Signal impedance adjusting method, device, equipment and medium
Through the intelligent and automated method based on network model, the problem of signal link impedance adjustment in the existing technology relying on manual experience, achieving more efficient and accurate signal transmission quality optimization, and adapting to the needs of diversified environments.
Patent Information
- Application Number
- CN202411223988.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-02
- Publication Date
- 2025-01-03
AI Technical Summary
The impedance adjustment of existing signal links depends on manual experience, resulting in large errors and low efficiency, making it difficult to ensure the optimal transmission quality, and requires a lot of testing time and resources.
Using an intelligent and automated method based on network model, the impedance adjustment strategy is predicted by inputting signal characteristics into the first and second network models, and the difference value between the theoretical and the real score is calculated. If the difference value is less than the threshold, the target impedance adjustment is output. Strategy, realize automatic closed-loop feedback and model updates, and improve prediction accuracy and adaptability.
It reduces manual intervention errors, improves the accuracy and efficiency of impedance adjustment, reduces costs, ensures the optimization of signal transmission quality, and adapts to diverse environmental needs.
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Figure CN119254358A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of electronic communication technology, and in particular to a signal impedance adjustment method, device, equipment and medium. Background Art
[0002] In electronic communication systems, the transmission quality of the signal link has an important impact on the stability and performance of the system. However, in the actual transmission process, the signal is easily affected by impedance mismatch, noise interference and other problems, resulting in signal overshoot, non-monotonicity and other problems, which in turn affect the normal operation of the system.
[0003] The existing impedance adjustment method relies on manual adjustment and empirical judgment. For example, engineers adjust the impedance value during signal transmission through experience and repeated experiments, and observe the changes in the signal to achieve the optimization effect. However, the method of adjusting the impedance value based on manual experience has large errors, making it difficult to ensure that the transmission quality of the signal link reaches the optimal effect, and it requires a lot of testing time and resources, which is inefficient. Summary of the invention
[0004] The present application provides a signal impedance adjustment method, device, equipment and medium to improve the accuracy and efficiency of impedance adjustment.
[0005] In a first aspect, a signal impedance adjustment method is provided, comprising:
[0006] Inputting the signal feature extracted based on the current signal into the first network model to predict the impedance adjustment strategy, and inputting the impedance adjustment strategy and the signal feature into the second network model to predict the theoretical score of the impedance adjustment strategy;
[0007] calculating a true score of the impedance adjustment strategy;
[0008] If the difference between the theoretical score and the actual score is less than a difference threshold, the impedance adjustment strategy is output as a target impedance adjustment strategy.
[0009] In an embodiment of the present application, first, the signal features extracted based on the current signal are input into the first network model to predict the impedance adjustment strategy, and the impedance adjustment strategy and the signal features are input into the second network model to predict the theoretical score of the impedance adjustment strategy; then, the real score of the impedance adjustment strategy is calculated; finally, if the difference between the theoretical score and the real score is less than the difference threshold, the impedance adjustment strategy is output as the target impedance adjustment strategy; compared with the prior art, the intelligent and automated impedance adjustment is achieved, manual intervention is reduced, errors caused by human factors are avoided, the accuracy and efficiency of the impedance adjustment are improved, and the cost of impedance adjustment is reduced.
[0010] In some embodiments, calculating the real score of the impedance adjustment strategy includes:
[0011] After performing the resistance adjustment operation based on the impedance adjustment strategy, collecting a new signal;
[0012] A true score of the impedance adjustment strategy is calculated according to the signal quality parameter of the new signal.
[0013] Through the above method, the tuning effect of the impedance adjustment strategy is reflected by actual test, which is more referenceable.
[0014] In some embodiments, the method further comprises:
[0015] If the difference value is greater than or equal to the difference threshold, the first network model and the second network model are updated based on the difference value; wherein the updated first network model and the second network model are used to determine the target impedance adjustment strategy.
[0016] Through the above method, automatic closed-loop feedback is achieved, so that the two models can better adapt to the changing external environment or internal conditions, gradually improve the prediction accuracy of the first network model, and improve the accuracy and reliability of impedance tuning.
[0017] In some embodiments, after updating the first network model and the second network model based on the difference value, the method further includes:
[0018] Inputting the extracted new signal features into the updated first network model to predict a new impedance adjustment strategy, and inputting the new impedance adjustment strategy and the new signal features into the updated second network model to predict a new theoretical score for the new impedance adjustment strategy; wherein the new signal features are features of the next signal;
[0019] calculating a new true score for the new impedance adjustment strategy;
[0020] If the difference between the new theoretical score and the new actual score is less than the difference threshold, the new impedance adjustment strategy is output as the target impedance adjustment strategy.
[0021] Through the above method, the performance of the two models can be iteratively improved through the feedback of the newly extracted new signal features, and the above models can be continuously optimized and adjusted, thereby enhancing the adaptability and robustness of the above models, so that the output impedance adjustment strategy is more accurate, ensuring that the tuning effect is closer to the actual application, thereby making the overall system more intelligent and flexible to adapt to more diverse environmental requirements, and further improving the impedance adjustment level within the adaptation range.
[0022] In some embodiments, the inputting the signal feature extracted based on the current signal into the first network model includes:
[0023] Using at least one filtering method to perform noise reduction processing on the current signal to obtain a target signal;
[0024] The signal features extracted based on the target signal are input into the first network model.
[0025] Through the above method, the proportion of useful signal features in subsequent analysis can be increased and the interference of irrelevant noise can be minimized as much as possible. Secondly, based on the relatively pure and clean target signal obtained after preliminary filtering and denoising, representative signal features are extracted and input into the first network model, so that the predicted impedance adjustment strategy is more accurate.
[0026] In some embodiments, the signal characteristics include at least one of the following:
[0027] Signal maximum value;
[0028] Signal minimum value;
[0029] Signal fluctuation waveform.
[0030] Through the above method, the multi-dimensional signal features are input into the first network model for prediction, so that the predicted impedance adjustment strategy is more accurate and the tuning effect is improved.
[0031] In a second aspect, a signal impedance adjustment device is provided, comprising:
[0032] A prediction module, used for inputting the signal features extracted based on the current signal into the first network model to predict the impedance adjustment strategy, and inputting the impedance adjustment strategy and the signal features into the second network model to predict the theoretical score of the impedance adjustment strategy;
[0033] A calculation module, used for calculating the real score of the impedance adjustment strategy;
[0034] An output module is used to output the impedance adjustment strategy as a target impedance adjustment strategy if the difference between the theoretical score and the actual score is less than a difference threshold.
[0035] In some embodiments, the apparatus further comprises an update module;
[0036] The updating module is used to update the first network model and the second network model based on the difference value if the difference value is greater than or equal to the difference threshold; wherein the updated first network model and the second network model are used to determine the target impedance adjustment strategy.
[0037] In some embodiments, the update module is specifically used to:
[0038] Inputting the extracted new signal features into the updated first network model to predict a new impedance adjustment strategy, and inputting the new impedance adjustment strategy and the new signal features into the updated second network model to predict a new theoretical score for the new impedance adjustment strategy; wherein the new signal features are features of the next signal;
[0039] calculating a new true score for the new impedance adjustment strategy;
[0040] If the difference between the new theoretical score and the new actual score is less than the difference threshold, the new impedance adjustment strategy is output as the target impedance adjustment strategy.
[0041] In some embodiments, the computing module is specifically used to:
[0042] After performing the resistance adjustment operation based on the impedance adjustment strategy, collecting a new signal;
[0043] A true score of the impedance adjustment strategy is calculated according to the signal quality parameter of the new signal.
[0044] In some embodiments, the prediction module is specifically used to:
[0045] Using at least one filtering method to perform noise reduction processing on the current signal to obtain a target signal;
[0046] The signal features extracted based on the target signal are input into the first network model.
[0047] According to a third aspect, an electronic device is provided, including:
[0048] A memory for storing a computer program; a processor for implementing any one of the methods described in the first aspect when executing the computer program stored in the memory.
[0049] According to a fourth aspect, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the method according to any one of the first aspects is implemented.
[0050] For each aspect from the second to the fourth aspect and the technical effects that may be achieved by each aspect, please refer to the above description of the technical effects that can be achieved by the first aspect or various possible schemes in the first aspect, and no further details will be given here. BRIEF DESCRIPTION OF THE DRAWINGS
[0051] Figure 1 A flow chart of a signal impedance adjustment method provided by an embodiment of the present application is exemplarily shown;
[0052] Figure 2 A logic diagram of signal impedance adjustment based on an Actor-Critic network provided in an embodiment of the present application is exemplarily shown;
[0053] Figure 3 A flowchart of a complete signal impedance adjustment method provided by an embodiment of the present application is exemplarily shown;
[0054] Figure 4 The structure diagram of a signal impedance adjustment device provided by an embodiment of the present application is exemplarily shown;
[0055] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is exemplarily shown. DETAILED DESCRIPTION
[0056] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described below in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is only a part of the embodiment of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be arbitrarily combined with each other. In addition, although the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in an order different from that here.
[0057] The terms "first" and "second" in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the term "comprising" and any of their variations are intended to cover non-exclusive protection. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices. "Multiple" in the present application can mean at least two, for example, two, three or more, and the embodiments of the present application are not limited.
[0058] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description. It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, which should be considered as exemplary, and their purpose is only to illustrate the feasibility of the implementation of the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.
[0059] Before introducing the signal impedance adjustment method provided in the embodiment of the present application, in order to facilitate understanding, the technical background of the embodiment of the present application is first introduced in detail.
[0060] At present, one of the impedance adjustment methods used is to rely on the manual adjustment and experience judgment of engineers. However, the method of adjusting the impedance value based on manual experience has large errors, making it difficult to ensure that the transmission quality of the signal link reaches the optimal effect, and requires a lot of testing time and resources, which is inefficient; another method is to adjust the impedance value of a certain signal through a signal simulation tool. For example, the impact of different impedance settings on a certain signal is tested in a simulation environment, but there may be errors between this and the actual test, and the simulation results may not fully reflect the actual situation; another method is to adjust the impedance value of a certain signal through preset rules and conditions, but this can only deal with a specific signal and lacks the flexibility and adaptability of adjusting impedance.
[0061] In view of this, the embodiments of the present application provide a signal impedance adjustment method, device, equipment and medium, which can improve the accuracy and efficiency of impedance adjustment for any signal.
[0062] To further illustrate the technical solution provided by the embodiment of the present application, this is described in detail below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiment of the present application provides the method operation steps as shown in the following embodiments or drawings, more or fewer operation steps may be included in the method based on routine or no creative labor. In the steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided by the embodiment of the present application. The method may be executed or executed in parallel in the actual processing process or when the device is executed in accordance with the method sequence shown in the embodiment or drawings.
[0063] Figure 1 The flowchart of a signal impedance adjustment method provided by an embodiment of the present application is exemplarily shown. The process can be executed by a signal impedance adjustment device, which can be implemented by software and / or hardware, such as Figure 1 As shown, the process includes the following steps:
[0064] 101: Inputting the signal feature extracted based on the current signal into the first network model to predict the impedance adjustment strategy, and inputting the impedance adjustment strategy and the signal feature into the second network model to predict the theoretical score of the impedance adjustment strategy.
[0065] In some embodiments, the input of the signal features extracted from the current signal into the first network model can be: using at least one filtering method to perform noise reduction processing on the current signal to obtain a target signal; based on the signal features extracted from the target signal, inputting the first network model; the proportion of useful signal features in subsequent analysis can be increased, and the interference of irrelevant noise can be minimized as much as possible, and then based on the relatively pure and clean target signal obtained after preliminary filtering and denoising, representative signal features are extracted and input into the first network model, so that the predicted impedance adjustment strategy is more accurate. Among them, the current signal can be waveform data collected by a certain oscilloscope.
[0066] In other embodiments, at least one filtering method may include a filter (such as a low-pass filter, a high-pass filter, a band-stop filter, a band-pass filter, etc.), a standard deviation method, a median filtering method, an absolute median deviation method (Median Absolute Deviation, MAD), etc. For different signals, a suitable filtering method can be selected according to actual needs, and the embodiments of the present application are not limited here.
[0067] It should be noted that the current signals include but are not limited to signals generated by a serial peripheral interface (SPI), signals generated by connecting a network controller and a baseband manager (NCSI), signals generated by an Inter-Integrated Circuit (I2C), etc., and the embodiments of the present application are not limited thereto.
[0068] In some embodiments, the signal feature may include at least one of a signal maximum value, a signal minimum value, and a signal fluctuation waveform, and may also include other valuable signal features. The multi-dimensional signal features are input into the first network model for prediction, so that the predicted impedance adjustment strategy is more accurate and the tuning effect is improved.
[0069] In some embodiments, the first network model can specifically be an actor network model, which predicts the next impedance adjustment strategy based on the signal characteristics extracted from the current signal; the second network model can specifically be a critic network model, which predicts the theoretical score of the impedance adjustment strategy based on the signal characteristic value and the impedance adjustment strategy to evaluate the effect of the impedance adjustment strategy on the signal transmission.
[0070] Furthermore, the theoretical score satisfies the following expression:
[0071] Q(s,a)=r+γV(s)…(1)
[0072] Among them, s represents the signal characteristics, a represents the impedance adjustment strategy, r represents the immediate reward, which can be understood as the direct reward obtained after adopting the impedance adjustment strategy under the signal characteristics, and γ represents the discount factor, which can be a constant between [0,1].
[0073] In other embodiments, the direction and value of the resistance adjustment of various signal data can be labeled based on expert knowledge, and an initial actor network can be trained using these labeled signal data to solve the cold start problem, which can reduce the exploration cost of the actor network in the initial stage and reduce the overall tuning cost.
[0074] 102: Calculate the true score of the above impedance adjustment strategy.
[0075] In some embodiments, the real score of the impedance adjustment strategy can be calculated by: collecting a new signal after performing a resistance adjustment operation based on the impedance adjustment strategy; calculating the real score of the impedance adjustment strategy based on the signal quality parameters of the new signal, so as to reflect the tuning effect of the impedance adjustment strategy through actual tests, which is more referenceable. For example, based on the impedance adjustment strategy, the resistance value is adjusted at the read end or the write end by an automatic resistance welding machine, and then a new signal is collected at the adjusted resistance value, and the real score of the impedance adjustment strategy is calculated based on the signal quality parameters of the new signal (for example, signal strength, signal-to-noise ratio, whether the signal fluctuation waveform is smooth and stable, etc.). The higher the score value, the better the current impedance adjustment strategy is, and the smaller the deviation between the goal achieved in the actual environment and the expectation is.
[0076] It should be noted that other similar methods may also be used to calculate the true score of the impedance adjustment strategy, such as manually scoring based on the display effect of the new signal on an oscilloscope, etc. The embodiments of the present application do not make specific limitations here.
[0077] 103: If the difference between the theoretical score and the actual score is less than the difference threshold, the impedance adjustment strategy is output as the target impedance adjustment strategy.
[0078] In this step, when the difference between the theoretical score and the actual score is less than the difference threshold, it indicates that the above impedance adjustment strategy can optimize the signal transmission quality and solve problems such as signal overshoot and non-monotonicity. Therefore, the predicted impedance adjustment strategy can be output as the target impedance adjustment strategy for this signal and can be used continuously.
[0079] In other embodiments, if the difference value is greater than or equal to the difference threshold, the first network model and the second network model are updated based on the difference value; wherein the updated first network model and the second network model are used to determine the target impedance adjustment strategy to achieve automatic closed-loop feedback, so that the two models can better adapt to the changing external environment or internal conditions, gradually improve the prediction accuracy of the first network model, and improve the accuracy and reliability of impedance tuning.
[0080] Further, based on the difference value, updating the first network model and the second network model may be:
[0081] The extracted new signal features are input into the updated first network model to predict the new impedance adjustment strategy, and the new impedance adjustment strategy and the new signal features are input into the updated second network model to predict the new theoretical score of the new impedance adjustment strategy; wherein the new signal features are the features of the next signal; the new real score of the new impedance adjustment strategy is calculated; if the difference between the new theoretical score and the new real score is less than the difference threshold, the new impedance adjustment strategy is output as the target impedance adjustment strategy.
[0082] It should be noted that if the difference between the new theoretical score and the new true score is greater than or equal to the difference threshold, the first network model and the second network model are updated based on the difference between the new theoretical score and the new true score until the final difference is less than the difference threshold.
[0083] In the embodiment of the present application, the performance of the two models can be iteratively improved through the feedback of the newly extracted new signal features, and the above-mentioned models can be continuously optimized and adjusted, thereby enhancing the adaptability and robustness of the above-mentioned models, so that the output impedance adjustment strategy is more accurate, ensuring that the tuning effect is closer to the actual application, thereby making the overall system more intelligent and flexible to adapt to more diverse environmental requirements, and further improving the impedance adjustment level within the adaptation range.
[0084] In some embodiments, the above difference value may satisfy the following expression:
[0085] Reward(decision i )=∑ n k＝0 Abs(real(xk )-spec(x k ))…(2)
[0086] Among them, decision i represents the impedance adjustment strategy predicted by the first network model for the i-th time, n represents the number of signal observation points (for example, the maximum value and the minimum value are each an observation point), spec(x k ) represents the best value in the specification, determined based on the above expression (1), real(x k ) represents the true score of the calculation.
[0087] In an embodiment of the present application, first, the signal features extracted based on the current signal are input into the first network model to predict the impedance adjustment strategy, and the impedance adjustment strategy and the signal features are input into the second network model to predict the theoretical score of the impedance adjustment strategy; then, the real score of the impedance adjustment strategy is calculated; finally, if the difference between the theoretical score and the real score is less than the difference threshold, the impedance adjustment strategy is output as the target impedance adjustment strategy; compared with the prior art, the intelligent and automated impedance adjustment is achieved, manual intervention is reduced, errors caused by human factors are avoided, the accuracy and efficiency of the impedance adjustment are improved, and the cost of impedance adjustment is reduced.
[0088] Figure 2 A logic diagram of signal impedance adjustment based on an Actor-Critic network provided in an embodiment of the present application is exemplified.
[0089] Firstly, the signal characteristics of the current signal are input into the actor network model to predict the impedance adjustment strategy, and the signal characteristics input and the impedance adjustment strategy are input into the critic network model to predict the theoretical score of the impedance adjustment strategy; then, the difference value between the theoretical score and the actual score calculation is calculated based on the loss function, and it is judged whether the difference value is less than the difference threshold. If so, the impedance adjustment strategy is output as the target impedance adjustment strategy; if not, the actor network model and the critic network model are updated based on the difference value, and the predicted new signal characteristics are input into the updated actor network model and the critic network model, and the new impedance adjustment strategy is predicted again, and this process is repeated until the difference value between the theoretical score and the actual score calculation of the new impedance adjustment strategy is less than the difference threshold, and the new impedance adjustment strategy is output as the target impedance strategy, thereby realizing the intelligentization and automation of impedance tuning, improving the efficiency and accuracy of impedance adjustment, and significantly improving the signal transmission quality, solving problems such as signal overshoot and non-monotonicity.
[0090] Figure 3 The flowchart of a complete signal impedance adjustment method provided by an embodiment of the present application is exemplified.
[0091] 301: Acquire waveform data of the corresponding signal through an oscilloscope.
[0092] 302: Extract features from the waveform data to obtain signal features of the waveform data.
[0093] 303: Input the signal feature into the actor network model to predict the impedance adjustment strategy, and input the impedance adjustment strategy and the signal feature into the critic network model to predict the theoretical score of the impedance adjustment strategy.
[0094] 304: After the impedance adjustment strategy is used to adjust the resistance, a new signal is collected, and a real score of the impedance adjustment strategy is calculated according to a signal quality parameter of the signal.
[0095] 305: A loss function is used to calculate the difference between the actual score and the theoretical score.
[0096] 306 : Determine whether the difference value is less than the difference threshold value. If not, proceed to 307 . If yes, proceed to 308 .
[0097] This step makes subsequent processing decisions by comparing the difference between the actual score and the theoretical score, ensuring that the changes to the above network model will not be too frequent due to inconspicuous changes. It also ensures that changes are only made when the monitored changes are large enough to significantly affect the effect, reducing the computational complexity.
[0098] 307: Update the actor network model and the critic network model according to the difference value to determine the target impedance adjustment strategy.
[0099] For the specific implementation process of this step, please refer to the previous Figure 1 The relevant description of 103 will not be repeated here.
[0100] 308: Output the impedance adjustment strategy as a target impedance adjustment.
[0101] Based on the same technical concept, a signal impedance adjustment device is also provided in an embodiment of the present application, and the device can implement the above-mentioned signal impedance adjustment method process in the embodiment of the present application.
[0102] Figure 4 The schematic diagram of the structure of a signal impedance adjustment device provided by an embodiment of the present application is exemplarily shown. The device includes a prediction module 401, a calculation module 402, an output module 403, and may further include an update module 404.
[0103] The prediction module 401 is used to input the signal characteristics extracted based on the current signal into the first network model to predict the impedance adjustment strategy, and input the impedance adjustment strategy and the signal characteristics into the second network model to predict the theoretical score of the impedance adjustment strategy.
[0104] The calculation module 402 is used to calculate the real score of the impedance adjustment strategy.
[0105] The output module 403 is configured to output the impedance adjustment strategy as a target impedance adjustment strategy if the difference between the theoretical score and the actual score is less than a difference threshold.
[0106] The updating module 404 is used to update the first network model and the second network model based on the difference value if the difference value is greater than or equal to the difference threshold; wherein the updated first network model and the second network model are used to determine the target impedance adjustment strategy.
[0107] The calculation module 402 is specifically configured to collect a new signal after performing a resistance adjustment operation based on the impedance adjustment strategy; and calculate a true score of the impedance adjustment strategy according to a signal quality parameter of the new signal.
[0108] The updating module 404 is specifically used to input the extracted new signal features into the updated first network model, predict a new impedance adjustment strategy, and input the new impedance adjustment strategy and the new signal features into the updated second network model to predict a new theoretical score for the new impedance adjustment strategy; wherein the new signal features are features of the next signal; calculate a new real score for the new impedance adjustment strategy; if the difference between the new theoretical score and the new real score is less than the difference threshold, output the new impedance adjustment strategy as the target impedance adjustment strategy.
[0109] The prediction module 401 is specifically used to adopt at least one filtering method to perform noise reduction processing on the current signal to obtain a target signal; based on the signal features extracted from the target signal, the signal features are input into the first network model.
[0110] It should be noted here that the above-mentioned device provided in the embodiment of the present application can implement all the method steps in the above-mentioned method embodiment and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.
[0111] Based on the same technical concept, an electronic device is also provided in an embodiment of the present application, and the electronic device can realize the function of the aforementioned signal impedance adjustment device.
[0112] Figure 5A schematic structural diagram of an electronic device provided in an embodiment of the present application is exemplarily shown.
[0113] At least one processor 501, and a memory 502 connected to the at least one processor 501. The specific connection medium between the processor 501 and the memory 502 is not limited in the embodiment of the present application. Figure 5 In the example, the processor 501 and the memory 502 are connected via a bus 500. The bus 500 is Figure 5 The connections between other components are shown in bold lines, and are not intended to be limiting. The bus 500 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. Alternatively, the processor 501 can also be called a controller, and there is no limitation on the name.
[0114] In the embodiment of the present application, the memory 502 stores instructions that can be executed by at least one processor 501. The at least one processor 501 can execute a signal impedance adjustment method discussed above by executing the instructions stored in the memory 502. The processor 501 can implement Figure 4 The functions of each module in the device shown.
[0115] Among them, the processor 501 is the control center of the device, and can use various interfaces and lines to connect the various parts of the entire control device. By running or executing instructions stored in the memory 502 and calling data stored in the memory 502, the various functions of the device and processing data, the device can be monitored as a whole.
[0116] In the embodiment of the present application, the processor 501 may include one or more processing units, and the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the modem processor may not be integrated into the processor 501. In some embodiments, the processor 501 and the memory 502 may be implemented on the same chip, and in some embodiments, they may also be implemented separately on separate chips.
[0117] The processor 501 may be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of a signal impedance adjustment method disclosed in the embodiments of the present application may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in a processor.
[0118] The memory 502 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 502 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. The memory 502 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 502 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.
[0119] By programming the processor 501, the code corresponding to the signal impedance adjustment method described in the above embodiment can be fixed into the chip, so that the chip can execute the code when running. Figure 1 The embodiment shown is a signal impedance adjustment method. How to design and program the processor 501 is a technology well known to those skilled in the art and will not be described in detail here.
[0120] It should be noted here that the above-mentioned electronic device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.
[0121] Based on the same technical concept, an embodiment of the present application provides a computer storage medium, the computer storage medium includes: a computer program code, when the computer program code is executed on a computer, the computer executes any one of the signal impedance adjustment methods discussed above. Since the principle of solving the problem by the above-mentioned computer storage medium is similar to that of a signal impedance adjustment method, the implementation of the above-mentioned computer storage medium can refer to the implementation of the method, and the repeated parts will not be repeated.
[0122] In the specific implementation process, the computer storage medium may include: Universal Serial Bus Flash Drive (USB), mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other storage media that can store program codes.
[0123] Based on the same technical concept, the embodiment of the present application also provides a computer program product, which includes: computer program code, when the computer program code is run on a computer, the computer executes any one of the signal impedance adjustment methods discussed above. Since the principle of solving the problem by the above computer program product is similar to that of a signal impedance adjustment method, the implementation of the above computer program product can refer to the implementation of the method, and the repeated parts will not be repeated.
[0124] The computer program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more conductors, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.
[0125] The method in the present application can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instruction is loaded and executed on a computer, the process or function described in the present application is executed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device, a core network device, an OAM or other programmable device.
[0126] The computer program or instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program or instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless means. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; it may also be an optical medium, such as a digital video disk; it may also be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both volatile and non-volatile types of storage media.
[0127] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.
[0128] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the instructions in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0130] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.
Claims
1. A signal impedance adjustment method, characterized in that: include: Inputting the signal feature extracted based on the current signal into the first network model to predict the impedance adjustment strategy, and inputting the impedance adjustment strategy and the signal feature into the second network model to predict the theoretical score of the impedance adjustment strategy; calculating a true score of the impedance adjustment strategy; If the difference between the theoretical score and the actual score is less than a difference threshold, the impedance adjustment strategy is output as a target impedance adjustment strategy.
2. The method according to claim 1, characterized in that The calculating the real score of the impedance adjustment strategy comprises: After performing the resistance adjustment operation based on the impedance adjustment strategy, collecting a new signal; A true score of the impedance adjustment strategy is calculated according to the signal quality parameter of the new signal.
3. The method according to claim 1, characterized in that The method further comprises: If the difference value is greater than or equal to the difference threshold, the first network model and the second network model are updated based on the difference value; wherein the updated first network model and the second network model are used to determine the target impedance adjustment strategy.
4. The method according to claim 3, characterized in that After updating the first network model and the second network model based on the difference value, the method further includes: Inputting the extracted new signal features into the updated first network model to predict a new impedance adjustment strategy, and inputting the new impedance adjustment strategy and the new signal features into the updated second network model to predict a new theoretical score for the new impedance adjustment strategy; wherein the new signal features are features of the next signal; calculating a new true score for the new impedance adjustment strategy; If the difference between the new theoretical score and the new actual score is less than the difference threshold, the new impedance adjustment strategy is output as the target impedance adjustment strategy.
5. The method according to claim 1, characterized in that The inputting the signal feature extracted based on the current signal into the first network model comprises: Using at least one filtering method to perform noise reduction processing on the current signal to obtain a target signal; The signal features extracted based on the target signal are input into the first network model.
6. The method according to any one of claims 1 to 5, characterized in that: The signal characteristics include at least one of the following: Signal maximum value; Signal minimum value; Signal fluctuation waveform.
7. A signal impedance adjustment device, characterized in that: include: A prediction module, used for inputting the signal features extracted based on the current signal into the first network model to predict the impedance adjustment strategy, and inputting the impedance adjustment strategy and the signal features into the second network model to predict the theoretical score of the impedance adjustment strategy; A calculation module, used for calculating the real score of the impedance adjustment strategy; An output module is used to output the impedance adjustment strategy as a target impedance adjustment strategy if the difference between the theoretical score and the actual score is less than a difference threshold.
8. The device according to claim 7, characterized in that The device also includes an update module; The updating module is used to update the first network model and the second network model based on the difference value if the difference value is greater than or equal to the difference threshold; wherein the updated first network model and the second network model are used to determine the target impedance adjustment strategy.
9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to implement the method according to any one of claims 1 to 6 when executing a computer program stored in the memory.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.