Power signal coding method and device and electronic equipment

Abstract

The invention discloses a power signal coding method and device and electronic equipment. The method comprises the following steps: acquiring a plurality of initial power signals in a predetermined time period, the plurality of initial power signals being in one-to-one correspondence with a plurality of time nodes included in the predetermined time period; determining an initial frequency domain sequence corresponding to the plurality of initial power signals; according to an initial frequency domain sequence corresponding to the plurality of initial power signals, the number of times of occurrence of a plurality of predetermined distribution frequency domains is determined, and the plurality of predetermined distribution frequency domains are frequency domains occurring in the initial frequency domain sequence; and according to the plurality of predetermined distribution frequency domains and the respective occurrence times of the plurality of predetermined distribution frequency domains, performing coding operation to obtain target codes corresponding to the plurality of initial power signals. According to the invention, the technical problem of low coding efficiency in the coding process of a large amount of electric power signals collected by field operation in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular, to a power signal encoding method, apparatus, and electronic device. Background Art

[0002] In related technologies, before transmitting the power signals collected during field operations using wireless communication technologies, satellite communication technologies, etc., a large amount of data needs to be encoded. During the encoding process of a large number of power signals collected during field operations using existing encoding methods, there is a technical problem of low encoding efficiency.

[0003] In response to the above problems, no effective solution has been proposed yet. Summary of the Invention

[0004] Embodiments of the present invention provide a power signal encoding method, apparatus, and electronic device to at least solve the technical problem of low encoding efficiency during the encoding process of a large number of power signals collected during field operations in related technologies.

[0005] According to one aspect of the embodiments of the present invention, a power signal encoding method is provided, including: obtaining a plurality of initial power signals within a predetermined time period, where the plurality of initial power signals correspond one-to-one to a plurality of time nodes included in the predetermined time period; determining an initial frequency domain sequence corresponding to the plurality of initial power signals; determining the number of occurrences of a plurality of predetermined distribution frequency domains respectively based on the initial frequency domain sequence corresponding to the plurality of initial power signals, where the plurality of predetermined distribution frequency domains are the frequency domains that appear in the initial frequency domain sequence; and performing an encoding operation based on the plurality of predetermined distribution frequency domains and the number of occurrences of the plurality of predetermined distribution frequency domains respectively to obtain a target encoding corresponding to the plurality of initial power signals.

[0006] According to one aspect of the embodiments of the present invention, a power signal encoding apparatus is provided, including: an obtaining module, configured to obtain a plurality of initial power signals within a predetermined time period, where the plurality of initial power signals correspond one-to-one to a plurality of time nodes included in the predetermined time period; a first determining module, configured to determine an initial frequency domain sequence corresponding to the plurality of initial power signals; a second determining module, configured to determine the number of occurrences of a plurality of predetermined distribution frequency domains respectively based on the initial frequency domain sequence corresponding to the plurality of initial power signals, where the plurality of predetermined distribution frequency domains are the frequency domains that appear in the initial frequency domain sequence; and a third determining module, configured to perform an encoding operation based on the plurality of predetermined distribution frequency domains and the number of occurrences of the plurality of predetermined distribution frequency domains respectively to obtain a target encoding corresponding to the plurality of initial power signals.

[0007] According to one aspect of an embodiment of the present invention, there is provided an electronic device, including: a processor; a memory for storing executable instructions of the processor; wherein, the processor is configured to execute the instructions to implement the power signal encoding method described in any one of the above.

[0008] According to one aspect of an embodiment of the present invention, there is provided a computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute the power signal encoding method described in any one of the above.

[0009] In an embodiment of the present invention, a plurality of initial power signals within a predetermined time period are obtained, wherein the plurality of initial power signals correspond one-to-one to a plurality of time nodes included in the predetermined time period; an initial frequency domain sequence corresponding to the plurality of initial power signals is determined; according to the initial frequency domain sequence corresponding to the plurality of initial power signals, the number of occurrences of a plurality of predetermined distributed frequency domains is determined, wherein the plurality of predetermined distributed frequency domains are the frequency domains that appear in the initial frequency domain sequence; an encoding operation is performed according to the plurality of predetermined distributed frequency domains and the number of occurrences of the plurality of predetermined distributed frequency domains respectively, to obtain a target encoding corresponding to the plurality of initial power signals. By determining the initial frequency domain sequence corresponding to the plurality of initial power signals, the frequency information related to the true state of the plurality of initial power signals within the predetermined time period can be reflected, which helps to accurately and efficiently remove the noise or unnecessary frequency domain components in the initial power signals, making the processing of the initial power signals more efficient. By determining the plurality of predetermined distributed frequency domains, the subsequent encoding process can focus on the key frequency domains, thereby avoiding redundant encoding of irrelevant or unnecessary frequency domains. By performing the encoding operation according to the plurality of predetermined distributed frequency domains and the number of occurrences of the plurality of predetermined distributed frequency domains respectively, not only the key feature information of the initial power signals is retained, but also the redundant and unnecessary information is effectively removed, significantly reducing the amount of data to be encoded. Thus, while meeting the requirement of efficiently encoding the plurality of initial power signals, sufficient information can also be provided for data parsing and signal state evaluation, thereby solving the technical problem of low encoding efficiency in the related art during the encoding process of a large number of power signals collected from field operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings described herein are used to provide a further understanding of the present invention, and constitute a part of this application. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0011] Figure 1 is a flowchart of the power signal encoding method according to an embodiment of the present invention;

[0012] Figure 2It is the overall structure diagram of the monitoring system in an alternative embodiment of the present invention;

[0013] Figure 3 It is the schematic diagram of data decomposition and reconstruction in an alternative embodiment of the present invention;

[0014] Figure 4 It is the sectional schematic diagram of the signal cut-off frequency in an alternative embodiment of the present invention;

[0015] Figure 5 It is the structure diagram of the transformed hardware circuit of the digital tool end in an alternative embodiment of the present invention;

[0016] Figure 6 It is the scatter diagram of the original temperature data in an alternative embodiment of the present invention;

[0017] Figure 7 It is the flow chart of the original data compression coding in an alternative embodiment of the present invention;

[0018] Figure 8 It is the structure diagram of the GPS module of the digital tool end in an alternative embodiment of the present invention;

[0019] Figure 9 It is the flow chart of data transmission of the digital tool end in an alternative embodiment of the present invention;

[0020] Figure 10 It is the flow chart of the operation of the cloud platform working end in an alternative embodiment of the present invention;

[0021] Figure 11 It is the flow chart of vibration data compression coding in an alternative embodiment of the present invention;

[0022] Figure 12 It is the structural block diagram of the power signal coding device of the embodiment of the present invention. Specific Embodiments

[0023] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0024] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0025] First, some nouns or terms that appear in the process of describing the embodiments of the present application are applicable to the following explanations:

[0026] MQTT module: The MQTT (Message Queuing Telemetry Transport) module is a lightweight, low-bandwidth, low-latency message transmission protocol used in the Internet of Things field, and is suitable for communication between resource-constrained devices and servers.

[0027] Embodiment 1

[0028] According to an embodiment of the present invention, an embodiment of a power signal encoding method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0029] Figure 1 is a flowchart of the power signal encoding method according to an embodiment of the present invention. As Figure 1 shown, the method includes the following steps:

[0030] S102, obtaining a plurality of initial power signals within a predetermined time period, wherein the plurality of initial power signals correspond one-to-one to a plurality of time nodes included in the predetermined time period;

[0031] In step S102 provided in the present application, a plurality of initial power signals within a predetermined time period are obtained.

[0032] Among them, a predetermined time period is involved. The predetermined time period is a pre-selected time range corresponding to the power signal to be encoded.

[0033] Among them, multiple initial power signals are involved. These multiple initial power signals are power signals obtained through data acquisition devices such as sensors within a predetermined time period. The multiple initial power signals can be a sequence of power signals within a predetermined time period, reflecting the state or change of the corresponding power equipment at different time points.

[0034] Among them, multiple time nodes are involved. These multiple time nodes are multiple specific time points or time periods divided according to a predetermined time period, and these time nodes are used to identify and distinguish power signal data at different time points.

[0035] By obtaining multiple initial power signals within a predetermined time period, and the multiple initial power signals correspond one-to-one to the multiple time nodes included in the predetermined time period, it helps to accurately understand the different states of the initial power signals corresponding to the target power equipment at different time nodes subsequently. Thus, by processing and analyzing the multiple initial power signals within the predetermined time period, it can provide a data basis for the subsequent compression coding of these power signals.

[0036] S104. Determine the initial frequency domain sequence corresponding to the multiple initial power signals;

[0037] In step S104 provided in this application, the initial frequency domain sequence corresponding to the multiple initial power signals is determined.

[0038] Among them, the initial frequency domain sequence is involved. The initial frequency domain sequence is a sequence obtained by converting the sequence of different states of the initial power signals corresponding to the target power equipment at different time nodes into the distribution of the initial power signals in different frequency domains. For example, the time domain sequence corresponding to multiple initial power signals is transformed to the frequency domain through a transform domain method to obtain the initial frequency domain sequence. Among them, the transform domain method can be methods such as wavelet transform and Fourier transform. The initial frequency domain sequence contains key information such as the energy distribution and frequency components of the initial power signals at different frequencies.

[0039] This step realizes the conversion of the initial power signals from the time domain to the frequency domain. By determining the initial frequency domain sequence corresponding to the multiple initial power signals, it can reflect the frequency information related to the true state (normal or abnormal state) of the multiple initial power signals within the predetermined time period, which helps the subsequent analysis of the frequency domain components of the initial power signals, and further helps to accurately and efficiently remove the noise or unnecessary frequency domain components in the initial power signals, making the processing of the initial power signals more efficient.

[0040] S106. According to the initial frequency domain sequence corresponding to the multiple initial power signals, determine the number of occurrences of multiple predetermined distributed frequency domains respectively, where the multiple predetermined distributed frequency domains are the frequency domains that appear in the initial frequency domain sequence;

[0041] In step S106 provided in this application, the occurrence times of multiple predetermined distribution frequency domains are determined.

[0042] Among them, a predetermined distribution frequency domain is involved. The predetermined distribution frequency domain is the frequency domain that appears in the initial frequency domain sequence. The predetermined distribution frequency domain is the frequency domain obtained by analyzing the initial frequency domain sequence and used to characterize the state of the initial power signal.

[0043] By identifying the predetermined distribution frequency domains that are relatively important for characterizing the state of the initial power signal, the subsequent encoding process can focus on the key frequency domains, thereby avoiding redundant encoding of irrelevant or unnecessary frequency domains, and improving the pertinence and efficiency of encoding. Furthermore, by counting the occurrence times of specific frequency domains in the frequency domain sequence, a coding basis is provided for subsequent encoding. Different lengths of encoding are performed according to the magnitude of the times. For example, the frequency domains with high occurrences are assigned shorter encodings to reduce the total length of the encoding sequence, thereby improving the encoding speed.

[0044] S108. Perform encoding operations based on multiple predetermined distribution frequency domains and the occurrence times of the multiple predetermined distribution frequency domains respectively to obtain target encodings corresponding to multiple initial power signals.

[0045] In step S108 provided in this application, target encodings corresponding to multiple initial power signals are obtained.

[0046] Among them, a target encoding is involved. The target encoding is the encoding obtained by performing encoding operations (such as specific encoding algorithms or rules) based on multiple predetermined distribution frequency domains and the occurrence times of the multiple predetermined distribution frequency domains respectively, and is corresponding to multiple initial power signals. The target encoding is a compressed representation of multiple initial power signals, containing the key feature information of multiple initial power signals, while removing redundant and unnecessary data.

[0047] By performing encoding operations based on multiple predetermined distribution frequency domains and the occurrence times of the multiple predetermined distribution frequency domains respectively, not only the key feature information of the initial power signal is retained, but also redundant and unnecessary information is effectively removed, significantly reducing the amount of data to be encoded. Thus, while meeting the requirement of efficient encoding of multiple initial power signals, it can also provide sufficient information for data parsing and signal state evaluation.

[0048] Through the above steps S102 - S108, multiple initial power signals within a predetermined time period are obtained, where the multiple initial power signals correspond one-to-one to multiple time nodes included in the predetermined time period; determine the initial frequency domain sequences corresponding to the multiple initial power signals; based on the initial frequency domain sequences corresponding to the multiple initial power signals, determine the number of occurrences of multiple predetermined distributed frequency domains respectively, where the multiple predetermined distributed frequency domains are the frequency domains that appear in the initial frequency domain sequences; perform an encoding operation based on the multiple predetermined distributed frequency domains and the number of occurrences of the multiple predetermined distributed frequency domains respectively to obtain the target encoding corresponding to the multiple initial power signals. By determining the initial frequency domain sequences corresponding to the multiple initial power signals, the frequency information related to the true state of the multiple initial power signals within the predetermined time period can be reflected, which helps to accurately and efficiently remove the noise or unnecessary frequency components in the initial power signals, making the processing of the initial power signals more efficient. By determining the multiple predetermined distributed frequency domains, the subsequent encoding process can focus on the key frequency domains, thus avoiding redundant encoding of irrelevant or unnecessary frequency domains. By performing the encoding operation according to the multiple predetermined distributed frequency domains and the number of occurrences of the multiple predetermined distributed frequency domains respectively, not only the key feature information of the initial power signals is retained, but also the redundant and unnecessary information is effectively removed, significantly reducing the amount of data to be encoded. Thus, while meeting the requirement of efficient encoding of the multiple initial power signals, it can also provide sufficient information for data analysis and signal state evaluation, thereby solving the technical problem of low encoding efficiency in the encoding process of a large number of power signals collected from field operations in the related art.

[0049] As an alternative embodiment, determining the initial frequency domain sequences corresponding to the multiple initial power signals includes: determining the converted signal sequences respectively corresponding to the initial signal sequence under multiple signal conversion methods, where the initial signal sequence includes multiple initial power signals; determining the fluctuation indices respectively corresponding to the multiple converted signal sequences, where the fluctuation index characterizes the corresponding converted signal sequence and reflects the severity of the fluctuation of the initial signal sequence; determining the initial fluctuation sequence based on the multiple converted signal sequences and the fluctuation indices respectively corresponding to the multiple converted signal sequences; determining the initial frequency domain sequences corresponding to the multiple initial power signals based on the initial fluctuation sequence.

[0050] In this embodiment, the specific steps for determining the initial frequency domain sequences corresponding to the multiple initial power signals are described.

[0051] Among them, the initial signal sequence is involved, and the initial signal sequence is a signal sequence determined by multiple initial signals in the time dimension.

[0052] Among them, a signal conversion method is involved, which is a method for converting an original signal sequence (such as an original signal sequence corresponding to multiple initial power signals) into a sequence of another form.

[0053] Among them, a converted signal sequence is involved, which is a signal sequence obtained by converting the original signal sequence through the corresponding signal conversion method.

[0054] Among them, a fluctuation index is involved, which is used to represent the degree of severity for quantifying the corresponding converted signal sequence with respect to the fluctuation situation of the original signal sequence.

[0055] Among them, an initial fluctuation sequence is involved, which is a signal sequence determined by multiple initial signals in the time dimension through multiple signal conversion methods and the fluctuation indices respectively corresponding to multiple converted signal sequences. This initial fluctuation sequence reflects the fluctuation characteristics of multiple initial power signals in the time dimension.

[0056] By converting the original signal sequence into the initial fluctuation sequence, the fluctuation characteristics of multiple initial power signals in the time dimension can be accurately reflected, thereby simplifying the complexity of subsequent signal analysis and processing and providing a data basis for subsequent obtaining of the initial frequency domain sequence.

[0057] As an optional embodiment, determining an initial frequency domain sequence corresponding to multiple initial power signals based on the initial fluctuation sequence includes: determining multiple mean sequences and multiple difference sequences corresponding to multiple initial power signals based on the initial fluctuation sequence, where the mean sequence is a sequence representing the stability characteristics of multiple initial power signals, and the difference sequence is a sequence representing the difference characteristics of multiple initial power signals, and multiple difference sequences correspond one-to-one to multiple mean sequences; determining the initial frequency domain sequence corresponding to multiple initial power signals based on the multiple mean sequences and multiple difference sequences corresponding to the initial fluctuation sequence.

[0058] In this embodiment, the specific steps for determining the initial frequency domain sequence corresponding to multiple initial power signals based on the initial fluctuation sequence are described.

[0059] Among them, a mean sequence is involved, which is a sequence obtained by calculating the mean of multiple initial power signals according to the initial fluctuation sequence. This mean sequence reflects the stability or average behavior characteristics of the power signal within a specific time period. The determination of this mean sequence can be based on taking the mean of values of multiple initial power signals in the initial fluctuation sequence at adjacent or specific intervals.

[0060] Among them, a difference sequence is involved, which is a sequence obtained by calculating the differences of multiple means included in the mean sequence. The difference sequence can be a sequence obtained by calculating the differences between adjacent or specifically spaced means among the multiple means included in the mean sequence. The difference sequence reflects the differences or change characteristics between signals in a specific time period of the power signal.

[0061] By determining multiple mean sequences corresponding to multiple initial power signals, the stability characteristics of the power signal in a specific time period can be accurately reflected, thereby realizing the smoothing process of the initial fluctuation sequence and reducing the influence of noise. By determining multiple difference sequences corresponding to multiple initial power signals, the change characteristics between signals in a specific time period of the power signal can be accurately reflected, so that the trend change conditions of multiple initial power signals in the initial fluctuation sequence at different time periods can be accurately understood. By comprehensively considering multiple mean sequences and multiple difference sequences corresponding to the initial fluctuation sequence, the stability and difference characteristics of multiple initial power signals can be more comprehensively and accurately identified, and thus the obtained initial frequency domain sequence is more accurate.

[0062] As an optional embodiment, determining multiple mean sequences corresponding to multiple initial power signals according to the initial fluctuation sequence includes: determining the number of multiple initial power signals in the initial fluctuation sequence and the position parameters respectively corresponding to the initial power signals; determining the total number of transformation levels according to the number of initial power signals in the initial fluctuation sequence; grouping the initial fluctuation sequence according to the grouping parameters and the position parameters respectively corresponding to the initial power signals to obtain multiple sub-fluctuation sequences; and determining the mean sequence corresponding to the corresponding transformation level according to the multiple sub-fluctuation sequences and the total number of transformation levels.

[0063] In this embodiment, the specific steps of determining multiple mean sequences corresponding to multiple initial power signals according to the initial fluctuation sequence are described.

[0064] Among them, a position parameter is involved, which is used to identify or locate the position of each initial power signal in the initial fluctuation sequence relative to the starting point of the sequence. The position parameter reflects the arrangement order or time position of the initial power signal in the initial fluctuation sequence. For example: if the initial fluctuation sequence is a one-dimensional array, then the position parameter can be the index value of each element in the array, which is used to uniquely identify each initial power signal in the array.

[0065] Among them, the total number of transformation levels is involved, which is the total number of times or levels of dividing or transforming the initial fluctuation sequence when determining the mean sequence. The total number of transformation levels reflects the accuracy of the mean sequence in reflecting the stability characteristics of the initial fluctuation sequence.

[0066] Among them, grouping parameters are involved. The grouping parameters are pre-determined parameters used to divide the initial fluctuation sequence into multiple sub-fluctuation sequences. The grouping parameters can be determined based on the number of signals, time intervals, signal characteristics, or other measurement criteria. For example, the grouping parameter can be a fixed number of signals (such as a group of every 5 signals) or a time interval (such as a group of every 10 minutes) for dividing the initial fluctuation sequence into corresponding multiple sub-fluctuation sequences.

[0067] Among them, sub-fluctuation sequences are involved. The sub-fluctuation sequences are subsequences obtained by dividing the initial fluctuation sequence according to the grouping parameters. The sub-fluctuation sequences contain a part of the signals in the initial fluctuation sequence.

[0068] By reasonably setting the total number of transformation levels, the accuracy of representing the stability characteristics of the initial fluctuation sequence by the mean sequence can be controlled, so as to adapt to the compression requirements of different power signal data. And by converting the initial fluctuation sequence into multiple mean sequences, not only can the main characteristics of multiple initial power signals be extracted from the initial fluctuation sequence, but also the data volume is significantly reduced, which helps to improve the coding efficiency of multiple initial power signals subsequently.

[0069] As an alternative embodiment, determining the mean sequence corresponding to the corresponding transformation level according to multiple sub-fluctuation sequences and the total number of transformation levels includes: at the first transformation level, determining multiple first signal means according to the initial power signals respectively corresponding to the multiple sub-fluctuation sequences, where the multiple first signal means correspond one-to-one to the multiple sub-fluctuation sequences; determining the first mean sequence according to the multiple first signal means; at the second transformation level, grouping the first mean sequence according to the position parameters respectively corresponding to the multiple first signal means according to the grouping parameters to obtain multiple sub-first mean sequences until the Nth transformation level is equal to the total number of transformation levels to obtain the mean sequence corresponding to the corresponding transformation level, where N is a positive integer greater than 1.

[0070] In this embodiment, the specific steps of determining the mean sequence corresponding to the corresponding transformation level according to multiple sub-fluctuation sequences and the total number of transformation levels are described.

[0071] Among them, the first transformation level is involved. The first transformation level is the transformation level of the first level in the process of determining the multi-level mean sequence. For example, if the entire calculation process is divided into three levels of transformation, then the first transformation level is the first level of these three levels.

[0072] Among them, the first signal mean is involved. The first signal mean is the value obtained by calculating the mean of the initial power signals respectively corresponding to the multiple sub-fluctuation sequences at the first transformation level.

[0073] Among them, a first mean sequence is involved, which is a sequence formed by arranging multiple first signal means in a determined order under a first transformation series.

[0074] Among them, a second transformation series is involved, which is the second-level transformation series in the process of determining a multi-level mean sequence after the first transformation series.

[0075] Among them, a sub-first mean sequence is involved, which is a sequence formed by arranging in order the results obtained by calculating the means of corresponding multiple means in the first mean sequence under the second transformation series. For example: if the first mean sequence contains 10 means and the second transformation series divides them into 5 groups with 2 means in each group, then these 5 groups of means form the sub-first mean sequence.

[0076] Among them, an Nth transformation series is involved, which is the Nth-level change series in the process of determining a multi-level mean sequence, where N is a positive integer greater than 1.

[0077] By determining the total transformation series and determining the corresponding mean sequences from the first transformation series to the Nth transformation series (where N is equal to the total transformation series), a hierarchical fine analysis of the initial fluctuation sequence is realized, which helps to reflect the stability characteristics corresponding to the initial fluctuation sequence from different fine levels.

[0078] As an alternative embodiment, according to the initial fluctuation sequence, multiple mean sequences and multiple difference sequences corresponding to multiple initial power signals are determined, including: according to the initial fluctuation sequence, determining multiple mean sequences corresponding to the initial fluctuation sequence; according to the multiple mean sequences, determining multiple difference sequences.

[0079] In this embodiment, the specific steps of determining multiple mean sequences and multiple difference sequences corresponding to multiple initial power signals according to the initial fluctuation sequence are described.

[0080] By determining multiple difference sequences according to multiple mean sequences, it is ensured that the obtained multiple difference sequences can also reflect the differential characteristics corresponding to the initial fluctuation sequence from different fine degrees.

[0081] As an alternative embodiment, determining a target code corresponding to a plurality of initial power signals based on a plurality of predetermined distributed frequency domains and the number of occurrences of each of the plurality of predetermined distributed frequency domains respectively includes: sorting the plurality of predetermined distributed frequency domains according to the number of occurrences of each of the plurality of predetermined distributed frequency domains respectively to obtain sorting positions corresponding to the plurality of predetermined distributed frequency domains respectively; encoding the sorting positions corresponding to the plurality of predetermined distributed frequency domains respectively to obtain prefix codes corresponding to the plurality of predetermined distributed frequency domains respectively; encoding the plurality of predetermined distributed frequency domains respectively to obtain distributed frequency domain codes corresponding to the plurality of predetermined distributed frequency domains respectively; and determining a target code corresponding to the plurality of initial power signals based on the prefix codes and the distributed frequency domain codes corresponding to the plurality of predetermined distributed frequency domains respectively.

[0082] In this embodiment, the specific steps of determining a target code corresponding to a plurality of initial power signals based on a plurality of predetermined distributed frequency domains and the number of occurrences of each of the plurality of predetermined distributed frequency domains respectively are described.

[0083] Among them, the sorting position is involved. The sorting position is the position of each predetermined distributed frequency domain in the sorting result after sorting the plurality of predetermined distributed frequency domains according to the number of occurrences of each of the plurality of predetermined distributed frequency domains respectively. The sorting position can reflect the frequency of occurrence of each predetermined distributed frequency domain, and thus can characterize the importance of each predetermined distributed frequency domain.

[0084] Among them, the prefix code is involved. The prefix code is a code obtained by encoding the plurality of predetermined distributed frequency domains according to the sorting position. The prefix code can uniquely identify the corresponding sorting position of the predetermined distributed frequency domain.

[0085] Among them, the distributed frequency domain code is involved. The distributed frequency domain code is a code obtained by encoding the plurality of predetermined distributed frequency domains themselves. The distributed frequency domain code can uniquely identify the corresponding predetermined distributed frequency domain.

[0086] By determining the corresponding prefix code according to the sorting position, a relatively short prefix code can be set for the predetermined distributed frequency domain that appears more frequently. While ensuring that each predetermined distributed frequency domain has a unique identification code, and the coding length is short, thus significantly reducing the length of the target code, and further helping to solve the technical problem of low coding efficiency in the process of encoding a large number of power signals collected from field operations in the related art.

[0087] Based on the above embodiment and the alternative embodiment, an alternative implementation manner is provided, which is specifically described below.

[0088] In the related art, before transmitting the electrical signals collected during field operations using wireless communication technology, satellite communication technology, etc., a large amount of data needs to be encoded. During the encoding process of a large number of electrical signals collected during field operations using the existing encoding methods, there is a technical problem of low encoding efficiency.

[0089] In response to the above problems, no effective solution has been proposed yet.

[0090] In view of this, in an alternative embodiment of the present invention, there is provided an electrical signal encoding method and a wireless network monitoring system based on data compression encoding. Among them, the data compression encoding in the monitoring system uses the above-mentioned electrical signal encoding method. It can effectively solve the technical problem of low encoding efficiency in the related art during the encoding process of a large number of electrical signals collected during field operations.

[0091] Figure 2 is the overall structure diagram of the monitoring system in an alternative embodiment of the present invention, Figure 3 is a schematic diagram of the decomposition and reconstruction of data in an alternative embodiment of the present invention, Figure 4 is a segmented schematic diagram of the signal cut-off frequency in an alternative embodiment of the present invention, Figure 5 is the transformed hardware circuit structure diagram of the digital tool end in an alternative embodiment of the present invention, Figure 6 is the scatter plot of the original temperature data in an alternative embodiment of the present invention, Figure 7 is the flow chart of the original data compression encoding in an alternative embodiment of the present invention, Figure 8 is the GPS module structure diagram of the digital tool end in an alternative embodiment of the present invention, Figure 9 is the data transmission flow chart of the digital tool end in an alternative embodiment of the present invention, Figure 10 is the work flow chart of the cloud platform working end in an alternative embodiment of the present invention, Figure 11 is the vibration data compression encoding flow chart. As Figures 2 to 11 shown, the following is a detailed description.

[0092] (1) Electrical signal encoding method:

[0093] S1. Obtain a plurality of initial electrical signals within a predetermined time period, where the plurality of initial electrical signals correspond one-to-one to the plurality of time nodes included in the predetermined time period;

[0094] S2. Determine the initial frequency domain sequence corresponding to the plurality of initial electrical signals;

[0095] Specifically, S2 further includes:

[0096] S21. Determine the converted signal sequences respectively corresponding to the initial signal sequence under multiple signal conversion methods, where the initial signal sequence includes multiple initial power signals;

[0097] S22. Determine the fluctuation indices respectively corresponding to the multiple converted signal sequences, where the fluctuation index characterizes the corresponding converted signal sequence and reflects the severity of the fluctuation of the initial signal sequence;

[0098] S23. Determine the initial fluctuation sequence based on the multiple converted signal sequences and the fluctuation indices respectively corresponding to the multiple converted signal sequences;

[0099] For example, after a same sensor node collects the original data of the discrete time series (the same as the above initial signal sequence) at different times, the least squares method is used for data preprocessing, and the fitting formula is as follows:

[0100] S(x) = a 0 φ 0 (x) + a 1 φ 1 (x) + … + a m φ m (x)

[0101] Among them, S(x) is the signal value at the x time point, x represents the time point corresponding to the initial power signal, φ 0 (x), φ 1 (x) …… φ m (x) represents a group of basis functions (the same as the above signal conversion method), and its selection depends on the specific problem and application scenario, m represents the total number of basis functions, a 0 、a 1 ……a m are the weighting coefficients of each basis function (the same as the above fluctuation index) and determine the contribution degree of each basis function to S(x).

[0102] S24. Determine multiple mean sequences and multiple difference sequences corresponding to the multiple initial power signals according to the initial fluctuation sequence, where the mean sequence is a sequence used to represent the stability characteristics of the multiple initial power signals, the difference sequence is a sequence used to represent the difference characteristics of the multiple initial power signals, and the multiple difference sequences correspond to the multiple mean sequences one by one;

[0103] Specifically, S24 further includes:

[0104] S241. Determine the number of multiple initial power signals in the initial fluctuation sequence and the position parameters respectively corresponding to the initial power signals;

[0105] S242. Determine the total transformation level according to the number of initial power signals in the initial fluctuation sequence;

[0106] S243. Group the initial fluctuation sequence according to the grouping parameters and the position parameters corresponding to the initial power signals respectively to obtain multiple sub-fluctuation sequences;

[0107] S244. At the first transformation level, determine multiple first signal means according to the initial power signals corresponding to the multiple sub-fluctuation sequences, where the multiple first signal means correspond one-to-one to the multiple sub-fluctuation sequences;

[0108] S245. Determine the first mean sequence according to the multiple first signal means;

[0109] S246. At the second transformation level, group the first mean sequence according to the grouping parameters and the position parameters corresponding to the multiple first signal means respectively to obtain multiple sub-first mean sequences until the Nth transformation level is equal to the total transformation level to obtain the mean sequence corresponding to the corresponding transformation level, where N is a positive integer greater than 1;

[0110] S247. Determine multiple mean sequences corresponding to the initial fluctuation sequence according to the initial fluctuation sequence;

[0111] S248. Determine multiple difference sequences according to the multiple mean sequences;

[0112] For example, use the transform domain method for the preprocessed data to achieve its decomposition and reconstruction, and obtain the frequency structure distribution of the original signal (the same as the above multiple mean sequences and multiple difference sequences). The formula is as follows:

[0113]

[0114] d n-1,l =s n,2l+1 -s n,2l

[0115] where s n represents the intermediate signal (i.e., the mean) obtained after each level of transformation, d n represents the difference between two adjacent original signal elements, 2l and 2l + 1 represent the positions of two adjacent signals in the sequence (the same as the above position parameters), and n represents the total transformation level.

[0116] S25. Determine the initial frequency domain sequence corresponding to the multiple initial power signals according to the multiple mean sequences and multiple difference sequences corresponding to the initial fluctuation sequence.

[0117] S3. Determine the number of times each of the multiple predetermined distribution frequency domains appears according to the initial frequency domain sequence corresponding to the multiple initial power signals, where the multiple predetermined distribution frequency domains are the frequency domains that appear in the initial frequency domain sequence;

[0118] S4. Sort the multiple predetermined distribution frequency domains according to the number of occurrences of each in the respective frequency domains to obtain the sorting positions corresponding to the multiple predetermined distribution frequency domains.

[0119] S5. Encode the sorting positions corresponding to the multiple predetermined distribution frequency domains respectively to obtain the prefix codes corresponding to the multiple predetermined distribution frequency domains.

[0120] S6. Encode the multiple predetermined distribution frequency domains respectively to obtain the distribution frequency domain codes corresponding to the multiple predetermined distribution frequency domains.

[0121] S7. Determine the target codes corresponding to the multiple initial power signals according to the prefix codes and distribution frequency domain codes corresponding to the multiple predetermined distribution frequency domains respectively.

[0122] For example, the original signal is divided into multiple segments according to its cut-off frequency, and a threshold is set to retain the frequency segments similar to the frequency characteristics of the sensor data and remove other frequency segments; data encoding is performed on the transformation coefficients (same as the above-mentioned predetermined distribution frequency domains) of the retained frequency segments. Arrange the probabilities (should be frequencies or numbers of occurrences, same as the above-mentioned sorting positions) of each symbol (same as the above-mentioned predetermined distribution frequency domains) in the original signal source in ascending order, establish an array, and store the index results (same as the above-mentioned sorting positions) in the encoding results of each character; finally, use base conversion to compress the data bits to obtain the final encoding result (same as the above-mentioned target code), completing the compression of the data collected by the digital tool segment.

[0123] The above method steps can be summarized as follows: First, perform data preprocessing. After the sensor collects the original data, the least squares method is used for curve fitting to convert the collected discrete signal into a continuous signal, so as to improve the signal collection accuracy and at the same time reduce the overall high-frequency proportion of the data. After preprocessing, use the transform domain method to obtain information such as the energy distribution and frequency components of the original signal. According to the data characteristics of the sensor, retain the frequency segments similar to the frequency characteristics of the sensor data and remove other frequency segments. After time-frequency transformation, perform data encoding on the processed transformation coefficients: arrange the characters in the signal source in ascending order according to the occurrence probability, establish an array, and then store the index results of the array in the encoding results of each source character. Finally, use base conversion to further compress the data bits, improve the data compression ratio, and ensure the accuracy of the signal.

[0124] Next, based on the above steps, a specific example will be introduced.

[0125] When detecting the temperature of the digital tool in the wild, the temperature sensor on the acquisition node will always be in a working state and continuously collect data at different times. After collecting the original temperature data of these discrete time series, use the above steps to encode it and compress the encoded data. The specific steps are as follows:

[0126] A1. Preprocess the collected original temperature data using the least squares method, and the fitting formula is as follows:

[0127] SW(w) = Wa 0 Wφ 0 (w) + Wa 1 Wφ 1 (w) + … + Wa m Wφ m (w)

[0128] Among them, SW(w) is the continuous function obtained by fitting the original temperature data, representing the temperature value at time w, and w represents the time point corresponding to the original temperature data. Wφ 0 (w), Wφ 1 (w) …… Wφ m (w) represents a set of basis functions. Wa 0 、Wa 1 ……Wa m are the weighting coefficients of each basis function, which determine the contribution degree of each basis function to SW(w).

[0129] When performing temperature detection, the data change rate is relatively fast. Select the pulse function as the basis function expression, and the formula is as follows:

[0130]

[0131] Among them, w 0 represents the mean value of the temperature, and σ determines the width of the pulse function.

[0132] A2. Use the transform domain method for the preprocessed temperature data to achieve its decomposition and reconstruction, and obtain the frequency structure distribution of the original signal.

[0133] A3. The change rate of the temperature is relatively slow, and the signal frequency output by the sensor is relatively low. Therefore, retain the low-frequency part W1 to W3 and discard the high-frequency part H1 at the same time.

[0134] A4. Count the number of occurrences of characters in each information source in the retained low-frequency band part of the original data, sort these characters in ascending order according to the occurrence probability, and establish an array according to their sorting order.

[0135] A5. Store the indexed results in the encoding results of each character, and finally use the base conversion to compress the data bits to obtain the final encoding result, completing the compression of the data collected by the digital tool segment.

[0136] When performing vibration detection on digital tools in the wild, the vibration sensors on the acquisition nodes will always remain in working condition and continuously collect data at different times. After collecting the original vibration data of these discrete time series, use the above steps to encode it and compress the encoded data. The specific steps are as follows:

[0137] B1. Preprocess the collected original vibration data using the least squares method. The fitting formula is as follows:

[0138] SZ(z) = za 0 zφ 0 (z) + Za 1 Zφ 1 (z) + … + za m Zφ m (z)

[0139] Among them, SZ(z) is the continuous function obtained by fitting the original temperature data, representing the vibration amplitude or acceleration value at time z. Zφ 0 (z), Zφ 1 (z) …… Zφ m (z) represents a set of basis functions. Za 0 、Za 1 ……Za m are the weighting coefficients of each basis function, determining the contribution degree of each basis function to SZ(z).

[0140] The basis function expression selects the polynomial basis function:

[0141] Zφ i (z) = z i (i = 0, 1, 2,..., m)

[0142] Among them, z represents the vibration time, and i represents the power.

[0143] B2. Use the transform domain method for the preprocessed vibration data to achieve its decomposition and reconstruction, and obtain the frequency structure distribution of the original signal. Divide it into four frequency regions according to the distribution range of frequencies: L1 is 0 - 500Hz, L2 is 500 - 1000Hz, L3 is 1000 - 2000Hz, and L4 is the segment with a frequency above 2000Hz.

[0144] B3. The frequency band of the vibration sensor is relatively wide. The acquisition frequency at the digital tool end is selected as 1000Hz. Therefore, retain the data of the L1 and L2 segments and remove other frequency segments to improve the overall compression rate of the data.

[0145] B4. Statistically count the number of occurrences of characters in each information source within the reserved frequency bands, sort these characters in ascending order according to their occurrence probabilities, and establish an array based on their sorting order.

[0146] B5. Store the indexing results in the encoding results of each character. Finally, use base conversion to compress the number of data bits to obtain the final encoding result, thus completing the compression of the data collected by the digital tool segment.

[0147] (2) Wireless network monitoring system based on data compression encoding:

[0148] In an alternative embodiment of the present invention, a wireless network monitoring system based on data compression encoding is also provided.

[0149] The wireless network monitoring system based on data compression encoding includes: a digital tool terminal, a cloud platform working terminal, and a user information display terminal.

[0150] Among them, the digital tool terminal is used to transmit the collected raw data to the cloud platform working terminal after compression encoding after digitizing the electric wrench, wire crimping pliers, and cable straightening and heating tool;

[0151] The cloud platform working terminal is responsible for storing and managing the data generated during the use of the digital tool and performing data analysis on it;

[0152] The user information display terminal is used to view and manage information such as the status of the digital tool and the data analysis results.

[0153] Optionally, the digital tool terminal is also used to achieve comprehensive statistics and in-depth analysis of the operation data of the construction tools, and compress the collected data using a power signal encoding method.

[0154] Optionally, the cloud platform working terminal is also used to collect and manage the working data of the digital tool terminal by using wireless communication technology, and establish a large database based on these processed data to manage and use the data.

[0155] Optionally, the user information display terminal is also used to perform operations such as setting parameter thresholds and viewing historical operations.

[0156] Based on this system, specific examples will be described in detail below.

[0157] C1. Digital tool terminal:

[0158] The electric wrench, wire crimping pliers, and cable straightening and heating tool after digitization form the digital tool terminal, which compresses and encodes the data collected by the sensor and transmits it to the cloud platform working terminal, mainly including: a power supply circuit, a positioning module, a data compression module, and a data transmission module.

[0159] When performing temperature detection or vibration detection on the digital tool in the field, a predetermined single-chip microcomputer is selected and installed on the digital tool to provide a 3.3V power supply voltage for the GPS positioning module to ensure the stable operation of the GPS positioning module. At the same time, a USB interface is equipped on the GPS positioning module and connected to the device through a USB cable to receive and parse GPS signals in real time. Finally, the data transmission module at the digital tool end transmits the temperature data or vibration data after compression encoding processing, together with the positioning information, to the cloud platform working end by means of wireless communication.

[0160] C2. Cloud platform working end:

[0161] The cloud platform working end writes the unique information corresponding to the cloud platform device into the digital tool through the predefined function in the MQTT module. Each device has a unique identity information. When the output pointer (MQTT_TxDataOutPtr) is inconsistent with the input pointer (MQTT_TxDataInPtr), the system will enter the attempt connection state. Once entering the connection loop, the program will continuously listen for feedback information from the cloud platform. After confirming the connection is correct, the temperature data collected by the sensor will be sent to the cloud platform working end through this channel. The cloud platform working end uses the corresponding decoding algorithm according to the data compression algorithm adopted by the sending end for real-time display and analysis, and establishes a large database based on these processed data to manage and use the data.

[0162] C3. User information display end:

[0163] After the cloud platform working end analyzes and processes the collected temperature data, it will be presented in the form of a table or a picture on the customer display end. After the user views the status of the digital tool in real time, they can choose to send instructions to adjust parameters such as its collection point and frequency.

[0164] Through the above optional implementation manners, at least the following beneficial effects can be achieved:

[0165] (1) Compared with the related art, the present invention can reflect the frequency information related to the true state of multiple initial power signals within a predetermined time period by determining the initial frequency domain sequences corresponding to the multiple initial power signals, which helps to accurately and efficiently remove the noise or unnecessary frequency domain components in the initial power signals, making the processing of the initial power signals more efficient. By determining multiple predetermined distributed frequency domains, the subsequent encoding process can focus on the key frequency domains, thus avoiding redundant encoding of irrelevant or unnecessary frequency domains. By performing encoding operations according to the multiple predetermined distributed frequency domains and the number of occurrences of the multiple predetermined distributed frequency domains respectively, not only the key feature information of the initial power signals is retained, but also redundant and unnecessary information is effectively removed, significantly reducing the amount of data to be encoded. Therefore, while meeting the requirement of efficient encoding of multiple initial power signals, sufficient information can also be provided for data analysis and signal state evaluation, thereby solving the technical problem of low encoding efficiency in the encoding process of a large number of power signals collected from field operations in the related art.

[0166] (2) Compared with the related art, the present invention can accurately reflect the stability characteristics of power signals within a specific time period by determining multiple mean sequences corresponding to the multiple initial power signals, thereby realizing the smoothing process of the initial fluctuation sequence and reducing the influence of noise. By determining multiple difference sequences corresponding to the multiple initial power signals, the change characteristics between signals within a specific time period of the power signals can be accurately reflected, so that the trend change of multiple initial power signals in the initial fluctuation sequence at different time periods can be accurately understood. By comprehensively considering the multiple mean sequences and multiple difference sequences corresponding to the initial fluctuation sequence, the stability and difference characteristics of multiple initial power signals can be more comprehensively and accurately identified, and thus the obtained initial frequency domain sequence is more accurate.

[0167] (3) Compared with the related art, the present invention can control the accuracy of the mean sequence representing the stability characteristics of the initial fluctuation sequence by reasonably setting the total number of transformation levels, so as to adapt to the compression requirements of different power signal data. And by converting the initial fluctuation sequence into multiple mean sequences, not only the main features of multiple initial power signals can be extracted from the initial fluctuation sequence, but also the amount of data is significantly reduced, which helps to improve the encoding efficiency of multiple initial power signals subsequently.

[0168] (4) Compared with the related art, the present invention can determine the corresponding prefix code according to the sorting order. It can set a relatively short prefix code for the predetermined distribution frequency domain that appears more frequently, ensuring that each predetermined distribution frequency domain has a unique identification code while having a short coding length, thereby significantly reducing the length of the target code, and further helping to solve the technical problem of low coding efficiency in the process of encoding a large number of power signals collected from field operations in the related art.

[0169] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0170] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0171] Embodiment 2

[0172] According to an embodiment of the present invention, there is also provided a device for implementing the above power signal encoding method. Figure 12 It is a structural block diagram of the power signal encoding device according to an embodiment of the present invention. As Figure 12 shown, the device includes: an acquisition module 1202, a first determination module 1204, a second determination module 1206, and a third determination module 1208. The device will be described in detail below.

[0173] An acquisition module 1202, configured to acquire a plurality of initial power signals within a predetermined time period, wherein the plurality of initial power signals correspond one-to-one to a plurality of time nodes included in the predetermined time period; a first determination module 1204, connected to the acquisition module 1202, configured to determine an initial frequency domain sequence corresponding to the plurality of initial power signals; a second determination module 1206, connected to the first determination module 1204, configured to determine the number of occurrences of each of a plurality of predetermined distributed frequency domains based on the initial frequency domain sequence corresponding to the plurality of initial power signals, wherein the plurality of predetermined distributed frequency domains are the frequency domains that appear in the initial frequency domain sequence; a third determination module 1208, connected to the second determination module 1206, configured to perform an encoding operation based on the plurality of predetermined distributed frequency domains and the number of occurrences of each of the plurality of predetermined distributed frequency domains, to obtain a target code corresponding to the plurality of initial power signals.

[0174] It should be noted here that the acquisition module 1202, the first determination module 1204, the second determination module 1206, and the third determination module 1208 correspond to steps S102 to S108 in the method for encoding power signals. The instances and application scenarios implemented by the plurality of modules and the corresponding steps are the same, but are not limited to the content disclosed in the above-mentioned Embodiment 1.

[0175] Embodiment 3

[0176] According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including: a processor; a memory for storing processor-executable instructions, wherein the processor is configured to execute the instructions to implement the power signal encoding method of any one of the above.

[0177] Embodiment 4

[0178] According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enabling the electronic device to execute the power signal encoding method of any one of the above.

[0179] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0180] In the above embodiments of the present invention, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0181] In several embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other can be through some interfaces. The indirect couplings or communication connections of units or modules can be in electrical or other forms.

[0182] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0183] In addition, in each embodiment of the present invention, the functional units can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0184] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs and other various media that can store program codes.

[0185] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for encoding a power signal, characterized in that: include: Acquire a plurality of initial power signals within a predetermined time period, wherein the plurality of initial power signals correspond one-to-one to a plurality of time nodes included in the predetermined time period; determining an initial frequency domain sequence corresponding to the plurality of initial power signals; Determining the number of times a plurality of predetermined distributed frequency domains respectively appear according to an initial frequency domain sequence corresponding to the plurality of initial power signals, wherein the plurality of predetermined distributed frequency domains are frequency domains appearing in the initial frequency domain sequence; According to the plurality of predetermined distributed frequency domains and the number of times the plurality of predetermined distributed frequency domains respectively appear, an encoding operation is performed to obtain target codes corresponding to the plurality of initial power signals.

2. The method according to claim 1, characterized in that The determining of the initial frequency domain sequences corresponding to the multiple initial power signals comprises: Determining conversion signal sequences corresponding to the initial signal sequence in multiple signal conversion modes, wherein the initial signal sequence includes the multiple initial power signals; Determine fluctuation indexes corresponding to the plurality of conversion signal sequences respectively, wherein the fluctuation indexes characterize the corresponding conversion signal sequences and reflect the severity of fluctuations of the initial signal sequences; Determining an initial fluctuation sequence according to the plurality of conversion signal sequences and the fluctuation indexes respectively corresponding to the plurality of conversion signal sequences; An initial frequency domain sequence corresponding to the plurality of initial power signals is determined according to the initial fluctuation sequence.

3. The method according to claim 2, characterized in that The determining, based on the initial fluctuation sequence, an initial frequency domain sequence corresponding to the plurality of initial power signals comprises: According to the initial fluctuation sequence, a plurality of mean sequences and a plurality of difference sequences corresponding to the plurality of initial power signals are determined, wherein the mean sequence is used to represent a sequence of stability characteristics of the plurality of initial power signals, the difference sequence is used to represent a sequence of difference characteristics of the plurality of initial power signals, and the plurality of difference sequences correspond one-to-one to the plurality of mean sequences; An initial frequency domain sequence corresponding to the multiple initial power signals is determined according to the multiple mean value sequences and the multiple difference value sequences corresponding to the initial fluctuation sequence.

4. The method according to claim 3, characterized in that Determining a plurality of mean value sequences corresponding to the plurality of initial power signals according to the initial fluctuation sequence includes: Determining the number of a plurality of initial power signals in the initial fluctuation sequence and position parameters respectively corresponding to the initial power signals; Determining the total number of transformation stages according to the number of initial power signals in the initial fluctuation sequence; According to the grouping parameter and the position parameters respectively corresponding to the initial power signals, the initial fluctuation sequence is grouped to obtain a plurality of sub-fluctuation sequences; Based on the multiple sub-fluctuation sequences and the total transformation series, the mean sequence of the corresponding transformation series is determined.

5. The method according to claim 4, characterized in that According to the plurality of sub-fluctuation sequences and the total transformation series, a mean sequence of corresponding transformation series is determined, including: Under the first transformation level, a plurality of first signal means are determined according to the initial power signals respectively corresponding to the plurality of sub-fluctuation sequences, wherein the plurality of first signal means correspond one-to-one to the plurality of sub-fluctuation sequences; Determining a first mean value sequence according to the plurality of first signal means; At the second transformation level, based on the grouping parameters, the first mean sequence is grouped according to the position parameters corresponding to the multiple first signal means, to obtain multiple sub-first mean sequences, until the Nth transformation level is equal to the total transformation level, to obtain the mean sequence corresponding to the transformation level, where N is a positive integer greater than 1.

6. The method according to claim 3, characterized in that Determining a plurality of mean value sequences and a plurality of difference value sequences corresponding to the plurality of initial power signals according to the initial fluctuation sequence includes: According to the initial fluctuation sequence, determining a plurality of mean value sequences corresponding to the initial fluctuation sequence; The plurality of difference sequences are determined according to the plurality of mean value sequences.

7. The method according to any one of claims 1 to 6, characterized in that: The determining of target codes corresponding to the plurality of initial power signals according to the plurality of predetermined distribution frequency domains and the number of times the plurality of predetermined distribution frequency domains respectively appear comprises: Sorting the plurality of predetermined distribution frequency domains according to the number of times the plurality of predetermined distribution frequency domains respectively appear, to obtain the rankings respectively corresponding to the plurality of predetermined distribution frequency domains; Encoding the sorting positions respectively corresponding to the plurality of predetermined distribution frequency domains to obtain prefix codes respectively corresponding to the plurality of predetermined distribution frequency domains; Encoding the plurality of predetermined distributed frequency domains respectively to obtain distributed frequency domain codes corresponding to the plurality of predetermined distributed frequency domains respectively; According to the prefix codes and the distributed frequency domain codes respectively corresponding to the plurality of predetermined distributed frequency domains, target codes corresponding to the plurality of initial power signals are determined.

8. A power signal encoding device, characterized in that: include: An acquisition module, configured to acquire a plurality of initial power signals within a predetermined time period, wherein the plurality of initial power signals correspond one-to-one to a plurality of time nodes included in the predetermined time period; A first determining module, configured to determine an initial frequency domain sequence corresponding to the plurality of initial power signals; A second determination module is used to determine the number of occurrences of a plurality of predetermined distributed frequency domains according to an initial frequency domain sequence corresponding to the plurality of initial power signals, wherein the plurality of predetermined distributed frequency domains are frequency domains that appear in the initial frequency domain sequence; The third determination module is used to perform encoding operations according to the multiple predetermined distributed frequency domains and the number of times the multiple predetermined distributed frequency domains appear respectively, to obtain target codes corresponding to the multiple initial power signals.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the power signal encoding method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the power signal encoding method according to any one of claims 1 to 7.

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