Direct current signal processing method and device, equipment and medium

Abstract

The embodiment of the invention discloses a DC signal processing method and device, equipment and a medium, and relates to the technical field of DC measurement. The method comprises the following steps: carrying out quantity statistics on discrete signal sequences to obtain a discrete integer counting result; performing iterative calculation on parameters in the data distribution function based on a discrete integer counting result, and determining the data distribution function according to an iterative result of the parameters; and in each data distribution function, determining a specific independent variable of the selected data distribution function as a direct current value of the direct current signal to be processed. According to the technical scheme, the direct current signal to be processed is randomly sampled, so that the obtained discrete integer counting result can truly reflect the distribution condition of the current value, and then the direct current value is obtained according to the specific independent variable in the determined and selected data distribution function; the effect of obtaining the direct current through only one processing mode in various interference environments is achieved.

Description

Technical Field

[0001] The present invention relates to the field of direct current measurement technology, and in particular to a direct current signal processing method, device, equipment and medium. Background Art

[0002] Arrester resistive current measurement, equipment grounding resistance, equipment insulation characteristics measurement, etc. all involve DC current measurement. When measuring DC current, it is necessary to face a variety of interference environments. How to avoid environmental interference when measuring DC current is a technical problem that needs to be solved urgently.

[0003] The current technical solution is mainly to target different interference environments and set up interference elimination solutions based on the characteristics of the interference environment. However, this method will have the problem of poor detection result accuracy when dealing with rapidly changing interference signals or irregular interference signals. Summary of the invention

[0004] The present invention provides a method, device, equipment and medium for processing a direct current signal, which can accurately measure the direct current of a direct current signal under various interference environments by using a measurement method.

[0005] According to one aspect of the present invention, a method for processing a DC signal is provided, the method comprising:

[0006] Performing quantity statistics on a discrete signal sequence to obtain a discrete integer counting result; the discrete signal sequence is a signal sequence in which all values ​​obtained by randomly sampling the DC signal to be processed in the process of receiving the DC signal to be processed are integers;

[0007] Iteratively calculate the parameters in the data distribution function based on the discrete integer counting result, and determine the data distribution function according to the iterative result of the parameters; different data distribution functions correspond to different DC signal influencing factors;

[0008] Among the data distribution functions, a specific independent variable of the selected data distribution function is determined as the DC current value of the DC signal to be processed; the selected data distribution function is the data distribution function with the largest function value among the data distribution functions; the specific independent variable is the independent variable corresponding to the maximum value of the selected data distribution function.

[0009] According to another aspect of the present invention, there is provided a DC signal processing device, comprising:

[0010] A quantity statistics module is used to perform quantity statistics on a discrete signal sequence to obtain a discrete integer counting result; the discrete signal sequence is a signal sequence in which the values ​​obtained by randomly sampling the DC signal to be processed in the process of receiving the DC signal to be processed are all integers;

[0011] A data distribution function determination module is used to iteratively calculate the parameters in the data distribution function based on the discrete integer counting result, and determine the data distribution function according to the iterative result of the parameters; different data distribution functions correspond to different DC signal influencing factors;

[0012] A DC current value determination module is used to determine a specific independent variable of a selected data distribution function among various data distribution functions as the DC current value of the DC signal to be processed; the selected data distribution function is the data distribution function with the largest function value among various data distribution functions; the specific independent variable is the independent variable corresponding to the maximum value of the selected data distribution function.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the DC signal processing method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the DC signal processing method described in any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present application includes: performing quantitative statistics on a discrete signal sequence to obtain a discrete integer counting result; the discrete signal sequence is a signal sequence whose values ​​are all integers obtained by randomly sampling the DC signal to be processed during the process of receiving the DC signal to be processed; iteratively calculating the parameters in the data distribution function based on the discrete integer counting result, and determining the data distribution function according to the iterative result of the parameters; different data distribution functions correspond to different DC signal influencing factors; in each data distribution function, a specific independent variable of the selected data distribution function is determined as the DC current value of the DC signal to be processed; the selected data distribution function is the data distribution function with the largest function value in each data distribution function; the specific independent variable is the independent variable corresponding to the maximum value of the selected data distribution function. The technical solution randomly samples the DC signal to be processed so that the obtained discrete integer counting result can truly reflect the distribution of the current value, and then obtains the DC current value by determining the specific independent variable in the selected data distribution function, so as to achieve the effect of obtaining the DC current by only one processing method under various interference environments.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 is a flow chart of a method for processing a DC signal provided according to Embodiment 1 of the present application;

[0022] Figure 2 is a flow chart of a method for processing a DC signal provided according to Embodiment 2 of the present application;

[0023] Figure 3 is a schematic diagram of a data distribution function provided according to Embodiment 2 of the present application;

[0024] Figure 4 is a structural schematic diagram of a DC signal processing device provided according to Embodiment 3 of the present application;

[0025] Figure 5 It is a structural schematic diagram of an electronic device for implementing a direct current signal processing method of an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only embodiments of a part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", "target", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1 A flowchart of a method for processing a DC signal is provided for the first embodiment of the present application. The embodiment of the present application is applicable to the case of measuring a DC current. The method can be executed by a DC signal processing device. The DC signal processing device can be implemented in the form of hardware and / or software. The DC signal processing device can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0030] S110, performing quantity statistics on the discrete signal sequence to obtain a discrete integer counting result.

[0031] Wherein, the discrete signal sequence is a signal sequence whose values ​​are all integers obtained by randomly sampling the DC signal to be processed during the process of receiving the DC signal to be processed. Since the discrete signal sequence is obtained by randomly sampling the DC signal to be processed, the values ​​in the discrete signal sequence are discrete, and since the DC signals to be processed are all integers, the values ​​in the discrete signal sequence are all integers.

[0032] Exemplarily, the discrete integer counting result includes an integer value and a counting result corresponding to the integer value, and the counting result reflects the number of integer values. For example, the integer values ​​are 5, 8, 10, 11, and 12; the counting result corresponding to the integer value 5 is 14, the counting result corresponding to the integer value 8 is 18, the counting result corresponding to the integer value 10 is 20, the counting result corresponding to the integer value 11 is 14, and the counting result corresponding to the integer value 12 is 10. It should be noted that the above discrete integer counting results are only specific examples, and the actual amount of data is usually much larger than the above examples.

[0033] In an embodiment of the present application, optionally, the process of determining the discrete signal sequence includes: amplifying the analog signal continuously received from the DC sensor, and performing analog-to-digital conversion on the amplified signal to obtain a time series of DC signals to be processed; and randomly sampling the time series of DC signals to be processed based on a random pulse sequence to obtain a discrete signal sequence whose values ​​are all integers.

[0034] Specifically, the analog signal sent by the DC sensor is continuously received, and the analog signal reflects the size of the DC current. The analog signal is amplified, and the amplified signal is converted into an analog-to-digital value, and the digital signals converted during the analog-to-digital conversion are all integer values, so as to obtain a time series of DC signals to be processed; a random pulse sequence is obtained, in which, as time goes by, the appearance of each pulse is random, and when each pulse appears, the time series of DC signals to be processed is obtained, random sampling is implemented, and the obtained values ​​are all discrete signal sequences of integers.

[0035] The present solution is configured in such a way that the discrete signal sequence is randomly extracted from the DC signal to be processed. Since the extraction process is performed randomly, there will be no influence caused by the extraction method (such as periodic extraction).

[0036] In an embodiment of the present application, optionally, the process of determining the random pulse sequence includes: generating a pseudo-random number sequence; if the pseudo-random number in the pseudo-random number sequence is greater than a specified constant, assigning the pseudo-random number in the pseudo-random number sequence a value of 1; otherwise, assigning a value of 0; and determining the pseudo-random number sequence as a random pulse sequence.

[0037] Specifically, a pseudo-random number sequence is generated, and then the size of each value in the pseudo-random number sequence and a specified constant is determined. If the pseudo-random number in the pseudo-random number sequence is greater than the specified constant, the pseudo-random number in the pseudo-random number sequence is assigned a value of 1; otherwise, the value is assigned to 0. The random pulse sequence obtained in this way can obtain a DC signal to be processed when the value is 1.

[0038] Furthermore, the specified constant can be pre-set. If the discrete integer counting result obtained presents an irregular distribution (for example, not a normal distribution), the value of the specified constant can be increased to reduce the sampling frequency, that is, the time to obtain the discrete signal sequence is lengthened. If the current is affected by external factors within a period of time, the influence of the influence on the discrete signal sequence can be reduced due to the reduction in the number of samples during this time.

[0039] S120, iteratively calculating the parameters in the data distribution function based on the discrete integer counting result, and determining the data distribution function according to the iterative result of the parameters.

[0040] Among them, different data distribution functions correspond to different DC signal influencing factors.

[0041] Specifically, the discrete integer counting result reflects the result of the combined effect of multiple DC signal influencing factors, such as real current, noise, etc., and the data distribution function corresponding to different DC signal influencing factors can be determined, and then the specific data distribution function is determined based on the discrete integer counting result, so as to calculate the real current through subsequent steps. Exemplarily, the data distribution function can be a Gaussian function.

[0042] S130, determining a specific independent variable of a selected data distribution function among the data distribution functions as a DC current value of the DC signal to be processed.

[0043] The selected data distribution function is the data distribution function with the largest function value among the data distribution functions; the specific independent variable is the independent variable corresponding to the maximum value of the selected data distribution function.

[0044] Specifically, since in the data distribution function, the independent variable reflects the current size, and the function value reflects the number of times the current is detected, the real current is sampled more times, that is, the function value corresponding to the real current in the data distribution function is larger, so, in each data distribution function, the maximum value of each data distribution function is determined, and then the function corresponding to the maximum value of each maximum value is determined as the selected data distribution function, and the independent variable corresponding to the maximum value of the selected data distribution function is determined as the specific independent variable, and the specific independent variable can be determined as the DC current value of the DC current signal to be processed. It should be noted that since the current value is within a certain range, each data distribution function can limit the independent variable range according to the current value range.

[0045] The technical solution of the embodiment of the present application includes: performing quantitative statistics on a discrete signal sequence to obtain a discrete integer counting result; the discrete signal sequence is a signal sequence whose values ​​are all integers obtained by randomly sampling the DC signal to be processed during the process of receiving the DC signal to be processed; iteratively calculating the parameters in the data distribution function based on the discrete integer counting result, and determining the data distribution function according to the iterative result of the parameters; different data distribution functions correspond to different DC signal influencing factors; in each data distribution function, a specific independent variable of the selected data distribution function is determined as the DC current value of the DC signal to be processed; the selected data distribution function is the data distribution function with the largest function value in each data distribution function; the specific independent variable is the independent variable corresponding to the maximum value of the selected data distribution function. The technical solution randomly samples the DC signal to be processed so that the obtained discrete integer counting result can truly reflect the distribution of the current value, and then obtains the DC current value by determining the specific independent variable in the selected data distribution function, so as to achieve the effect of obtaining the DC current by only one processing method under various interference environments.

[0046] Embodiment 2

[0047] Figure 2 This is a flow chart of a method for processing a DC signal provided in Embodiment 2 of the present application. Embodiment of the present application is optimized based on the above embodiment.

[0048] like Figure 2 As shown, the method of the embodiment of the present application specifically includes the following steps:

[0049] S210, when performing quantity statistics on a discrete signal sequence, if it is determined that the count quantity of integer values ​​in a current statistical result is greater than a preset quantity threshold, the current statistical result is determined as a discrete integer counting result.

[0050] Among them, the preset quantity threshold can be determined according to actual conditions, and the embodiments of the present application do not limit this.

[0051] The method further includes: if it is determined that the discrete integer counting result is a non-Gaussian distribution, increasing the preset quantity threshold.

[0052] The method further includes: if the initial function value corresponding to the integer value obtained in the subsequent step A2 satisfies the following conditions:

[0053]

[0054] Among them, k is the influencing factor, K is the number of influencing factors, and f k (n) is the initial function value corresponding to the integer n, and ε is a small constant, such as 10 -4 .

[0055] The integer values ​​are determined as discrete points, and then a part of the discrete signal sequence is obtained, so that the number of integer values ​​in the statistical result is equal to the preset number threshold.

[0056] S220, iteratively calculating the parameters in the data distribution function based on the discrete integer counting result, and determining the data distribution function according to the iterative result of the parameters.

[0057] In an embodiment of the present application, optionally, parameters in the data distribution function are iteratively calculated based on the discrete integer counting results, and the data distribution function is determined according to the iterative results of the parameters, including: iteratively calculating the standard deviation, central value and selection probability in the Gaussian function based on the discrete integer counting results, and determining the Gaussian function according to the iterative results of the parameters; different Gaussian functions correspond to different DC signal influencing factors.

[0058] Specifically, in the embodiment of the present application, the data distribution functions corresponding to different influencing factors are all Gaussian distributions, and the Gaussian distributions corresponding to different influencing factors are different. Since the standard deviation, the central value, and the selection probability (the selection probability can be called the weight) are determined in the Gaussian distribution, the specific Gaussian function can be determined, so the parameters of this solution are determined as the standard deviation, the central value, and the selection probability.

[0059] Specifically, the Gaussian function can be expressed as:

[0060] {f k (t,φ k )} k＝1,2,…,K ;

[0061] Among them, φ k =(σ k ,μ k ,w k ), σ k is the standard deviation, μ k is the center value, w k is the selection probability, t is the independent variable, and k is the influencing factor.

[0062]

[0063] Because of w k It is the coefficient of the function, also known as the weight.

[0064] In the embodiment of the present application, optionally, when determining the Gaussian function, the following constraints need to be met:

[0065]

[0066] Among them, n is the integer value in the discrete integer counting result, N-1 is the upper limit of the integer value, that is, the integer value is less than or equal to N-1, h n The count result of the integer value n.

[0067] In the embodiment of the present application, optionally, the standard deviation, the central value and the selection probability in the Gaussian function are iteratively calculated based on the discrete integer counting result, including steps A1-A6:

[0068] Step A1, initializing the standard deviation, center value and selection probability in the target Gaussian function to obtain the initialized standard deviation, initialized center value and initialized selection probability; the target Gaussian function is a Gaussian function corresponding to the target influencing factor.

[0069] Step A2, determine the initialization target Gaussian function according to the initialization standard deviation, the initialization center value and the initialization selection probability, and substitute the target integer value in the discrete integer counting result into the initialization target Gaussian function to obtain the target initial function value corresponding to the target integer value.

[0070] Step A3, determining the probability that the target integer value is classified as the target influencing factor according to the ratio of the target function value corresponding to the target integer value to the total function value; the total function value is the sum of the initial function values ​​corresponding to the target integer value under each influencing factor.

[0071] Step A4, determining a target calculated value based on the probability of each integer value being classified as a target influencing factor and the counting result of the integer value.

[0072] Step A5, determining the updated standard deviation, updated center value and updated selection probability after this iteration according to the target calculation value and the discrete integer counting result.

[0073] Step A6, if the updated standard deviation, updated center value and updated selection probability meet the iteration end condition, the updated standard deviation, updated center value and updated selection probability are determined as the final standard deviation, final center value and final selection probability; otherwise, the updated standard deviation, updated center value and updated selection probability are determined as the initialized standard deviation, initialized center value and initialized selection probability for the next iteration.

[0074] Accordingly, the Gaussian function is determined according to the iterative results of the parameters, including:

[0075] The target Gaussian function is determined according to the final standard deviation, the final center value, and the final selection probability.

[0076] Specifically, we can first give an initial initialization standard deviation, an initialization center value, and an initialization selection probability to obtain an initialization target Gaussian function, substitute the target integer value in the discrete integer counting result into the initialization target Gaussian function, and obtain the target initial function value corresponding to the target integer value. After substituting each integer value, we can obtain the initial function value corresponding to each integer value. The following formula is the expression of the initial function value:

[0077] f k (n,φ k );

[0078] n is an integer value, φ k Includes: initialization standard deviation, initialization center value and initialization selection probability, where k is an influencing factor.

[0079] Furthermore, the total function value is determined according to the following formula:

[0080] Σ k f k (n,φ k );

[0081] This formula reflects the cumulative summation of the initial function values ​​corresponding to the target integer value under each influencing factor.

[0082] The probability of the target integer value being classified as the target influencing factor is determined according to the following formula:

[0083]

[0084] Among them, r k,n The probability that the target integer value n is classified as the target influencing factor k.

[0085] The target calculation value is determined according to the following formula:

[0086] r k =Σ n h n r k,n ;

[0087] Among them, r k is the target calculation value, Σ n is the cumulative sum, equivalent to h n is the counting result corresponding to the target integer value n.

[0088] In the embodiment of the present application, optionally, determining the updated standard deviation, updated center value, and updated selection probability after this iteration according to the target calculation value and the discrete integer counting result includes:

[0089] The update selection probability is determined according to the following formula:

[0090]

[0091] in, is the update selection probability, r k Calculate the target value, h n is the counting result corresponding to the integer value n in the discrete integer counting result;

[0092] The update center value is determined according to the following formula:

[0093]

[0094] in, is to update the center value, n is the integer value n in the discrete integer counting result, r k,n is the probability of an integer value n being classified as an influencing factor k;

[0095] The updated standard deviation is determined according to the following formula:

[0096]

[0097] in, is the updated standard deviation.

[0098] If the updated standard deviation, updated center value, and updated selection probability satisfy the following formula:

[0099]

[0100] Where δ is a small positive number specified by humans, such as (but not limited to) 10 -6 .

[0101] The updated standard deviation, updated center value and updated selection probability are determined as the final standard deviation, final center value and final selection probability; otherwise, the updated standard deviation, updated center value and updated selection probability are determined as the initialization standard deviation, initialization center value and initialization selection probability for the next iteration.

[0102] S230 , determining a specific independent variable of a selected data distribution function among the data distribution functions as a DC current value of the DC signal to be processed.

[0103] For example, Figure 3 is a schematic diagram of data distribution function, such as Figure 3 As shown, is the selected data distribution function.

[0104] This scheme is set up so that each Gaussian function, that is, each data distribution function, can be accurately calculated, and then the DC current value can be accurately determined according to the specific independent variable. Since the digital signal is an integer value output, but the specific independent variable can be a decimal, the effect of improving the current measurement accuracy is also achieved.

[0105] Embodiment 3

[0106] Figure 4 This is a schematic diagram of the structure of a DC signal processing device provided in Embodiment 3 of the present application. The device can execute the DC signal processing method provided in any embodiment of the present invention and has the corresponding functional modules and beneficial effects of the execution method. Figure 4 As shown, the device comprises:

[0107] The quantity statistics module 310 is used to perform quantity statistics on a discrete signal sequence to obtain a discrete integer counting result; the discrete signal sequence is a signal sequence whose values ​​are all integers obtained by randomly sampling the DC signal to be processed during the process of receiving the DC signal to be processed;

[0108] The data distribution function determination module 320 is used to iteratively calculate the parameters in the data distribution function based on the discrete integer counting result, and determine the data distribution function according to the iterative result of the parameters; different data distribution functions correspond to different DC signal influencing factors;

[0109] The DC current value determination module 330 is used to determine the specific independent variable of the selected data distribution function among each data distribution function as the DC current value of the DC signal to be processed; the selected data distribution function is the data distribution function with the largest function value among each data distribution function; the specific independent variable is the independent variable corresponding to the maximum value of the selected data distribution function.

[0110] The technical solution of the embodiment of the present application includes: a quantity statistics module 310, which is used to perform quantity statistics on a discrete signal sequence to obtain a discrete integer counting result; the discrete signal sequence is a signal sequence whose values ​​are all integers obtained by randomly sampling the DC signal to be processed during the process of receiving the DC signal to be processed; a data distribution function determination module 320, which is used to iteratively calculate the parameters in the data distribution function based on the discrete integer counting result, and determine the data distribution function according to the iterative result of the parameters; different data distribution functions correspond to different DC signal influencing factors; a DC current value determination module 330, which is used to determine the specific independent variable of the selected data distribution function in each data distribution function as the DC current value of the DC signal to be processed; the selected data distribution function is the data distribution function with the largest function value in each data distribution function; the specific independent variable is the independent variable corresponding to the maximum value of the selected data distribution function. The technical solution randomly samples the DC signal to be processed so that the discrete integer counting result obtained can truly reflect the distribution of the current value, and then obtains the DC current value by determining the specific independent variable in the selected data distribution function, so as to achieve the effect of obtaining the DC current by only one processing method under various interference environments.

[0111] In the embodiment of the present application, optionally, the data distribution function determination module 320 includes:

[0112] The Gaussian function determination unit is used to iteratively calculate the standard deviation, central value and selection probability in the Gaussian function based on the discrete integer counting result, and determine the Gaussian function according to the iterative result of the parameter; different Gaussian functions correspond to different DC signal influencing factors.

[0113] In the embodiment of the present application, optionally, the Gaussian function determination unit is specifically used to:

[0114] Initializing the standard deviation, the center value, and the selection probability in the target Gaussian function to obtain an initialized standard deviation, an initialized center value, and an initialized selection probability; the target Gaussian function is a Gaussian function corresponding to the target influencing factor;

[0115] Determine the initialization target Gaussian function according to the initialization standard deviation, the initialization center value and the initialization selection probability, and substitute the target integer value in the discrete integer counting result into the initialization target Gaussian function to obtain the target initial function value corresponding to the target integer value;

[0116] Determine the probability that the target integer value is classified as the target influencing factor according to the ratio of the target function value corresponding to the target integer value to the total function value; the total function value is the sum of the initial function values ​​corresponding to the target integer value under each influencing factor;

[0117] Determine a target calculation value based on the probability of each integer value being classified as a target influencing factor and the counting result of the integer value;

[0118] Determine the updated standard deviation, updated center value and updated selection probability after this iteration according to the target calculation value and the discrete integer counting result;

[0119] If the updated standard deviation, the updated center value, and the updated selection probability meet the iteration end condition, the updated standard deviation, the updated center value, and the updated selection probability are determined as the final standard deviation, the final center value, and the final selection probability;

[0120] Otherwise, the updated standard deviation, the updated center value, and the updated selection probability are determined as the initialization standard deviation, the initialization center value, and the initialization selection probability for the next iteration;

[0121] Accordingly, the Gaussian function is determined according to the iterative results of the parameters, including:

[0122] The target Gaussian function is determined according to the final standard deviation, the final center value, and the final selection probability.

[0123] In the embodiment of the present application, optionally, determining the updated standard deviation, updated center value, and updated selection probability after this iteration according to the target calculation value and the discrete integer counting result includes:

[0124] The update selection probability is determined according to the following formula:

[0125]

[0126] in, is the update selection probability, r k Calculate the target value, h n is the counting result corresponding to the integer value n in the discrete integer counting result;

[0127] The update center value is determined according to the following formula:

[0128]

[0129] in, is to update the center value, n is the integer value n in the discrete integer counting result, r k,n is the probability of an integer value n being classified as an influencing factor k;

[0130] The updated standard deviation is determined according to the following formula:

[0131]

[0132] in, is the updated standard deviation.

[0133] In the embodiment of the present application, optionally, the device further includes:

[0134] The analog-to-digital conversion module is used to amplify the analog signal continuously received from the DC sensor and perform analog-to-digital conversion on the amplified signal to obtain a time series of DC signals to be processed;

[0135] The random sampling module is used to randomly sample the DC signal to be processed in the time series based on the random pulse sequence, and the obtained values ​​are all discrete signal sequences of integers.

[0136] In the embodiment of the present application, optionally, the device further includes:

[0137] A pseudo-random number sequence generation module, used for generating a pseudo-random number sequence;

[0138] a pseudo-random number sequence adjustment module, configured to assign a value of 1 to the pseudo-random number in the pseudo-random number sequence if the pseudo-random number in the pseudo-random number sequence is greater than a specified constant;

[0139] Otherwise, the value is 0;

[0140] A pseudo-random number sequence is determined as a random pulse sequence.

[0141] In the embodiment of the present application, optionally, the quantity statistics module 310 includes:

[0142] The quantity statistics unit is used to determine the current statistical result as a discrete integer counting result if it is determined that the counting quantity of the integer value in the current statistical result is greater than a preset quantity threshold when performing quantity statistics on the discrete signal sequence.

[0143] A DC signal processing device provided in an embodiment of the present application can execute a DC signal processing method provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0144] Embodiment 4

[0145] Figure 5A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0146] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0147] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0148] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a method for processing a DC signal.

[0149] In some embodiments, the method for processing a DC signal may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for processing a DC signal described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for processing a DC signal in any other appropriate manner (e.g., by means of firmware).

[0150] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0151] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0152] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0153] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0154] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0155] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0156] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0157] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for processing a DC signal, characterized in that: include: Perform quantity statistics on discrete signal sequences to obtain discrete integer counting results; The discrete signal sequence is a signal sequence in which all the values ​​obtained by randomly sampling the DC signal to be processed are integers during the process of receiving the DC signal to be processed; Iteratively calculate the parameters in the data distribution function based on the discrete integer counting result, and determine the data distribution function according to the iterative result of the parameters; different data distribution functions correspond to different DC signal influencing factors; Among the data distribution functions, a specific independent variable of the selected data distribution function is determined as the DC current value of the DC signal to be processed; the selected data distribution function is the data distribution function with the largest function value among the data distribution functions; the specific independent variable is the independent variable corresponding to the maximum value of the selected data distribution function.

2. The method according to claim 1, characterized in that: Iteratively calculating the parameters in the data distribution function based on the discrete integer counting result, and determining the data distribution function according to the iterative result of the parameters, including: The standard deviation, central value and selection probability in the Gaussian function are iteratively calculated based on the discrete integer counting results, and the Gaussian function is determined according to the iterative results of the parameters; different Gaussian functions correspond to different DC signal influencing factors.

3. The method according to claim 2, characterized in that Iterative calculation of the standard deviation, central value and selection probability in the Gaussian function based on the discrete integer counting results, including: Initializing the standard deviation, the center value, and the selection probability in the target Gaussian function to obtain an initialized standard deviation, an initialized center value, and an initialized selection probability; the target Gaussian function is a Gaussian function corresponding to the target influencing factor; Determine the initialization target Gaussian function according to the initialization standard deviation, the initialization center value and the initialization selection probability, and substitute the target integer value in the discrete integer counting result into the initialization target Gaussian function to obtain the target initial function value corresponding to the target integer value; Determine the probability that the target integer value is classified as the target influencing factor according to the ratio of the target function value corresponding to the target integer value to the total function value; the total function value is the sum of the initial function values ​​corresponding to the target integer value under each influencing factor; Determine a target calculation value based on the probability of each integer value being classified as a target influencing factor and the counting result of the integer value; Determine the updated standard deviation, updated center value and updated selection probability after this iteration according to the target calculation value and the discrete integer counting result; If the updated standard deviation, the updated center value, and the updated selection probability meet the iteration end condition, the updated standard deviation, the updated center value, and the updated selection probability are determined as the final standard deviation, the final center value, and the final selection probability; Otherwise, the updated standard deviation, the updated center value, and the updated selection probability are determined as the initialization standard deviation, the initialization center value, and the initialization selection probability for the next iteration; Accordingly, the Gaussian function is determined according to the iterative results of the parameters, including: The target Gaussian function is determined according to the final standard deviation, the final center value, and the final selection probability.

4. The method according to claim 3, characterized in that The updated standard deviation, updated center value, and updated selection probability after this iteration are determined according to the target calculation value and the discrete integer counting result, including: The update selection probability is determined according to the following formula: in, is the update selection probability, r k Calculate the target value, h n is the counting result corresponding to the integer value n in the discrete integer counting result; The update center value is determined according to the following formula: in, is to update the center value, n is the integer value n in the discrete integer counting result, r k,n is the probability of an integer value n being classified as an influencing factor k; The updated standard deviation is determined according to the following formula: in, is the updated standard deviation.

5. The method according to claim 1, characterized in that The process of determining the discrete signal sequence includes: Amplifying the analog signal continuously received from the DC sensor, and performing analog-to-digital conversion on the amplified signal to obtain a time series of DC signals to be processed; Based on the random pulse sequence, the DC signal to be processed in the time series is randomly sampled, and the obtained values ​​are all discrete signal sequences of integers.

6. The method according to claim 5, characterized in that The process of determining the random pulse sequence includes: Generate a pseudo-random number sequence; If the pseudo-random number in the pseudo-random number sequence is greater than the specified constant, the pseudo-random number in the pseudo-random number sequence is assigned a value of 1; Otherwise, the value is 0; A pseudo-random number sequence is determined as a random pulse sequence.

7. The method according to claim 1, characterized in that Perform quantitative statistics on discrete signal sequences to obtain discrete integer counting results, including: When performing quantity statistics on a discrete signal sequence, if it is determined that the count quantity of integer values ​​in a current statistical result is greater than a preset quantity threshold, the current statistical result is determined as a discrete integer count result.

8. A DC signal processing device, characterized in that: include: A quantity statistics module is used to perform quantity statistics on discrete signal sequences to obtain discrete integer counting results; The discrete signal sequence is a signal sequence in which all the values ​​obtained by randomly sampling the DC signal to be processed are integers during the process of receiving the DC signal to be processed; A data distribution function determination module is used to iteratively calculate the parameters in the data distribution function based on the discrete integer counting result, and determine the data distribution function according to the iterative result of the parameters; different data distribution functions correspond to different DC signal influencing factors; A DC current value determination module is used to determine a specific independent variable of a selected data distribution function among various data distribution functions as the DC current value of the DC signal to be processed; the selected data distribution function is the data distribution function with the largest function value among various data distribution functions; the specific independent variable is the independent variable corresponding to the maximum value of the selected data distribution function.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for processing a DC signal according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for processing a DC signal according to any one of claims 1 to 7 when executed.

Images