc.
It should be noted that any one of the first radio frequency parameter and the second radio frequency parameter in the embodiments of the present disclosure may correspond to a value of one interval. For example, the second transmit signal power may be an interval of transmit signal power.
Optionally, the obtaining module 301 is further configured to obtain environmental parameters of the target device, establish a light wave reflection constraint condition, a light wave scattering constraint condition, a light wave refraction constraint condition and a light wave diffraction constraint condition based on a light wave reflection principle, a light wave scattering principle, a light wave refraction principle and a light wave diffraction principle, respectively, establish a light wave propagation energy function based on a light wave propagation energy loss minimum as a criterion, establish a mathematical model based on the light wave reflection constraint condition, the light wave scattering constraint condition, the light wave refraction constraint condition, the light wave diffraction constraint condition and the light wave propagation energy function, input the environmental parameters into the mathematical model, output a second operating wavelength of the target device, determine a second operating frequency of the target device according to the operating wavelength, and calibrate the target device based on the second operating frequency and the first operating frequency when a difference between the second operating frequency and the first operating frequency is greater than a preset threshold.
The second radio frequency parameter comprises a second operating frequency, and the first radio frequency parameter comprises a first operating frequency.
Embodiments of the present disclosure pertain to the idea of mathematical modeling. The propagation capabilities of different wavelengths in an environment vary, including reflection, scattering, refraction, diffraction, etc. In order to enhance the propagation capability of working waves of target equipment in the environment and reduce loss in the propagation process, the embodiment of the disclosure establishes a light wave reflection constraint condition based on a light wave reflection principle, establishes a light wave scattering constraint condition based on a light wave scattering principle, establishes a light wave refraction constraint condition based on a light wave refraction principle, and establishes a light wave diffraction constraint condition based on a light wave diffraction principle. The wavelength of the working wave of the target equipment can be controlled within a certain interval through the constraint condition, and then the second working wavelength of the target equipment is solved from the interval according to the light wave propagation energy function. Since the wavelength and the frequency satisfy certain conditions, the second operating frequency of the target device can be determined according to the operating wavelength. The environmental parameters include all information of the environment in which the target device is located, such as which obscuration objects affect the propagation of the light waves are located in the environment in which the target device is located, and the position and size of the obscuration objects.
Optionally, the obtaining module 301 is further configured to obtain an environmental parameter of the target device, determine, according to the environmental parameter, at least one parameter of an optimal voltage, an optimal current and anti-interference information of the target device, determine an optimal transmit signal power of the target device based on at least one parameter of the optimal voltage, the optimal current and the anti-interference information, wherein the second radio frequency parameter comprises the optimal transmit signal power, divide the optimal transmit signal power by the current transmit signal power to obtain a division value, determine an optimal signal gain according to the division value, wherein the second radio frequency parameter comprises the optimal signal gain, the first radio frequency parameter comprises the current transmit signal power, and determine an operating circuit of the target device according to the optimal signal gain.
The target device may operate in different environments or with different target device operation, and the allowed transmit signal power of the target device may vary, or the optimal transmit signal power of the target device may vary, taking into account the power consumption of the device. In order to solve the above technical problems, embodiments of the present disclosure determine, through environmental parameters, an optimal voltage, or an optimal current, or anti-interference information of a target device. Since the internal resistance of the target device is known, the optimal transmit signal power of the target device can be determined from the relationship of voltage, current, resistance, and power. The immunity information is used to indicate the immunity capability that the transmit signal power of the target device should have. In some cases, if the target device requires stronger immunity to interference, the transmit signal power of the target device should be increased, and if the target device does not require stronger immunity to interference, the transmit signal power of the target device should be appropriately reduced according to the specific circumstances. The optimal transmit signal power of the target device is also determined based on the immunity information.
The embodiment of the disclosure can provide a plurality of gain circuits for the target equipment, so as to provide a plurality of gains for the target equipment. The division value obtained by dividing the optimal transmission signal power by the current transmission signal power can be understood as the gain required by the target device, that is, the optimal signal gain. The current signal gain is the gain being used by the target device at the current time. After the optimal signal gain, the operating circuit of the target device may be determined based on the optimal signal gain. Determining the operational circuitry of the target device is also a method of calibrating the target device.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not constitute any limitation on the implementation process of the embodiments of the disclosure.
Fig. 4 is a schematic diagram of an electronic device 4 provided by an embodiment of the present disclosure. As shown in fig. 4, the electronic device 4 of this embodiment comprises a processor 401, a memory 402 and a computer program 403 stored in the memory 402 and executable on the processor 401. The steps of the various method embodiments described above are implemented by processor 401 when executing computer program 403. Or the processor 401, when executing the computer program 403, performs the functions of the modules/units in the above-described device embodiments.
Illustratively, the computer program 403 may be partitioned into one or more modules/units, which are stored in the memory 402 and executed by the processor 401 to complete the present disclosure. One or more of the modules/units may be a series of computer program instruction segments capable of performing a specific function for describing the execution of the computer program 403 in the electronic device 4.
The electronic device 4 may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The electronic device 4 may include, but is not limited to, a processor 401 and a memory 402. It will be appreciated by those skilled in the art that fig. 4 is merely an example of the electronic device 4 and is not meant to be limiting of the electronic device 4, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device may also include an input-output device, a network access device, a bus, etc.
The Processor 401 may be a central processing unit (Central Processing Unit, CPU) or may be other general purpose Processor, digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 402 may be an internal storage unit of the electronic device 4, for example, a hard disk or a memory of the electronic device 4. The memory 402 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the electronic device 4. Further, the memory 402 may also include both internal storage units and external storage devices of the electronic device 4. The memory 402 is used to store computer programs and other programs and data required by the electronic device. The memory 402 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other manners. For example, the apparatus/electronic device embodiments described above are merely illustrative, e.g., the division of modules or elements is merely a logical functional division, and there may be additional divisions of actual implementations, multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present disclosure may implement all or part of the flow of the method of the above-described embodiments, or may be implemented by a computer program to instruct related hardware, and the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of the method embodiments described above. The computer program may comprise computer program code, which may be in source code form, object code form, executable file or in some intermediate form, etc. The computer readable medium can include any entity or device capable of carrying computer program code, recording medium, USB flash disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), electrical carrier signals, telecommunications signals, and software distribution media, among others. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
The foregoing embodiments are merely for illustrating the technical solutions of the present disclosure, and not for limiting the same, and although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions described in the foregoing embodiments may be modified or some of the technical features may be replaced with the same, and that the modifications or the replacement should not depart from the spirit and scope of the technical solutions of the embodiments of the present disclosure.