Physical characteristic value detection method and device and computer equipment

Abstract

The invention relates to a physical characteristic value detection method and device and computer equipment. The method comprises the steps of responding to a physical characteristic value detection instruction of an input signal of a target object, calling a physical characteristic value detection model, and performing detection processing on a physical characteristic value of the input signal of the target object in a detection period, the physical characteristic value detection model is a model for realizing physical characteristic value detection based on a plurality of sampling signals on an input signal. By adopting the method, the physical characteristic value detection model can be constructed by detecting part of the input signals in the period, so that the construction speed of the physical characteristic value detection model can be improved, and the detection speed and the detection efficiency of the physical characteristic value can be improved to a great extent.

Description

Technical Field

[0001] The present application relates to the field of electronic technology, and in particular to a method, device and computer equipment for detecting physical characteristic values. Background Art

[0002] With the development of electronic technology, electronic devices are widely used. In practical applications, the physical characteristics of the input signal of the electronic device or the input signal of each hardware circuit in the electronic device determine the stability and performance of the electronic device. Therefore, it is becoming more and more important to detect the physical characteristic value of the input signal of the electronic device or each hardware circuit in the electronic device.

[0003] In the related art, input signals of electronic devices or hardware circuits are mainly collected, and physical characteristic values ​​of the input signals of the electronic devices or hardware circuits are detected according to the input signals.

[0004] However, in the related art, there is a problem of low detection efficiency in the process of detecting the physical characteristic value of the input signal of the electronic device or each hardware circuit. Summary of the invention

[0005] Based on this, it is necessary to provide a physical characteristic value detection method, device and computer equipment to address the above technical problems, which can improve the detection efficiency of the physical characteristic value of the input signal in the process of detecting the physical characteristic value of the input signal of the electronic device or each hardware circuit.

[0006] In a first aspect, an embodiment of the present application provides a method for detecting a physical property value, the method comprising:

[0007] A physical characteristic value detection instruction in response to an input signal of a target object; the target object is an electronic device or any hardware circuit in the electronic device;

[0008] The physical characteristic value detection model is called to detect and process the physical characteristic value of the input signal of the target object within the detection period; the physical characteristic value detection model is a model that realizes physical characteristic value detection based on multiple sampling signals on the input signal.

[0009] The technical solution in the embodiment of the present application responds to the physical characteristic value detection instruction of the input signal of the target object, and calls the physical characteristic value detection model to detect and process the physical characteristic value of the input signal of the target object within the detection period, wherein the physical characteristic value detection model is a model for realizing physical characteristic value detection based on multiple sampling signals on the input signal; the above method can construct the physical characteristic value detection model through part of the input signal within the detection period, thereby improving the construction speed of the physical characteristic value detection model, and then greatly improving the detection speed and detection efficiency of the physical characteristic value, that is, improving the timeliness of the detection, and can quickly ensure that the electronic device is maintained in a stable state; at the same time, the above method does not require manual participation, thereby improving the detection accuracy of the physical characteristic value; in addition, the above method has no limitations on the frequency scenario of the input signal, and can be applicable to the fixed frequency scenario of the input signal, and is also applicable to the dynamic frequency scenario of the input signal, thereby improving the wide applicability of the physical characteristic value detection method; furthermore, the above method does not need to complete the physical characteristic value detection process through instructions one by one, and the physical characteristic value detection process can be realized by responding to the physical characteristic value detection instruction with one key, thereby greatly improving the detection speed and detection efficiency of the physical characteristic value.

[0010] In one embodiment, the detection cycle includes multiple half cycles, and the multiple sampling signals include multiple adjacent signals corresponding to each zero-crossing point on the input signal; the physical characteristic value of the input signal of the target object within the detection cycle is detected and processed, including:

[0011] Acquire multiple adjacent signals corresponding to the zero-crossing points of the input signal in each half cycle within the detection cycle;

[0012] Filtering is performed according to the sampling frequency, each adjacent signal and each zero-crossing time in each half cycle to determine multiple physical characteristic values ​​of the input signal in the detection cycle to complete the detection process.

[0013] The technical solution in the embodiment of the present application obtains multiple adjacent signals corresponding to each zero-crossing point of the input signal in each half cycle within the detection period, and performs filtering processing according to the sampling frequency, each adjacent signal and each zero-crossing time in each half cycle, and determines multiple physical characteristic values ​​of the input signal in the detection period to complete the detection processing; the above method does not need to pass through all the input signals in the detection period, and only part of the sampling signals in the detection period can complete the physical characteristic value detection of the input signal of the target object, which can reduce the amount of signals required to be processed during the detection process, thereby improving the detection speed and detection efficiency of the physical characteristic value; at the same time, the above method is not limited to the input signal of the zero-crossing point, and the detection of the physical characteristic value can be completed through the multiple adjacent signals corresponding to each zero-crossing point in each half cycle within the detection period, which can eliminate data acquisition errors and avoid errors caused by the inability to accurately acquire zero-crossing points, thereby improving the detection accuracy of the physical characteristic value.

[0014] In one embodiment, the multiple physical characteristic values ​​include multiple frequency values ​​and multiple frequency change rate values; filtering is performed according to the sampling frequency in each half cycle, each adjacent signal and each zero crossing time to determine multiple physical characteristic values ​​of the input signal in the detection cycle, including:

[0015] Perform sliding window filtering according to each sampling frequency, each adjacent signal and each zero-crossing time to obtain multiple frequency values ​​corresponding to each frequency sliding window within the detection period;

[0016] Sliding window filtering is performed according to each frequency value, the total duration of the detection cycle and the duration of each half cycle to obtain multiple frequency change rate values ​​corresponding to each frequency change rate sliding window within the detection cycle; the size of the frequency change rate sliding window is larger than the size of the frequency sliding window.

[0017] The technical solution in the embodiment of the present application performs sliding window filtering processing according to each sampling frequency, each adjacent signal and each zero-crossing time to obtain multiple frequency values ​​corresponding to each frequency sliding window in the detection period, and performs sliding window filtering processing according to each frequency value, the total duration of the detection period and the duration of each half period to obtain multiple frequency change rate values ​​corresponding to each frequency change rate sliding window in the detection period, wherein the size of the frequency change rate sliding window is larger than the size of the frequency sliding window; the above method can obtain multiple frequency values ​​and multiple frequency change rate values ​​within the detection period through sliding window filtering processing, thereby reducing calculation errors and improving the accuracy of the acquired frequency values ​​and frequency change rate values, that is, improving the detection accuracy of the physical characteristic values.

[0018] In one embodiment, the method further comprises:

[0019] Acquire multiple groups of information to be fitted corresponding to each half cycle, and perform fitting processing on each group of information to be fitted to determine multiple fitting values; the fitting value represents the frequency change rate value of the fitting;

[0020] Averaging each fitting value to obtain a plurality of fitting means; and averaging each frequency change rate value to obtain a plurality of sliding window means;

[0021] According to each fitting mean and each sliding window mean, each frequency change rate value is optimized to obtain the target frequency change rate value within the detection period.

[0022] The technical solution in the embodiment of the present application obtains multiple groups of information to be fitted corresponding to each half cycle, and fits each group of information to be fitted, determines multiple fitting values, averages the fitting values ​​to obtain multiple fitting means, and averages the frequency change rate values ​​to obtain multiple sliding window means, and optimizes each frequency change rate value according to each fitting mean and each sliding window mean to obtain the target frequency change rate value within the detection period; the above method can obtain the optimized frequency change rate value corresponding to the signal fluctuation point within the detection period to adjust the input signal of the target object and improve the stability and working performance of the target object; at the same time, the above method does not need to involve a nonlinear fitting algorithm in the detection process, thereby reducing the system computing resources occupied during the detection process.

[0023] In one embodiment, the signal fluctuation point includes a signal entry point; and each frequency change rate value is optimized according to each fitting mean and each sliding window mean to obtain a target frequency change rate value within a detection period, including:

[0024] If the absolute value of the differential sliding window value corresponding to each fitting mean is greater than the first preset threshold, and each fitting mean is greater than the target sliding window mean, then determining the type of the frequency change rate of the input signal in the entry fluctuation section according to the differential sliding window value, and obtaining at least one latest entry frequency change rate value generated by the input signal after the signal entry point;

[0025] According to the latest entry frequency change rate values ​​and the type of the entry fluctuation segment, the fitting value and each frequency change rate value corresponding to the fluctuation segment where the signal entry point is located are screened to determine a plurality of first frequency change rate values;

[0026] Kalman filtering is performed on each first frequency change rate value to obtain a target frequency change rate value of the input signal within a detection period.

[0027] The technical solution in the embodiment of the present application is that if each fitting mean is greater than the target sliding window mean, and the absolute value of the differential sliding window value corresponding to each fitting mean is greater than the first preset threshold value, then the type of the frequency change rate of the input signal in the entry fluctuation segment is determined according to the differential sliding window value, and at least one latest entry frequency change rate value generated by the input signal after the signal entry point is obtained, and according to each latest entry frequency change rate value and the type of the entry fluctuation segment, the fitting value and each frequency change rate value corresponding to the fluctuation segment where the signal entry point is located are screened and processed to determine multiple first frequency change rate values, and each first frequency change rate value is Kalman filtered to obtain the target frequency change rate value of the input signal within the detection period; the above method can obtain the corresponding optimized frequency change rate value within the detection period, so as to realize the application of adjusting the input signal of the target object or evaluating the stability of the target object, thereby improving the stability and working performance of the target object.

[0028] In one embodiment, according to each latest entry frequency change rate value and the type of the entry fluctuation segment, the fitting value and each frequency change rate value corresponding to the fluctuation segment where the signal entry point is located are screened to determine a plurality of first frequency change rate values, including:

[0029] According to each latest entry frequency change rate value, the fitting value and frequency change rate value corresponding to the fluctuation section where the signal entry point is located are screened and processed to determine the entry frequency change rate value corresponding to the fluctuation section where the signal entry point is located;

[0030] According to the type of the entry fluctuation segment, the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point and at least one latest frequency change rate value generated after the signal entry point are screened to determine multiple first frequency change rate values.

[0031] The technical solution in the embodiment of the present application screens the fitting values ​​and frequency change rate values ​​corresponding to the fluctuation segment where the signal entry point is located according to each latest entry frequency change rate value, determines the entry frequency change rate value corresponding to the fluctuation segment where the signal entry point is located, and screens the entry frequency change rate value, each frequency change rate value generated before the signal entry point, and at least one latest frequency change rate value generated after the signal entry point according to the type of the entry fluctuation segment, to determine multiple first frequency change rate values; the above method can screen out the effective frequency change rate value corresponding to the fluctuation segment where the signal exit point is located according to different actual scenarios, that is, the first frequency change rate value, so as to provide reference information for subsequent precise optimization of the frequency change rate value.

[0032] In one embodiment, according to each latest entry frequency change rate value, the fitting value and the frequency change rate value corresponding to the fluctuation section where the signal entry point is located are screened and processed to determine the entry frequency change rate value corresponding to the fluctuation section where the signal entry point is located, including:

[0033] If the derivatives of the latest input frequency change rate values ​​are all greater than the preset error threshold, the fitting value corresponding to the fluctuation segment where the signal entry point is located is determined as the input frequency change rate value; otherwise, the frequency change rate value corresponding to the fluctuation segment where the signal entry point is located is determined as the input frequency change rate value.

[0034] The technical solution in the embodiment of the present application is to determine the fitting value corresponding to the fluctuation segment where the signal entry point is located as the entry frequency change rate value when the derivatives of each latest entry frequency change rate value are greater than a preset error threshold value, otherwise, the frequency change rate value corresponding to the fluctuation segment where the signal entry point is located is determined as the entry frequency change rate value; the above method can filter the entry frequency change rate value corresponding to the fluctuation segment where the signal entry point is located according to the size relationship between the derivatives of each latest entry frequency change rate value and the preset error threshold value, the processing process is relatively simple, and the complexity of filtering the entry frequency change rate value is reduced, thereby being able to improve the filtering speed and efficiency of the entry frequency change rate.

[0035] In one embodiment, performing Kalman filtering on each first frequency change rate value to obtain a target frequency change rate value of the input signal within a detection period includes:

[0036] Determine the process noise according to each first frequency change rate value and the historical frequency change rate value of the input signal; the historical frequency change rate value is the target frequency change rate value at the historical moment;

[0037] Based on the process noise, Kalman filtering is performed on each first frequency change rate value to obtain a target frequency change rate value.

[0038] The technical solution in the embodiment of the present application determines the process noise based on each first frequency change rate value and the historical frequency change rate value of the input signal, and performs Kalman filtering on each first frequency change rate value based on the process noise to obtain a target frequency change rate value; the above method can accurately determine the process noise required for the Kalman filtering processing according to the actual application scenario, and on this basis, can make the Kalman filtering processing result more accurate, and improve the accuracy of the frequency change rate value optimization processing.

[0039] In one embodiment, the signal fluctuation point also includes a signal exit point; and the method further includes:

[0040] If the absolute value of the latest differential sliding window value is less than the second preset threshold, then the exit frequency change rate value corresponding to the fluctuation segment where the signal exit point is located within the detection period is obtained;

[0041] According to the type of the exit fluctuation segment, the exit frequency change rate value, the frequency change rate values ​​generated before the signal exit point, and at least one latest frequency change rate value generated after the signal exit point are screened to determine a plurality of second frequency change rate values;

[0042] Kalman filtering is performed on each second frequency change rate value to obtain a target frequency change rate value of the input signal within a detection period.

[0043] The technical solution in the embodiment of the present application performs Kalman filtering on the multiple second frequency change rate values ​​corresponding to the detection period to obtain the target frequency change rate value of the input signal within the detection period; the above method can obtain the optimized frequency change rate value within the detection period to achieve the application of adjusting the input signal of the target object or evaluating the stability of the target object, thereby improving the stability and working performance of the target object.

[0044] In one embodiment, obtaining the exit frequency change rate value corresponding to the fluctuation segment where the signal exit point is located within the detection period includes:

[0045] Obtain at least one fluctuation segment frequency change rate value corresponding to the fluctuation segment where the signal exit point is located;

[0046] If the derivatives of the frequency change rate values ​​of each fluctuation segment are greater than the preset error threshold, the fitting value corresponding to the fluctuation segment where the signal exit point is located is determined as the exit frequency change rate value; otherwise, the frequency change rate value corresponding to the fluctuation segment where the signal exit point is located is determined as the exit frequency change rate value.

[0047] The technical solution in the embodiment of the present application obtains at least one frequency change rate value of the fluctuation segment corresponding to the fluctuation segment where the signal exit point is located. If the derivatives of the frequency change rate values ​​of each fluctuation segment are greater than a preset error threshold, the fitting value corresponding to the fluctuation segment where the signal exit point is located is determined as the exit frequency change rate value; otherwise, the frequency change rate value corresponding to the fluctuation segment where the signal exit point is located is determined as the exit frequency change rate value. The above method can filter the exit frequency change rate value corresponding to the fluctuation segment where the signal exit point is located through the relationship between the derivatives of the frequency change rate values ​​of each fluctuation segment and the preset error threshold. The processing process is relatively simple, which reduces the complexity of filtering the exit frequency change rate value, and thus can improve the filtering speed and efficiency of the exit frequency change rate.

[0048] In a second aspect, an embodiment of the present application provides a physical property value detection device, the device comprising:

[0049] An instruction response module, used to respond to a physical characteristic value detection instruction of an input signal of a target object; the target object is an electronic device or any hardware circuit in the electronic device;

[0050] The model calling module is used to call the physical characteristic value detection model to detect and process the physical characteristic value of the input signal of the target object within the detection period; the physical characteristic value detection model is a model that realizes physical characteristic value detection based on multiple sampling signals on the input signal.

[0051] In a third aspect, an embodiment of the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method of any one of the embodiments of the first aspect when executing the computer program.

[0052] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method of any embodiment in the above-mentioned first aspect are implemented.

[0053] In a fifth aspect, an embodiment of the present application further provides a computer program product, the computer program product comprising a computer program, which, when executed by a processor, implements the steps of the method of any embodiment of the first aspect above.

[0054] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic diagram of a flow chart of a method for detecting a physical property value in one embodiment;

[0056] Figure 2 A schematic diagram of a flow chart of a method for detecting a physical characteristic value in another embodiment;

[0057] Figure 3 A schematic diagram of a flow chart of a method for detecting a physical characteristic value in another embodiment;

[0058] Figure 4 A schematic diagram of the detection results of physical property values ​​in one embodiment;

[0059] Figure 5 A schematic diagram of a flow chart of a method for detecting a physical characteristic value in another embodiment;

[0060] Figure 6 A waveform diagram showing a change in an input signal of a target object over time in one embodiment;

[0061] Figure 7 is a flow chart of a method for detecting a physical characteristic value in another embodiment;

[0062] Figure 8 A graph showing the frequency change rate of an input signal over time during a detection period in one embodiment;

[0063] Fig. 9 is a flow chart of a method for detecting a physical characteristic value in another embodiment;

[0064] Fig.10 A comparison diagram of the frequency change rate values ​​before and after optimization processing in one embodiment;

[0065] Fig.11 A schematic diagram of a flow chart of a method for detecting a physical characteristic value in another embodiment;

[0066] Fig.12 A schematic diagram of a flow chart of a method for detecting a physical characteristic value in another embodiment;

[0067] Fig.13 A waveform diagram showing the variation of the differential sliding window value over time within a detection period in an embodiment;

[0068] Fig.14 A schematic diagram of a flow chart of a method for detecting a physical characteristic value in another embodiment;

[0069] Fig.15 is a structural block diagram of a physical characteristic value detection device in another embodiment;

[0070] Fig.16 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0072] In the field of power electronics, electronic devices are widely used. In practical applications, the physical characteristics of the input signal of the electronic device or the input signal of each hardware circuit in the electronic device determine the stability and performance of the electronic device. Therefore, it becomes more and more important to detect the physical characteristic value of the input signal of the electronic device or each hardware circuit in the electronic device. In the related art, the input signal of the electronic device or each hardware circuit is mainly collected, and the physical characteristic value of the input signal of the electronic device or each hardware circuit is detected according to the input signal. However, in the process of detecting the physical characteristic value of the input signal of the electronic device or each hardware circuit in the related art, there is a problem of low detection efficiency. Based on this, the embodiment of the present application provides a physical characteristic value detection method, which can improve the detection efficiency of the physical characteristic value.

[0073] The physical characteristic value detection method provided in the embodiment of the present application can be applied to a computer device. Optionally, the computer device can be a personal computer, a desktop computer, a laptop computer, a smart phone, a smart bracelet, or other electronic device with a data processing function. The specific form of the computer device is not limited in this embodiment. The following embodiment uses a computer device as the execution subject of the physical characteristic value detection method to introduce the specific process of the physical characteristic value detection method.

[0074] like Figure 1 FIG. 1 is a flow chart of a method for detecting a physical property value provided in an embodiment of the present application. The method can be implemented by the following steps:

[0075] S100, responding to a physical characteristic value detection instruction of an input signal of a target object, wherein the target object is an electronic device or any hardware circuit in the electronic device.

[0076] The target object may be any electronic device, and the electronic device and the computer device may be the same device or different devices. In practical applications, the electronic device may include multiple hardware circuits, and the target object may be any hardware circuit in the electronic device.

[0077] In the embodiment of the present application, the computer device can receive a physical characteristic value detection instruction of an input signal of a target object input by a user, and can also automatically trigger the physical characteristic value detection instruction of the input signal of the target object. Correspondingly, the computer device can respond to the physical characteristic value detection instruction.

[0078] Optionally, the physical characteristic value of the input signal may be the amplitude, period, etc. of the input signal. In an embodiment of the present application, the physical characteristic value of the input signal may be the frequency value and frequency change rate value of the input signal.

[0079] S200, calling a physical characteristic value detection model to detect and process the physical characteristic value of the input signal of the target object within the detection period. The physical characteristic value detection model is a model that implements physical characteristic value detection based on multiple sampling signals on the input signal.

[0080] In actual applications, a computer device can obtain the input signal of the target object within the detection period, and input the input signal of the target object within the detection period into a physical characteristic value detection model. The physical characteristic value detection model detects and processes the physical characteristic value of the input signal of the target object within the detection period and outputs the detection result.

[0081] Optionally, the above-mentioned physical characteristic value detection model can be a combination of at least one of a convolutional neural network model, a fully connected neural network model, a recurrent neural network model, a long short-term memory neural network model, etc.; in an embodiment of the present application, the above-mentioned physical characteristic value detection model can be composed of a mathematical model.

[0082] It should be noted here that the above-mentioned physical characteristic value detection model is an internal algorithm of the computer device, which is conducive to the computer device's maintenance of the physical characteristic value detection model and flexible modification to dynamically update the physical characteristic value detection model and continuously improve the stability of the physical characteristic value detection model.

[0083] The technical solution in the embodiment of the present application responds to the physical characteristic value detection instruction of the input signal of the target object, and calls the physical characteristic value detection model to detect and process the physical characteristic value of the input signal of the target object within the detection period, wherein the physical characteristic value detection model is a model for realizing physical characteristic value detection based on multiple sampling signals on the input signal; the above method can construct the physical characteristic value detection model through part of the input signal within the detection period, thereby improving the construction speed of the physical characteristic value detection model, and then greatly improving the detection speed and detection efficiency of the physical characteristic value, that is, improving the timeliness of the detection, and can quickly ensure that the electronic device is maintained in a stable state; at the same time, the above method does not require manual participation, thereby improving the detection accuracy of the physical characteristic value; in addition, the above method has no limitations on the frequency scenario of the input signal, and can be applicable to the fixed frequency scenario of the input signal, and is also applicable to the dynamic frequency scenario of the input signal, thereby improving the wide applicability of the physical characteristic value detection method; furthermore, the above method does not need to complete the physical characteristic value detection process through instructions one by one, and the physical characteristic value detection process can be realized by responding to the physical characteristic value detection instruction with one key, thereby greatly improving the detection speed and detection efficiency of the physical characteristic value.

[0084] In one embodiment, the detection cycle includes a plurality of half cycles, and the plurality of sampling signals include a plurality of adjacent signals corresponding to each zero-crossing point on the input signal; Figure 2 As shown, the step of detecting and processing the physical characteristic value of the input signal of the target object within the detection period in the above S200 may include the following steps:

[0085] S210, obtaining a plurality of adjacent signals corresponding to the zero-crossing points of the input signal in each half cycle within the detection cycle.

[0086] Optionally, the above-mentioned input signal can be a sine signal or a cosine signal; the above-mentioned zero-crossing point refers to the point where the input signal is 0. In practical applications, multiple adjacent signals corresponding to each zero-crossing point in each half cycle within the detection period can be collected according to a preset sampling interval. Optionally, the above-mentioned multiple adjacent signals can at least include input signals of multiple points near the zero-crossing point; in the embodiment of the present application, the above-mentioned multiple adjacent signals corresponding to the zero-crossing point can include two adjacent signals corresponding to two points near the zero-crossing point, and the time interval between the two points is equal to one sampling period.

[0087] Among them, a complete cycle includes two half cycles, wherein the input signal of one half cycle is a positive signal, and the input signal of the other half cycle is a negative signal.

[0088] It should be noted here that the duration of the detection cycle can be user-defined; during the process of inputting a signal from the target object, multiple adjacent signals corresponding to the zero-crossing points of the input signal in each half cycle within the detection cycle can be collected in real time.

[0089] S220, performing filtering processing according to the sampling frequency in each half cycle, each adjacent signal and each zero-crossing time, and determining multiple physical characteristic values ​​of the input signal in the detection cycle to complete the detection process.

[0090] Specifically, the computer device can use a filtering algorithm to filter the sampling frequency, each adjacent signal and each zero-crossing time in each half cycle to determine multiple physical characteristic values ​​of the input signal in the detection cycle. Optionally, the above filtering algorithm can be implemented by at least one of a limiting filtering method, an arithmetic average filtering method, a limiting average filtering method, a first-order lag filtering method, a weighted recursive average filtering method, a de-jitter filtering method, a limiting de-jitter filtering method, etc.

[0091] At the same time, the computer device can pre-train an algorithm model, and then input the sampling frequency, each adjacent signal and each zero-crossing time in each half cycle into the algorithm model. The algorithm model outputs multiple physical characteristic values ​​of the input signal in the detection cycle after filtering according to the sampling frequency, each adjacent signal and each zero-crossing time in each half cycle.

[0092] The technical solution in the embodiment of the present application obtains multiple adjacent signals corresponding to each zero-crossing point of the input signal in each half cycle within the detection period, and performs filtering processing according to the sampling frequency, each adjacent signal and each zero-crossing time in each half cycle, and determines multiple physical characteristic values ​​of the input signal in the detection period to complete the detection processing; the above method does not need to pass through all the input signals in the detection period, and only part of the sampling signals in the detection period can complete the physical characteristic value detection of the input signal of the target object, which can reduce the amount of signals required to be processed during the detection process, thereby improving the detection speed and detection efficiency of the physical characteristic value; at the same time, the above method is not limited to the input signal of the zero-crossing point, and the detection of the physical characteristic value can be completed through the multiple adjacent signals corresponding to each zero-crossing point in each half cycle within the detection period, which can eliminate data acquisition errors and avoid errors caused by the inability to accurately acquire zero-crossing points, thereby improving the detection accuracy of the physical characteristic value.

[0093] The following is an explanation of the process of filtering the input signal in the detection period according to the sampling frequency, adjacent signals and zero-crossing time in each half cycle to determine the multiple physical characteristic values ​​of the input signal in the detection period. In one embodiment, the multiple physical characteristic values ​​include multiple frequency values ​​and multiple frequency change rate values; Figure 3 As shown, the steps in the above S220 can be implemented in the following manner:

[0094] S221, performing sliding window filtering processing according to each sampling frequency, each adjacent signal and each zero-crossing time, to obtain multiple frequency values ​​corresponding to each frequency sliding window within a detection period.

[0095] In the process of sliding window filtering, the start time of the detection period is taken as the starting point, and the frequency sliding window moves within the detection period with the step length of the frequency sliding window, and the step length of the frequency sliding window can be half a period.

[0096] In practical applications, for each frequency sliding window, each sampling frequency, each adjacent signal and each zero-crossing time in the frequency sliding window can be obtained, and a sliding window filtering algorithm is used to perform sliding window filtering processing according to each sampling frequency, each adjacent signal and each zero-crossing time in the frequency sliding window to obtain frequency values ​​corresponding to multiple half-cycles in the frequency sliding window. Among them, each movement of the frequency sliding window obtains a corresponding frequency value. Optionally, the above sliding window filtering algorithm can be a sliding average filter, a sliding median filter, a sliding Gaussian filter, etc.

[0097] In the embodiment of the present application, the frequency values ​​corresponding to the multiple half cycles in the frequency sliding window are win It can be expressed as:

[0098]

[0099] The frequency sliding window includes n half cycles (that is, n represents the size of the frequency sliding window), and the starting time of the frequency sliding window is t i , and so on, the time of zero crossing in the nth half cycle is t i+n (that is, the end time of the frequency sliding window), the time length of each half cycle in the frequency sliding window is Δt(i), and FS*n represents the total sampling frequency of n half cycles. win It can also be expressed as b_win.

[0100] S222: Perform sliding window filtering according to each frequency value, the total duration of the detection cycle and the duration of each half cycle to obtain multiple frequency change rate values ​​corresponding to each frequency change rate sliding window within the detection cycle, wherein the size of the frequency change rate sliding window is larger than the size of the frequency sliding window.

[0101] In the process of sliding window filtering, the start time of the detection period is taken as the starting point, and the sliding window moves within the detection period at the step length of the frequency change rate sliding window according to the frequency change rate sliding window, and the step length of the frequency change rate sliding window can be half a period.

[0102] In practical applications, for each frequency change rate sliding window, each sampling frequency, each adjacent signal and each zero-crossing time in the frequency change rate sliding window can be obtained, and a sliding window filtering algorithm is used to perform sliding window filtering processing according to each frequency value in the frequency change rate sliding window, the total duration of the detection cycle and the duration of each half cycle, to obtain the frequency change rate values ​​corresponding to multiple half cycles in the frequency change rate sliding window. Among them, each movement of the frequency change rate sliding window obtains a corresponding frequency change rate value. In an embodiment of the present application, the size of the above-mentioned frequency change rate sliding window is larger than the size of the frequency sliding window.

[0103] In addition, for each frequency change rate sliding window, the computer device can also obtain each sampling frequency, each adjacent signal and each zero-crossing time in the frequency change rate sliding window, and perform arithmetic operations on each sampling frequency, each adjacent signal and each zero-crossing time in the frequency change rate sliding window to obtain multiple frequency change rate values ​​corresponding to each frequency change rate sliding window in the detection period. Optionally, the arithmetic operation can be implemented by at least one of addition, multiplication, division, subtraction, logarithm, integration, etc.

[0104] In the embodiment of the present application, the frequency change rate values ​​corresponding to multiple half cycles in the frequency change rate sliding window are FrequencyRatio win It can be expressed as:

[0105]

[0106] Among them, t s Indicates the start time of the frequency change rate sliding window, tN Indicates the end time of the frequency change rate sliding window, t i Indicates the start time of the frequency sliding window, t i+n Indicates the end time of the frequency sliding window, It represents the sum of the differences of the frequency values ​​f corresponding to two adjacent frequency sliding windows, f(t s+1 ) and f(t s ) corresponds to a time period that differs by one and a half cycles, t sum Equal to (t N -t s ). In the embodiment of the present application, the frequency change rate value FrequencyRatio win It can also be expressed as a_win. Figure 4 Shown is a combined schematic diagram of a curve showing the frequency value determined within a detection period changing with time and a curve showing the frequency value changing with time.

[0107] The technical solution in the embodiment of the present application performs sliding window filtering processing according to each sampling frequency, each adjacent signal and each zero-crossing time to obtain multiple frequency values ​​corresponding to each frequency sliding window in the detection period, and performs sliding window filtering processing according to each frequency value, the total duration of the detection period and the duration of each half period to obtain multiple frequency change rate values ​​corresponding to each frequency change rate sliding window in the detection period, wherein the size of the frequency change rate sliding window is larger than the size of the frequency sliding window; the above method can obtain multiple frequency values ​​and multiple frequency change rate values ​​within the detection period through sliding window filtering processing, thereby reducing calculation errors and improving the accuracy of the acquired frequency values ​​and frequency change rate values, that is, improving the detection accuracy of the physical characteristic values.

[0108] In some scenarios, the optimized frequency change rate value within the detection period can be obtained to adjust the input signal of the target object and improve the stability and working performance of the target object. The process of obtaining the optimized frequency change rate value within the detection period is described below. In one embodiment, after executing the steps in S200 above, Figure 5 As shown, the above method may further include the following steps:

[0109] S300, obtaining multiple groups of information to be fitted corresponding to each half cycle, and performing fitting processing on each group of information to be fitted to determine multiple fitting values, wherein the fitting value represents the fitted frequency change rate value.

[0110] Specifically, the computer device may obtain multiple groups of information to be fitted corresponding to each half period within the detection period, and perform fitting processing on each group of information to be fitted to determine multiple fitting values.

[0111] Optionally, the above information to be fitted can be expressed as (xi, yi); x1 is equal to the product of the time interval between two adjacent sampling points in the first half cycle within the detection cycle and the number of sampling points in the first half cycle; wherein, starting from the second half cycle within the detection cycle, xi can be equal to the product of the time interval between two adjacent sampling points in the i-th half cycle within the detection cycle and the number of sampling points in the i-th half cycle, and the sum of the product of the time interval between two adjacent sampling points in each half cycle from the first half cycle to the (i-1)th half cycle within the detection cycle and the number of sampling points in each half cycle; yi represents the instantaneous frequency of the i-th half cycle within the detection cycle.

[0112] Based on the above method, the information to be fitted corresponding to each half cycle in the detection cycle can be obtained, that is, multiple information to be fitted can be obtained. Further, the information to be fitted corresponding to each half cycle in the detection cycle can be grouped to obtain multiple groups of information to be fitted.

[0113] It should be noted here that Figure 6 As shown in the figure, the horizontal axis is the input signal of the target object during the detection period, that is, the input voltage or current, and the horizontal axis is time. If the time between two adjacent zero crossing points is t i and t i+1 , t i to i+1 For one half cycle, the instantaneous frequency of this half cycle is Δt(i+1) It can be expressed as:

[0114] Frequency Δt(i+1) =FS / (2*ΔSamp cnt ) (3)

[0115] ΔSamp cnt =v2last / (v1last+v2last)+CNT-1+v1 / (v1+v2) (4)

[0116] Where Δt(i+1) represents t i to i+1 The time interval between i and t i+1 FS represents the sampling frequency within Δt(i+1), n ​​represents the sequence number of the half cycle within the detection cycle, v1last and v2last are t i Two adjacent signals of zero crossing at time t, v1 and v2 i+1 Two adjacent signals of zero crossing points at the same time. Among them, the FS in each half cycle is the same.

[0117] It should be noted here that the number of each group of information to be fitted can be set arbitrarily, but at least one of the information to be fitted in two adjacent groups of information to be fitted is the same, and at least one of the information to be fitted is different. In the embodiment of the present application, the computer device can use the least squares method to perform linear fitting processing according to each group of information to be fitted, obtain the fitting straight line corresponding to the group of information to be fitted, and use the slope of the fitting straight line as the corresponding fitting value, that is, a_leaq.

[0118] S400, averaging each fitting value to obtain a plurality of fitting means; and averaging each frequency change rate value to obtain a plurality of sliding window means.

[0119] Among them, based on the fitting values ​​obtained in the previous steps, the fitting values ​​can be grouped, and then the average of each group of fitting values ​​is calculated to obtain the fitting mean corresponding to each group of fitting values, that is, a_leaq_avg.

[0120] At the same time, the frequency change rate values ​​can be grouped, and then the average value of each group of frequency change rate values ​​is calculated to obtain the sliding window average corresponding to each group of frequency change rate values, that is, a_win_avg.

[0121] S500: Optimize each frequency change rate value according to each fitting mean value and each sliding window mean value to obtain a target frequency change rate value corresponding to a signal fluctuation point within a detection period.

[0122] It should be noted here that the target frequency change rate value within the detection period is obtained based on the fitting means and sliding window means obtained before entering the signal fluctuation point.

[0123] In one implementation, an algorithm model can be pre-trained, and then the fitting means and sliding window means obtained within the detection period are input into the algorithm model. The algorithm model optimizes each frequency change rate value and outputs the target frequency change rate value within the detection period.

[0124] In another implementation, each fitting mean and each sliding window mean obtained within the detection period may be subjected to arithmetic operation, comparison, and / or matching, etc., to optimize each frequency change rate value and obtain a target frequency change rate value within the detection period. Optionally, the arithmetic operation may include at least one of addition, multiplication, subtraction, division, logarithmic, exponential, etc.

[0125] The technical solution in the embodiment of the present application obtains multiple groups of information to be fitted corresponding to each half cycle, and fits each group of information to be fitted, determines multiple fitting values, averages the fitting values ​​to obtain multiple fitting means, and averages the frequency change rate values ​​to obtain multiple sliding window means, and optimizes each frequency change rate value according to each fitting mean and each sliding window mean to obtain the target frequency change rate value within the detection period; the above method can obtain the optimized frequency change rate value corresponding to the signal fluctuation point within the detection period to adjust the input signal of the target object and improve the stability and working performance of the target object; at the same time, the above method does not need to involve a nonlinear fitting algorithm in the detection process, thereby reducing the system computing resources occupied during the detection process.

[0126] The following describes the process of optimizing each frequency change rate value according to each fitting mean and each sliding window mean to obtain the target frequency change rate value within the detection period. In one embodiment, the above-mentioned signal fluctuation point includes a signal entry point; Figure 7 As shown, the steps in S500 above can be implemented in the following manner:

[0127] S510. If the absolute value of the differential sliding window value corresponding to each fitting mean is greater than the first preset threshold, and each fitting mean is greater than the target sliding window mean, the type of the frequency change rate of the input signal in the entry fluctuation segment is determined according to the differential sliding window value, and at least one latest entry frequency change rate value generated by the input signal after the signal entry point is obtained.

[0128] Among them, the above-mentioned target sliding window mean value can be the latest sliding window mean value corresponding to the current moment in the detection period; the differential sliding window value corresponding to the above-mentioned fitting mean values ​​can be equal to the difference between the latest fitting mean value corresponding to the detection period and the first fitting mean value corresponding to the detection period. At the same time, the above-mentioned signal entry point can be understood as the critical point where the frequency change rate value of the input signal in the detection period gradually increases or decreases from a stable state.

[0129] For example, Figure 8 The figure shows the change curve of the frequency change rate value within the detection period (the curve in the actual enlarged figure has slight fluctuations, Figure 8 (not shown), points A and B within the rectangle may represent signal entry points.

[0130] In practical applications, the computer device can determine whether the absolute value of the differential sliding window value is greater than a preset first sliding window threshold. If the absolute value of the differential sliding window value is greater than the first sliding window threshold, continue to determine whether each currently acquired fitting mean is greater than the target sliding window mean. When each fitting mean is greater than the target sliding window mean, it is determined that if the differential sliding window value is greater than the absolute value of the first sliding window threshold, the type of the entry fluctuation segment is determined to be a rising segment of the frequency change rate. If the differential sliding window value is less than the first sliding window threshold, the type of the entry fluctuation segment is determined to be a falling segment of the frequency change rate. Optionally, the above-mentioned entry fluctuation segment can be understood as a frequency change rate fluctuation segment including a signal entry point.

[0131] Optionally, the first sliding window threshold may be user-defined or determined based on historical experience. In an embodiment of the present application, the first sliding window threshold may be greater than the frequency change rate fluctuation error and may be a negative value.

[0132] At the same time, at least one latest entry frequency change rate value generated by the input signal after the signal entry point can be obtained, wherein the frequency change rate sliding window size of the latest entry frequency change rate value is the same as the size of the above frequency change rate sliding window.

[0133] It should be noted here that the rising segment refers to the interval in which the frequency change rate value after the signal entry point is continuously increasing; the falling segment refers to the interval in which the frequency change rate value after the signal entry point is continuously decreasing.

[0134] S520. According to the latest entry frequency change rate values ​​and the type of the entry fluctuation segment, the fitting value and each frequency change rate value corresponding to the fluctuation segment where the signal entry point is located are screened to determine a plurality of first frequency change rate values.

[0135] In one implementation, the computer device may also pre-train a processing model, and then input the latest entry frequency change rate values, the type of entry fluctuation segment, the fitting value corresponding to the fluctuation segment where the signal entry point is located, and each frequency change rate value into the processing model, and the processing model screens the fitting value corresponding to the fluctuation segment where the signal entry point is located and each frequency change rate value and outputs multiple values ​​as multiple first frequency change rate values. Optionally, the above processing model may be implemented by at least one of a convolutional neural network model, a fully connected neural network model, a recurrent neural network model, a residual network model, and the like.

[0136] In another implementation, the computer device can search for the type of entry fluctuation segment in the mapping relationship between different fluctuation segment types and different filtering strategies, and obtain the filtering strategy corresponding to the fluctuation segment type that matches the type of entry fluctuation segment in the mapping relationship, and then filter and process the latest entry frequency change rate values ​​and each frequency change rate value, the fitting value corresponding to the fluctuation segment where the signal entry point is located, and each frequency change rate value according to the obtained filtering strategy to obtain multiple first frequency change rate values.

[0137] S530: Perform Kalman filtering on each first frequency change rate value to obtain a target frequency change rate value of the input signal within a detection period.

[0138] Specifically, the computer device may call a Kalman filter and use a Kalman filter algorithm to perform Kalman filter processing on each first frequency change rate value to obtain a target frequency change rate value of the input signal within a detection period.

[0139] Optionally, the above-mentioned Kalman filter algorithm may be an extended Kalman filter algorithm, an unscented Kalman filter algorithm, etc., but in the embodiment of the present application, the above-mentioned Kalman filter algorithm may be a linear Kalman filter algorithm.

[0140] In one embodiment, if Fig. 9 As shown, the step of performing Kalman filtering on each first frequency change rate value in S530 to obtain a target frequency change rate value of the input signal within the detection period can be implemented by the following steps:

[0141] S531. Determine process noise according to each first frequency change rate value and a historical frequency change rate value of the input signal, wherein the historical frequency change rate value is a target frequency change rate value corresponding to a historical moment.

[0142] Specifically, the computer device may perform arithmetic operations on each first frequency change rate value and a historical frequency change rate value of the input signal to obtain process noise, wherein the period length corresponding to the historical frequency change rate value is the same as the length of the detection period.

[0143] In the embodiment of the present application, the process noise Q can be expressed as:

[0144]

[0145] in, Indicates the first frequency change rate values ​​z obtained at time k during the period from the current time (i.e., time k) to a time before time k in the detection cycle A The target frequency change rate value x obtained at the previous moment (i.e. (k-1) moment) A-1 The sum of the differences between ′is a process noise coefficient, and the Q corresponding to the signal entry point is ′ It can be determined by the user or based on historical experience. In the embodiment of the present application, the Q corresponding to the above signal entry point is ′ It can be equal to 1. It should be noted here that the process noise Q is The calculation can be applied to dynamically changing input signals and can also ensure that the process noise Q will not drop to 0.

[0146] S532: Based on process noise, perform Kalman filtering on each first frequency change rate value to obtain a target frequency change rate value.

[0147] Specifically, the computer device can pre-train an algorithm model, and then input the process noise and each first frequency change rate value into the algorithm model. The algorithm model performs Kalman filtering on each first frequency change rate value based on the process noise and then outputs the target frequency change rate value within the detection period.

[0148] In the embodiment of the present application, the process of performing Kalman filtering on each first frequency change rate value based on process noise can be described as:

[0149] (1) Obtain multiple optimal frequency change rate values ​​x of the input signal at the previous moment (i.e., moment (k-1)) k-1 ;

[0150] (2) Based on the multiple optimal frequency change rate values ​​x at the previous moment (i.e., moment (k-1)) k-1 Predict multiple frequency change rate values ​​x' at the current time (i.e. time k) k , that is, x′ k =A*x k-1 +B*u k-1 ;

[0151] (3) According to the error covariance P at time (k-1) k-1 and the process noise Q predicts the error covariance P′ at time k k , that is, P′ k =A*P k-1 *A T +Q; where P 0 is equal to 0;

[0152] (4) According to the predicted error covariance P′ at time k k , calculate the Kalman gain K at time k k , that is, K k =P′ k *H T / (H*P′ k *H T+R); where R represents the observation noise covariance;

[0153] (5) Based on the predicted multiple frequency change rate values ​​x′ k , for each first frequency change rate value z at time k k Perform correction and update to obtain the target frequency change rate value x of the input signal at time k within the detection period k , that is, x k = x′ k +K k *(z k -H*x′ k ).

[0154] It should be noted here that, since the frequency change rate is usually 0 or non-0, a one-dimensional Kalman filter algorithm can be used to perform accurate Kalman filtering on each first frequency change rate value. Naturally, in the Kalman filter processing, the above A is equal to 1, B is equal to 0, and H is equal to 1.

[0155] At the same time, according to the Kalman gain K at time k k The error covariance P′ for the predicted k-th time k Update and get the error covariance P at time k k , that is, P k =(1-K k )*P′ k , in order to provide dependency information for optimizing multiple optimal frequency change rate values ​​of the input signal at the next moment (i.e., moment (k+1)).

[0156] For example, Fig.10 The figure shows a comparison diagram of each frequency change rate value within the detection period and the target frequency change rate value within the detection period, wherein the smooth and relatively stable black curve represents the target frequency change rate value curve within the detection period (i.e., the frequency change rate value curve of the input signal after optimization processing), and the fluctuating gray curve represents the frequency change rate value curve within the detection period (i.e., the frequency change rate value curve of the input signal before optimization processing).

[0157] In this embodiment, the process noise is determined based on each first frequency change rate value and the historical frequency change rate value of the input signal, and Kalman filtering is performed on each first frequency change rate value based on the process noise to obtain a target frequency change rate value; the above method can accurately determine the process noise required for the Kalman filtering processing process according to the actual application scenario, and on this basis, the Kalman filtering processing result can be made more accurate, thereby improving the accuracy of the frequency change rate value optimization processing.

[0158] The technical solution in the embodiment of the present application is that if each fitting mean is greater than the target sliding window mean, and the absolute value of the differential sliding window value corresponding to each fitting mean is greater than the first preset threshold value, then the type of the frequency change rate of the input signal in the entry fluctuation segment is determined according to the differential sliding window value, and at least one latest entry frequency change rate value generated by the input signal after the signal entry point is obtained, and according to each latest entry frequency change rate value and the type of the entry fluctuation segment, the fitting value and each frequency change rate value corresponding to the fluctuation segment where the signal entry point is located are screened and processed to determine multiple first frequency change rate values, and each first frequency change rate value is Kalman filtered to obtain the target frequency change rate value of the input signal within the detection period; the above method can obtain the corresponding optimized frequency change rate value within the detection period, so as to realize the application of adjusting the input signal of the target object or evaluating the stability of the target object, thereby improving the stability and working performance of the target object.

[0159] In one embodiment, Fig.11 As shown, in the above S520, according to each latest entry frequency change rate value and the type of the entry fluctuation segment, the fitting value and each frequency change rate value corresponding to the fluctuation segment where the signal entry point is located are screened and processed to determine multiple first frequency change rate values, which can be achieved by the following steps:

[0160] S521. According to each latest entry frequency change rate value, the fitting value and frequency change rate value corresponding to the fluctuation segment where the signal entry point is located are screened and processed to determine the entry frequency change rate value corresponding to the fluctuation segment where the signal entry point is located.

[0161] Specifically, the computer device can select any value from the fitting value and the frequency change rate value corresponding to the fluctuation segment where the signal entry point is located according to the size range of each latest entry frequency change rate value and the preset screening strategy as the entry frequency change rate value corresponding to the fluctuation segment where the signal entry point is located. It should be noted here that the latest entry frequency change rate value can be understood as the frequency change rate value generated after including the signal entry point.

[0162] S522. According to the type of the entry fluctuation segment, the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and at least one latest frequency change rate value generated after the signal entry point are screened to determine multiple first frequency change rate values.

[0163] Among them, if the signal entry point is the first signal entry point in the detection period, then the frequency change rate values ​​generated before the signal entry point may include multiple frequency change rate values ​​corresponding to the stable segment of the frequency change rate in the detection period; if the signal entry point is any signal entry point after the first signal entry point in the detection period, then the frequency change rate values ​​generated before the signal entry point may include multiple frequency change rate values ​​corresponding to multiple stable segments of the frequency change rate in the detection period, and multiple frequency change rate values ​​corresponding to at least one frequency change rate fluctuation segment (the rising segment of the frequency change rate and / or the falling segment of the frequency change rate) before the signal entry point. Continue to see Figure 8 As shown, signal entry point A can be called the first signal entry point in the detection period, and signal entry point B can be called the second signal entry point in the detection period.

[0164] It should be noted here that the frequency change rate fluctuation section can be understood as the interval between the signal entry point and the signal exit point in the detection period; at the same time, the intervals in the detection period other than the frequency change rate fluctuation section are referred to as the frequency change rate stable section. Figure 8 The AC segment and BD segment are both called frequency change rate fluctuation segments, among which the frequency change rate value of the AC segment is constantly increasing, so naturally, the AC segment can be called the rising segment of the frequency change rate, and the frequency change rate value of the BD segment is constantly decreasing, so naturally, the BD segment can be called the falling segment of the frequency change rate; the segments outside point A, outside point D and the BC segment are all called stable segments of the frequency change rate.

[0165] Furthermore, the type of entry fluctuation segment can be searched in the mapping relationship between different fluctuation segment types and different filtering methods, and the filtering method corresponding to the fluctuation segment type that matches the type of the entry fluctuation segment in the mapping relationship can be obtained. The entry frequency change rate value, each frequency change rate value generated before the signal entry point, and at least one latest frequency change rate value generated after the signal entry point are filtered and processed according to the obtained filtering method to obtain multiple first frequency change rate values.

[0166] Among them, if the type of the entry fluctuation segment is a rising segment, the first modified median filtering algorithm can be used to screen and process the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and at least one latest frequency change rate value generated after the signal entry point to obtain multiple first frequency change rate values.

[0167] Here, the process of screening and processing the rising section using the first modified median filtering algorithm is explained: if the absolute values ​​of the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and the latest frequency change rate values ​​are all greater than 0 and less than 0.6, then the maximum and minimum values ​​are deleted from the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and the latest frequency change rate values, and the remaining values ​​are used as multiple first frequency change rate values; if the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and the latest frequency change rate values ​​are all greater than 0.6 and less than 0.7, then the minimum value is deleted from the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and the latest frequency change rate values, and the remaining values ​​are used as multiple first frequency change rate values. The remaining values ​​are used as multiple first frequency change rate values; if the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and the latest frequency change rate values ​​are all greater than 0.7 and less than 0.8, then two minimum values ​​are deleted from the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and the latest frequency change rate values, and the remaining values ​​are used as multiple first frequency change rate values; if the absolute values ​​of the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and the latest frequency change rate values ​​belong to other numerical ranges, then three minimum values ​​are deleted from the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and the latest frequency change rate values, and the remaining values ​​are used as multiple first frequency change rate values.

[0168] At the same time, if the type of the entry fluctuation segment is a descending segment, the second modified median filtering algorithm is used to screen the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and at least one latest frequency change rate value generated after the signal entry point to obtain multiple first frequency change rate values.

[0169] Here, the process of screening and processing the descending segment using the second modified median filtering algorithm is explained: if the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and the latest frequency change rate values ​​are all greater than -0.6 and less than 0, then the maximum and minimum values ​​are deleted from the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and the latest frequency change rate values, and the remaining values ​​are used as multiple first frequency change rate values; if the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and the latest frequency change rate values ​​are all greater than -0.7 and less than -0.6, then the maximum value is deleted from the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and the latest frequency change rate values, and the remaining values ​​are used as multiple first frequency change rate values. The remaining values ​​are used as multiple first frequency change rate values; if the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and the latest frequency change rate values ​​are all greater than -0.8 and less than -0.7, then the two maximum values ​​are deleted from the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and the latest frequency change rate values, and the remaining values ​​are used as multiple first frequency change rate values; if the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and the latest frequency change rate values ​​belong to other numerical ranges, then the three maximum values ​​are deleted from the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and the latest frequency change rate values, and the remaining values ​​are used as multiple first frequency change rate values.

[0170] The technical solution in the embodiment of the present application screens the fitting values ​​and frequency change rate values ​​corresponding to the fluctuation segment where the signal entry point is located according to each latest entry frequency change rate value, determines the entry frequency change rate value corresponding to the fluctuation segment where the signal entry point is located, and screens the entry frequency change rate value, each frequency change rate value generated before the signal entry point, and at least one latest frequency change rate value generated after the signal entry point according to the type of the entry fluctuation segment, to determine multiple first frequency change rate values; the above method can screen out the effective frequency change rate value corresponding to the fluctuation segment where the signal exit point is located according to different actual scenarios, that is, the first frequency change rate value, so as to provide reference information for subsequent precise optimization of the frequency change rate value.

[0171] The following is a description of the process of screening the fitting value and the frequency change rate value corresponding to the fluctuation segment where the signal entry point is located according to each latest entry frequency change rate value to determine the entry frequency change rate value corresponding to the fluctuation segment where the signal entry point is located. In one embodiment, the steps in S521 above may include:

[0172] If the derivatives of the latest entry frequency change rate values ​​are all greater than the preset error threshold, the fitting value corresponding to the fluctuation segment where the signal entry point is located is determined as the entry frequency change rate value; if the derivatives of the latest entry frequency change rate values ​​are all less than or equal to the preset error threshold, the frequency change rate value corresponding to the fluctuation segment where the signal entry point is located is determined as the entry frequency change rate value.

[0173] The preset error threshold is determined according to the processing error of the computer device. Specifically, the computer device can derive the latest entry frequency change rate values ​​to obtain the derivatives of the latest entry frequency change rate values. If the derivatives of the latest entry frequency change rate values ​​are all greater than the preset error threshold, the fitting value corresponding to the fluctuation segment where the signal entry point is located is determined as the entry frequency change rate value; if the derivatives of the latest entry frequency change rate values ​​are all less than or equal to the preset error threshold, the frequency change rate value corresponding to the fluctuation segment where the signal exit point is located is determined as the exit frequency change rate value.

[0174] The technical solution in the embodiment of the present application is to determine the fitting value corresponding to the fluctuation segment where the signal exit point is located as the entry frequency change rate value when the derivatives of each latest entry frequency change rate value are greater than a preset error threshold value, otherwise, the frequency change rate value corresponding to the fluctuation segment where the signal entry point is located is determined as the entry frequency change rate value; the above method can filter the entry frequency change rate value corresponding to the fluctuation segment where the signal entry point is located according to the size relationship between the derivatives of each latest entry frequency change rate value and the preset error threshold value, the processing process is relatively simple, and the complexity of filtering the entry frequency change rate value is reduced, thereby being able to improve the filtering speed and efficiency of the entry frequency change rate.

[0175] In some scenarios, a signal exit point may also appear in the detection period. The following is an explanation of the process of obtaining the optimized frequency change rate value in the detection period. In one embodiment, the above signal fluctuation point also includes a signal exit point; after executing the steps in S500, Fig.12 As shown, the above method may further include the following steps:

[0176] S600: If the absolute value of the latest differential sliding window value is less than the second preset threshold, obtain the exit frequency change rate value corresponding to the fluctuation segment where the signal exit point within the detection period is located.

[0177] The signal exit point can be understood as the critical point where the frequency change rate of the input signal changes from small to large and tends to a stable state within the detection period, or the critical point where the frequency change rate of the input signal changes from large to small and tends to a stable state. Figure 8 As shown, Figure 8 Points C and D within the middle circle may represent signal exit points.

[0178] In actual applications, the computer device can determine whether the absolute value of the latest differential sliding window value is greater than the preset second sliding window threshold. If the absolute value of the latest differential sliding window value is less than the second sliding window threshold, it indicates that the signal point corresponding to the current time is the signal exit point. At this time, the frequency change rate value corresponding to the signal exit point within the detection period can be obtained, that is, the exit frequency change rate value.

[0179] It should be noted that the output frequency change rate value corresponding to the fluctuation segment where the above signal output point is located may include at least one frequency change rate value corresponding to the previous signal input point adjacent to the signal output point and the signal output point. Figure 8 As shown, if the signal exit point is the first signal exit point C within the detection period, the exit frequency change rate value corresponding to the signal exit point C may include at least one frequency change rate value corresponding to the preceding signal entry point A adjacent to the signal exit point C and the signal exit point C, that is, at least one exit frequency change rate value corresponding to the AC segment; if the signal exit point is any signal exit point after the first signal exit point C within the detection period, such as the signal exit point B, then the exit frequency change rate value corresponding to the signal exit point D may include at least one frequency change rate value corresponding to the preceding signal entry point B adjacent to the signal exit point D and the signal exit point D, that is, at least one exit frequency change rate value corresponding to the BD segment.

[0180] S700. According to the type of the exit fluctuation segment, filter the exit frequency change rate value, the frequency change rate values ​​generated before the signal exit point, and at least one latest frequency change rate value generated after the signal exit point to determine a plurality of second frequency change rate values.

[0181] It should be noted here that if the signal exit point is the first signal exit point within the detection period, the frequency change rate values ​​generated before the signal exit point may include the frequency change rate values ​​corresponding to the frequency change rate stable segment before the signal exit point within the detection period, and at least one exit frequency change rate value corresponding to the previous signal entry point adjacent to the signal exit point and the fluctuation segment where the signal exit point is located; if the signal exit point is any signal exit point after the first signal exit point within the detection period, the frequency change rate values ​​generated before the signal exit point may include the frequency change rate values ​​corresponding to multiple frequency change rate stable segments within the detection period, at least one exit frequency change rate value corresponding to the previous signal entry point adjacent to the signal exit point and the fluctuation segment where the signal exit point is located, and the frequency change rate values ​​corresponding to other frequency change rate fluctuation segments.

[0182] Among them, the computer device can determine the type of the frequency change rate of the input signal in the exit fluctuation segment according to the latest differential sliding window value; specifically, it can be judged whether the current latest differential sliding window value is greater than the preset second sliding window threshold. If the absolute value of the latest differential sliding window value is greater than the absolute value of the second sliding window threshold, the type of the exit fluctuation segment is determined to be an ascending segment; if the latest differential sliding window value is less than the second sliding window threshold, the type of the exit fluctuation segment is determined to be a descending segment.

[0183] Optionally, the second sliding window threshold may be user-defined or determined based on historical experience. In the embodiment of the present application, the second sliding window threshold is a negative value approaching 0. The second sliding window threshold is smaller than the first sliding window threshold.

[0184] For example, Fig.13 As shown in the figure, the horizontal axis represents time, and the vertical axis represents the curve of the frequency change rate value changing with time. Among them, 10 segments represent the interval where the frequency change rate rises from 0 to a certain positive value, that is, the rising segment of the frequency change rate; 20 segments represent the interval where the frequency change rate drops from a certain positive value to 0, that is, the falling segment of the frequency change rate; 30 segments represent the interval where the frequency change rate drops from 0 to a certain negative value, that is, the falling segment of the frequency change rate; 40 segments represent the interval where the frequency change rate rises from a certain negative value to 0, that is, the rising and falling segment of the frequency change rate.

[0185] Furthermore, the type of the export fluctuation segment can be searched in the mapping relationship between different fluctuation segment types and different filtering methods, and the filtering method corresponding to the fluctuation segment type that matches the type of the export fluctuation segment in the mapping relationship can be obtained. The export frequency change rate value, each frequency change rate value generated before the signal exit point, and at least one latest frequency change rate value generated after the signal exit point can be filtered and processed according to the obtained filtering method to obtain multiple first frequency change rate values.

[0186] Among them, if the type of the exit fluctuation section is an ascending section, the first modified median filtering algorithm is used to screen and process the exit frequency change rate value, the frequency change rate values ​​generated before the signal exit point, and at least one latest frequency change rate value generated after the signal exit point, so as to obtain multiple second frequency change rate values; if the type of the exit fluctuation section is a descending section, the second modified median filtering algorithm is used to screen and process the exit frequency change rate value, the frequency change rate values ​​generated before the signal exit point, and at least one latest frequency change rate value generated after the signal exit point, so as to obtain multiple second frequency change rate values. The first modified median filtering algorithm and the second modified median filtering algorithm here are similar to the algorithms in the above-mentioned embodiments, and will not be repeated here.

[0187] S800 , performing Kalman filtering on each second frequency change rate value to obtain a target frequency change rate value of the input signal within a detection period.

[0188] Furthermore, a Kalman filter may be called to perform Kalman filtering processing on each second frequency change rate value using a Kalman filtering algorithm to obtain a target frequency change rate value of the input signal within a detection period.

[0189] In the embodiment of the present application, the implementation process of step S800 is similar to the implementation process of the above 530. The specific implementation process of the above S800 is described below:

[0190] (1) Obtain multiple optimal frequency change rate values ​​x of the input signal at the signal exit point at the previous moment (i.e., moment (k-1)) k-1 ;

[0191] (2) Based on the multiple optimal frequency change rate values ​​x at the previous moment (i.e., moment (k-1)) k-1 Predict multiple frequency change rate values ​​x' at the current time (i.e. time x) k , that is, x′ k =A*x k-1 +B*u k-1 ;

[0192] (3) According to the error covariance P at time (k-1) k-1 and the process noise Q predicts the error covariance P′ at time k k , that is, P′ k =A*P k-1 *A T +Q; where P 0 is equal to 0;

[0193] (4) According to the predicted error covariance P′ at time k k , calculate the Kalman gain K at time k k , that is, K k =P′ k *H T / (H*P′ k *H T +R); where R represents the observation noise covariance;

[0194] (5) Based on the predicted multiple frequency change rate values ​​k′ k , for each first frequency change rate value z at time k k Perform correction and update to obtain the target frequency change rate value x of the input signal at the signal exit point at time k k , that is, x k = x′ k +K k *(z k -H*x′ k ).

[0195] It should be noted here that, since the frequency change rate is usually 0 or non-0, a one-dimensional Kalman filter algorithm can be used to perform accurate Kalman filtering on each second frequency change rate value. Naturally, in the Kalman filter processing, the above A is equal to 1, B is equal to 0, and H is equal to 1.

[0196] At the same time, according to the Kalman gain K at time k k The error covariance P′ for the predicted k-th time k Update and get the error covariance P at time k k , that is, P k =(1-K k )*P′ k , in order to provide dependency information for optimizing multiple optimal frequency change rate values ​​of the input signal at the signal exit point at the next moment (i.e., moment (k+1)).

[0197] In practical applications, the above process noise Q can be expressed as:

[0198]

[0199] Wherein, Q′ is a process noise coefficient. The Q′ corresponding to the signal exit point can be determined by the user or based on historical experience. In the embodiment of the present application, the Q′ corresponding to the signal exit area is much smaller than the Q corresponding to the signal entry area. ′ , the Q corresponding to the above signal exit area ′ It can be equal to 0.001.

[0200] The technical solution in the embodiment of the present application performs Kalman filtering on the multiple second frequency change rate values ​​corresponding to the detection period to obtain the target frequency change rate value of the input signal within the detection period; the above method can obtain the optimized frequency change rate value within the detection period to achieve the application of adjusting the input signal of the target object or evaluating the stability of the target object, thereby improving the stability and working performance of the target object.

[0201] In some scenarios, the frequency change rate value of a period of time within the detection cycle is constantly decreasing or increasing. This section is called the frequency change rate fluctuation section. The following describes the process of obtaining the export frequency change rate value corresponding to the frequency change rate fluctuation section, that is, the export frequency change rate value corresponding to the fluctuation section where the signal exit point is located. In one embodiment, Fig.14 As shown, the step of obtaining the exit frequency change rate value corresponding to the signal exit point within the detection period in the above S600 can be implemented by the following steps:

[0202] S610. Obtain at least one fluctuation segment frequency change rate value corresponding to the fluctuation segment where the signal exit point is located.

[0203] The above-mentioned fluctuation segment frequency change rate value may be at least one frequency change rate value corresponding to the fluctuation segment including the signal exit point and the adjacent fluctuation segment before the signal exit point.

[0204] S620. If the derivatives of the frequency change rate values ​​of each fluctuation segment are greater than the preset error threshold, the fitting value corresponding to the fluctuation segment where the signal exit point is located is determined as the exit frequency change rate value; otherwise, the frequency change rate value corresponding to the fluctuation segment where the signal exit point is located is determined as the exit frequency change rate value.

[0205] In practical applications, computer equipment can screen and process the fitting values ​​and frequency change rate values ​​corresponding to the adjacent fluctuation segments before the signal exit point according to the frequency change rate values ​​of each fluctuation segment, and determine the exit frequency change rate values ​​corresponding to the signal exit point and the adjacent fluctuation segments before the signal exit point.

[0206] Specifically, the computer device can select any value from the fitting value and the frequency change rate value corresponding to the fluctuation segment where the signal exit point is located as the export frequency change rate value corresponding to the fluctuation segment where the signal exit point is located according to the size range of the frequency change rate value of each fluctuation segment and the preset screening strategy.

[0207] It should be noted here that the exit frequency change rate value and the entry frequency change rate value can be the same or different. The difference between the exit frequency change rate value and the entry frequency change rate value can be understood as a large difference in time between the signal entry point and the signal exit point. When processing the fluctuation segment where the signal exit point is located, the relevant signal corresponding to a fluctuation segment where the signal entry point is located can be filtered out. Optionally, the fluctuation segment where the signal exit point is located and the fluctuation segment where the signal entry point is located can be the same fluctuation segment or adjacent fluctuation segments.

[0208] In an embodiment of the present application, when it is determined that the derivatives of the frequency change rate values ​​of each fluctuation segment are greater than a preset error threshold, the fitting value corresponding to the fluctuation segment where the signal exit point is located is determined as the exit frequency change rate value; when it is determined that the derivatives of the frequency change rate values ​​of each fluctuation segment are less than or equal to the preset error threshold, the frequency change rate value corresponding to the adjacent fluctuation segment before the signal exit point is determined as the exit frequency change rate value.

[0209] In this embodiment, at least one frequency change rate value of the fluctuation segment corresponding to the fluctuation segment where the signal exit point is located is obtained. If the derivatives of the frequency change rate values ​​of each fluctuation segment are greater than the preset error threshold, the fitting value corresponding to the fluctuation segment where the signal exit point is located is determined as the exit frequency change rate value; otherwise, the frequency change rate value corresponding to the fluctuation segment where the signal exit point is located is determined as the exit frequency change rate value. The above method can filter the exit frequency change rate value corresponding to the fluctuation segment where the signal exit point is located according to the relationship between the derivatives of the frequency change rate values ​​of each fluctuation segment and the preset error threshold. The processing process is relatively simple, which reduces the complexity of filtering the exit frequency change rate value, and thus can improve the filtering speed and efficiency of the exit frequency change rate.

[0210] In one embodiment, the present application also provides a physical property value detection method, which includes the following process:

[0211] (1) a physical characteristic value detection instruction in response to an input signal of a target object; the target object is an electronic device or any hardware circuit in the electronic device;

[0212] (2) calling a physical characteristic value detection model to obtain multiple adjacent signals corresponding to each zero-crossing point of the input signal in each half cycle within the detection cycle; the physical characteristic value detection model is a model for realizing physical characteristic value detection based on multiple sampling signals on the input signal;

[0213] (3) performing sliding window filtering according to each sampling frequency, each adjacent signal and each zero-crossing time to obtain multiple frequency values ​​corresponding to each frequency sliding window within the detection period;

[0214] (4) performing sliding window filtering processing according to each frequency value, the total duration of the detection cycle, and the duration of each half cycle to obtain multiple frequency change rate values ​​corresponding to each frequency change rate sliding window within the detection cycle; the size of the frequency change rate sliding window is larger than the size of the frequency sliding window;

[0215] (5) obtaining multiple sets of information to be fitted corresponding to each half cycle, and performing fitting processing on each set of information to be fitted to determine multiple fitting values; the fitting value represents the frequency change rate value of the fitting;

[0216] (6) averaging each fitting value to obtain a plurality of fitting means; and averaging each frequency change rate value to obtain a plurality of sliding window means;

[0217] (7) If the absolute value of the differential sliding window value corresponding to each fitting mean is greater than a first preset threshold value, and each fitting mean is greater than a target sliding window mean, then determining the type of the frequency change rate of the input signal in the entry fluctuation section according to the differential sliding window value, and obtaining at least one latest entry frequency change rate value generated by the input signal after the signal entry point;

[0218] (8) If the derivatives of the latest input frequency change rate values ​​are all greater than the preset error threshold, the fitting value corresponding to the fluctuation segment where the signal entry point is located is determined as the input frequency change rate value; otherwise, the frequency change rate value corresponding to the fluctuation segment where the signal entry point is located is determined as the input frequency change rate value;

[0219] (9) screening the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and at least one latest frequency change rate value generated after the signal entry point according to the type of the entry fluctuation segment to determine a plurality of first frequency change rate values;

[0220] (10) determining process noise according to each first frequency change rate value and a historical frequency change rate value of the input signal; the historical frequency change rate value is a target frequency change rate value at a historical moment;

[0221] (11) Based on the process noise, perform Kalman filtering on each first frequency change rate value to obtain a target frequency change rate value;

[0222] (12) if the absolute value of the latest differential sliding window value is less than a second preset threshold, obtaining at least one fluctuation segment frequency change rate value corresponding to the fluctuation segment where the signal exit point is located;

[0223] (13) If the derivatives of the frequency change rate values ​​of each fluctuation segment are greater than the preset error threshold, the fitting value corresponding to the fluctuation segment where the signal exit point is located is determined as the exit frequency change rate value; otherwise, the frequency change rate value corresponding to the fluctuation segment where the signal exit point is located is determined as the exit frequency change rate value;

[0224] (14) screening the export frequency change rate value, the frequency change rate values ​​generated before the signal export point, and at least one latest frequency change rate value generated after the signal export point according to the type of the frequency change rate of the input signal in the export fluctuation section, and determining a plurality of second frequency change rate values;

[0225] (15) Perform Kalman filtering on each second frequency change rate value to obtain a target frequency change rate value of the input signal within a detection period.

[0226] The execution process of the above (1) to (15) can be specifically referred to the description of the above embodiment. The implementation principles and technical effects are similar and will not be repeated here.

[0227] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0228] Based on the same inventive concept, the embodiment of the present application also provides a physical characteristic value detection device for implementing the physical characteristic value detection method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more physical characteristic value detection device embodiments provided below can refer to the limitations of the physical characteristic value detection method above, and will not be repeated here.

[0229] In one embodiment, Fig.15 This is a schematic diagram of the structure of a physical property value detection device in one embodiment of the present application. The physical property value detection device provided in the embodiment of the present application can be applied to computer equipment. Fig.15 As shown, the physical characteristic value detection device of the embodiment of the present application may include: an instruction response module 11 and a model calling module 12, wherein:

[0230] The instruction response module 11 is used to respond to the physical characteristic value detection instruction of the input signal of the target object; the target object is an electronic device or any hardware circuit in the electronic device;

[0231] The model calling module 12 is used to call the physical characteristic value detection model to detect and process the physical characteristic value of the input signal of the target object within the detection period; the physical characteristic value detection model is a model that realizes physical characteristic value detection based on multiple sampling signals on the input signal.

[0232] The physical property value detection device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned physical property value detection method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0233] In one embodiment, the detection cycle includes multiple half cycles, and the multiple sampling signals include multiple adjacent signals corresponding to each zero-crossing point on the input signal; the model calling module 12 includes: a signal acquisition unit and a filtering processing unit, wherein:

[0234] A signal acquisition unit, used for acquiring a plurality of adjacent signals corresponding to each zero-crossing point of the input signal in each half cycle within a detection cycle;

[0235] The filtering processing unit is used to perform filtering processing according to the sampling frequency, each adjacent signal and each zero-crossing time in each half cycle, and determine multiple physical characteristic values ​​of the input signal in the detection cycle to complete the detection processing.

[0236] The physical property value detection device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned physical property value detection method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0237] In one embodiment, the multiple physical characteristic values ​​include multiple frequency values ​​and multiple frequency change rate values; the filtering processing unit includes: a first filtering processing subunit and a second filtering processing subunit, wherein:

[0238] A first filtering processing subunit is used to perform sliding window filtering according to each sampling frequency, each adjacent signal and each zero-crossing time, so as to obtain a plurality of frequency values ​​corresponding to each frequency sliding window within a detection period;

[0239] The second filtering processing subunit is used to perform sliding window filtering according to each frequency value, the total duration of the detection cycle and the duration of each half cycle to obtain multiple frequency change rate values ​​corresponding to each frequency change rate sliding window within the detection cycle; the size of the frequency change rate sliding window is larger than the size of the frequency sliding window.

[0240] The physical property value detection device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned physical property value detection method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0241] In one embodiment, the model calling module 12 further includes: a fitting processing unit, a mean value calculation unit and an optimization processing unit, wherein:

[0242] A fitting processing unit is used to obtain multiple groups of information to be fitted corresponding to each half cycle, and perform fitting processing on each group of information to be fitted to determine multiple fitting values; the fitting value represents the frequency change rate value of the fitting;

[0243] A mean value calculation unit is used to calculate the mean of each fitting value to obtain a plurality of fitting means; and calculate the mean of each frequency change rate value to obtain a plurality of sliding window means;

[0244] The optimization processing unit is used to optimize each frequency change rate value according to each fitting mean and each sliding window mean, so as to obtain a target frequency change rate value corresponding to a signal fluctuation point within a detection period.

[0245] The physical property value detection device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned physical property value detection method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0246] In one embodiment, the signal fluctuation point includes a signal entry point; the optimization processing unit includes: a first acquisition subunit, a first screening subunit and a first filtering processing subunit, wherein:

[0247] A first acquisition subunit is used to determine the type of the frequency change rate of the input signal in the entry fluctuation section according to the differential sliding window value if the absolute value of the differential sliding window value corresponding to each fitting mean value is greater than a first preset threshold value, and each fitting mean value is greater than a target sliding window mean value, and to acquire at least one latest entry frequency change rate value generated by the input signal after the signal entry point;

[0248] A first screening subunit is used to screen the fitting value and each frequency change rate value corresponding to the fluctuation segment where the signal entry point is located according to each latest entry frequency change rate value and the type of the entry fluctuation segment, and determine a plurality of first frequency change rate values;

[0249] The first filtering processing subunit is used to perform Kalman filtering on each first frequency change rate value to obtain a target frequency change rate value of the input signal.

[0250] The physical property value detection device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned physical property value detection method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0251] In one embodiment, the first screening subunit includes: an entry value determination subunit and a first value determination subunit, wherein:

[0252] The entry value determination subunit is used to screen the fitting value and the frequency change rate value corresponding to the fluctuation section where the signal entry point is located according to each latest entry frequency change rate value, and determine the entry frequency change rate value corresponding to the fluctuation section where the signal entry point is located;

[0253] The first value determination subunit is used to screen and process the entry frequency change rate value, the frequency change rate values ​​generated before the signal entry point, and at least one latest frequency change rate value generated after the signal entry point according to the type of the entry fluctuation segment, so as to determine multiple first frequency change rate values.

[0254] The physical property value detection device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned physical property value detection method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0255] In one embodiment, the entry value determination subunit is specifically used for:

[0256] If the derivatives of the latest input frequency change rate values ​​are all greater than the preset error threshold, the fitting value corresponding to the fluctuation segment where the signal entry point is located is determined as the input frequency change rate value; otherwise, the frequency change rate value corresponding to the fluctuation segment where the signal entry point is located is determined as the input frequency change rate value.

[0257] The physical property value detection device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned physical property value detection method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0258] In one embodiment, the first filtering processing subunit is specifically used for:

[0259] Determine the process noise according to each first frequency change rate value and the historical frequency change rate value of the input signal; the historical frequency change rate value is the target frequency change rate value at the historical moment;

[0260] Based on the process noise, Kalman filtering is performed on each first frequency change rate value to obtain a target frequency change rate value.

[0261] The physical property value detection device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned physical property value detection method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0262] In one embodiment, the signal fluctuation point also includes a signal exit point; the optimization processing unit also includes: a second acquisition subunit and a second screening subunit, wherein:

[0263] A second acquisition subunit is used to obtain the exit frequency change rate value corresponding to the fluctuation segment where the signal exit point is located within the detection period if the absolute value of the latest differential sliding window value is less than a second preset threshold;

[0264] A second screening subunit is used to screen the outlet frequency change rate value, each frequency change rate value generated before the signal outlet point, and at least one latest frequency change rate value generated after the signal outlet point according to the type of the outlet fluctuation segment, and determine a plurality of second frequency change rate values;

[0265] The second filtering processing subunit is used to perform Kalman filtering on each second frequency change rate value to obtain a target frequency change rate value of the input signal at a signal exit point.

[0266] The physical property value detection device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned physical property value detection method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0267] In one embodiment, the second acquisition subunit is specifically used for:

[0268] Obtain at least one fluctuation segment frequency change rate value corresponding to the fluctuation segment where the signal exit point is located;

[0269] If the derivatives of the frequency change rate values ​​of each fluctuation segment are greater than the preset error threshold, the fitting value corresponding to the fluctuation segment where the signal exit point is located is determined as the exit frequency change rate value; otherwise, the frequency change rate value corresponding to the fluctuation segment where the signal exit point is located is determined as the exit frequency change rate value.

[0270] The physical property value detection device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned physical property value detection method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0271] For the specific definition of the physical characteristic value detection device, please refer to the definition of the physical characteristic value detection method above, which will not be repeated here. Each module in the above-mentioned physical characteristic value detection device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0272] In one embodiment, a computer device is also provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.16 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide processing power. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store multiple adjacent signals corresponding to each zero crossing point on the input signal of the target object within the detection cycle. The network interface of the computer device is used to communicate with an external endpoint through a network connection. When the computer program is executed by the processor, a physical characteristic value detection method is implemented.

[0273] Those skilled in the art will understand that Fig.16 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0274] In one embodiment, a computer device is also provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the technical solution in the above-mentioned physical characteristic value detection method embodiment of the present application is implemented. The implementation principle and technical effect are similar and will not be repeated here.

[0275] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed by a processor, the technical solution of the above-mentioned physical characteristic value detection method of the present application is implemented. The implementation principle and technical effect are similar and will not be repeated here.

[0276] In one embodiment, a computer program product is also provided, including a computer program. When the computer program is executed by a processor, the technical solution of the above-mentioned physical characteristic value detection method of the present application is implemented. The implementation principle and technical effect are similar and will not be repeated here.

[0277] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0278] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0279] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for detecting a physical property value, characterized in that: The method comprises: A physical characteristic value detection instruction in response to an input signal of a target object; the target object is an electronic device or any hardware circuit in the electronic device; The physical characteristic value detection model is called to detect and process the physical characteristic value of the input signal of the target object within the detection period; the physical characteristic value detection model is a model that realizes physical characteristic value detection based on multiple sampling signals on the input signal.

2. The method according to claim 1, characterized in that The detection cycle includes a plurality of half cycles, and the plurality of sampling signals include a plurality of adjacent signals corresponding to each zero-crossing point on the input signal; the detection and processing of the physical characteristic value of the input signal of the target object within the detection cycle includes: Acquire a plurality of adjacent signals corresponding to the zero-crossing points in each half cycle of the input signal within a detection cycle; Filtering is performed according to the sampling frequency in each half cycle, each adjacent signal and each zero-crossing time, and multiple physical characteristic values ​​of the input signal in the detection cycle are determined to complete the detection process.

3. The method according to claim 2, characterized in that The multiple physical characteristic values ​​include multiple frequency values ​​and multiple frequency change rate values; the filtering process is performed according to the sampling frequency in each half cycle, each adjacent signal and each zero-crossing time to determine the multiple physical characteristic values ​​of the input signal in the detection cycle, including: Perform sliding window filtering according to each of the sampling frequencies, each of the adjacent signals and each of the zero-crossing times to obtain a plurality of frequency values ​​corresponding to each frequency sliding window within the detection period; Sliding window filtering is performed according to each of the frequency values, the total duration of the detection cycle and the duration of each half cycle to obtain multiple frequency change rate values ​​corresponding to each frequency change rate sliding window within the detection cycle; the size of the frequency change rate sliding window is larger than the size of the frequency sliding window.

4. The method according to claim 2, characterized in that: The method further comprises: Acquire multiple groups of information to be fitted corresponding to each of the half-cycles, and perform fitting processing on each group of information to be fitted to determine multiple fitting values; the fitting values ​​represent the fitted frequency change rate values; Averaging the fitting values ​​to obtain a plurality of fitting means; and averaging the frequency change rate values ​​to obtain a plurality of sliding window means; According to each of the fitting means and each of the sliding window means, each of the frequency change rate values ​​is optimized to obtain a target frequency change rate value within the detection period.

5. The method according to claim 4, characterized in that The signal fluctuation point includes a signal entry point; the optimizing processing of each frequency change rate value according to each fitting mean and each sliding window mean to obtain a target frequency change rate value within the detection period includes: If the absolute value of the differential sliding window value corresponding to each of the fitting means is greater than a first preset threshold value, and each of the fitting means is greater than a target sliding window mean, then determining the type of the frequency change rate of the input signal in the entry fluctuation section according to the differential sliding window value, and obtaining at least one latest entry frequency change rate value generated by the input signal after the signal entry point; According to each of the latest entry frequency change rate values ​​and the type of the entry fluctuation segment, the fitting value corresponding to the fluctuation segment where the signal entry point is located and each of the frequency change rate values ​​are screened to determine a plurality of first frequency change rate values; Kalman filtering is performed on each of the first frequency change rate values ​​to obtain a target frequency change rate value of the input signal within the detection period.

6. The method according to claim 5, characterized in that The method of screening the fitting value corresponding to the fluctuation segment where the signal entry point is located and each of the frequency change rate values ​​according to each of the latest entry frequency change rate values ​​and the type of the entry fluctuation segment to determine a plurality of first frequency change rate values ​​includes: According to each of the latest entry frequency change rate values, the fitting value and the frequency change rate value corresponding to the fluctuation section where the signal entry point is located are screened and processed to determine the entry frequency change rate value corresponding to the fluctuation section where the signal entry point is located; According to the type of the entry fluctuation segment, the entry frequency change rate value, each frequency change rate value generated before the signal entry point, and at least one latest frequency change rate value generated after the signal entry point are screened to determine the multiple first frequency change rate values; Correspondingly, the fitting value and the frequency change rate value corresponding to the fluctuation section where the signal entry point is located are screened according to each of the latest entry frequency change rate values ​​to determine the entry frequency change rate value corresponding to the fluctuation section where the signal entry point is located, including: If the derivatives of the latest input frequency change rate values ​​are all greater than the preset error threshold, the fitting value corresponding to the fluctuation segment where the signal entry point is located is determined as the input frequency change rate value; otherwise, the frequency change rate value corresponding to the fluctuation segment where the signal entry point is located is determined as the input frequency change rate value.

7. The method according to claim 5, characterized in that The performing Kalman filtering on each of the first frequency change rate values ​​to obtain a target frequency change rate value of the input signal within the detection period includes: Determine the process noise according to each of the first frequency change rate values ​​and the historical frequency change rate value of the input signal at the signal entry point; the historical frequency change rate value is the target frequency change rate value at a historical moment; Based on the process noise, Kalman filtering is performed on each of the first frequency change rate values ​​to obtain the target frequency change rate value.

8. The method according to claim 5, characterized in that The signal fluctuation point also includes a signal exit point; the method also includes: If the absolute value of the latest differential sliding window value is less than the second preset threshold, then obtaining the exit frequency change rate value corresponding to the fluctuation segment where the signal exit point in the detection period is located; According to the type of the frequency change rate of the input signal in the exit fluctuation section, the exit frequency change rate value, the frequency change rate values ​​generated before the signal exit point, and at least one latest frequency change rate value generated after the signal exit point are screened to determine a plurality of second frequency change rate values; Kalman filtering is performed on each of the second frequency change rate values ​​to obtain a target frequency change rate value of the input signal within the detection period.

9. The method according to claim 8, characterized in that The obtaining of the exit frequency change rate value corresponding to the fluctuation section where the signal exit point in the detection period is located includes: Obtain at least one fluctuation segment frequency change rate value corresponding to the fluctuation segment where the signal exit point is located; If the derivatives of the frequency change rate values ​​of each fluctuation segment are greater than the preset error threshold, the fitting value corresponding to the fluctuation segment where the signal exit point is located is determined as the exit frequency change rate value; otherwise, the frequency change rate value corresponding to the fluctuation segment where the signal exit point is located is determined as the exit frequency change rate value.

10. A physical property value detection device, characterized in that: The device comprises: An instruction response module, used to respond to a physical characteristic value detection instruction of an input signal of a target object; the target object is an electronic device or any hardware circuit in the electronic device; The model calling module is used to call the physical characteristic value detection model to detect and process the physical characteristic value of the input signal of the target object within the detection period; the physical characteristic value detection model is a model that realizes physical characteristic value detection based on multiple sampling signals on the input signal.

11. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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