Running state data sending method of charging pile and related device

Abstract

The invention discloses an operation state data sending method of a charging pile and a related device, the method is applied to the charging pile in a charging pile management system, the charging pile management system further comprises a server, and the method comprises the following steps: obtaining operation state data of the charging pile; determining a data feature type of the operation state data, wherein the data feature type is one of a smooth data type, a local feature data type and a specific frequency data type; generating compressed data corresponding to the operation state data according to a sparsification mode corresponding to the data feature type; and sending the compressed data to a server. By adopting the scheme of the invention, the transmission efficiency of the operation state data of the charging pile can be improved.

Description

Technical Field

[0001] The present invention relates to the general data processing field related to new energy, and in particular to a method for sending operation status data of a charging pile and a related device. Background Art

[0002] With the popularity of electric vehicles, the number of charging piles has increased dramatically. Fault detection and information transmission of charging piles are crucial to ensure the normal operation of charging piles. In the prior art, the transmission of data reflecting the operating status of charging piles, such as voltage, current, fault information, etc., usually adopts the traditional direct transmission method.

[0003] Since the various data of the charging pile's operating status data are large in volume and some data need to be transmitted in real time, the transmission efficiency of directly transmitting the charging pile's operating status data through traditional transmission methods is low and it is difficult to meet the needs of efficient transmission of the charging pile's operating status data. Summary of the invention

[0004] In response to the above problems, the example of the present application provides a method and related device for sending the operating status data of a charging pile. The adoption of the solution of the present application is conducive to improving the transmission efficiency of the operating status data of the charging pile.

[0005] In a first aspect, an embodiment of the present application provides a method for sending operating status data of a charging pile, which is applied to charging piles in a charging pile management system. The charging pile management system also includes a server, and the method includes: obtaining the operating status data of the charging pile; determining the data feature type of the operating status data, the data feature type is one of a smooth data type, a local feature data type, and a specific frequency data type; generating compressed data corresponding to the operating status data according to a sparse method corresponding to the data feature type; and sending the compressed data to the server.

[0006] It can be seen that in the embodiment of the present application, by analyzing the data feature types of the operating status data collected by the charging pile, different sparse processing methods are used for the operating status data of different data feature types, thereby generating compressed data of the operating status data, and sending the compressed data to the server, thereby improving the transmission efficiency of the operating status data of the charging pile.

[0007] In combination with the first aspect, in a possible embodiment, the compressed data includes sampled data and sparse data of operating status data, and the compressed data corresponding to the operating status data is generated according to the sparse method corresponding to the data feature type, including: determining the measurement matrix of the operating status data according to the data feature type of the operating status data; compressing and sampling the operating status data according to the measurement matrix of the operating status data to generate the sampled data of the operating status data; determining the sparse processing method of the operating status data according to the data feature type of the operating status data; and sparse processing the operating status data according to the sparse processing method of the operating status data to generate the sparse data of the operating status data.

[0008] In combination with the first aspect, in a possible embodiment, a sparse processing method for the running status data is determined according to the data feature type of the running status data, including: if the data feature type of the running status data is a smooth data type, one of a random Gaussian matrix, a random Bernoulli matrix or a Toeplitz matrix is ​​determined as the measurement matrix of the running status data; if the data feature type of the running status data is a local feature data type, one of a random Gaussian matrix or a partial Fourier matrix is ​​determined as the measurement matrix of the running status data; if the data feature type of the running status data is a specific frequency data type, one of a random Gaussian matrix or a partial Fourier matrix is ​​determined as the measurement matrix of the running status data.

[0009] It can be seen that in the embodiment of the present application, a corresponding measurement matrix is ​​selected for the running status data according to the data feature type of the running status data to generate sampling data corresponding to the running status data, thereby improving the generation efficiency of the sampling data and further improving the transmission efficiency of the running status data.

[0010] In combination with the first aspect, in a possible embodiment, a sparse processing method for the running status data is determined according to the data feature type of the running status data, including: if the data feature type of the running status data is a smooth data type, then the discrete cosine transform is determined as the sparse processing method for the running status data; if the data feature type of the running status data is a local feature data type, then the discrete wavelet transform is determined as the sparse processing method for the running status data; if the data feature type of the running status data is a specific frequency data type, then the Fourier transform is determined as the sparse processing method for the running status data.

[0011] It can be seen that in the embodiment of the present application, a corresponding sparse processing method is selected for the running status data according to the data feature type of the running status data to obtain sparse data corresponding to the running status data, thereby improving the processing efficiency and compression rate of the sparse data, and further improving the transmission efficiency of the running status data.

[0012] In combination with the first aspect, in a possible embodiment, sending compressed data to a server includes: determining a data type of the operating status data, the data type being one of voltage data, current data, fault data, temperature data, power data or harmonic data; determining a transmission type of the operating status data according to the data type of the operating status data, the transmission type being a periodic transmission type, a real-time transmission type or a bidirectional transmission type; if the transmission type of the operating status data is a periodic transmission type, sending the compressed data to the server according to the HTTP protocol; if the transmission type of the operating status data is a real-time transmission type, sending the compressed data to the server according to the MQRR protocol; if the transmission type of the operating status data is a bidirectional transmission type, sending the compressed data to the server according to the WebSocket protocol.

[0013] It can be seen that in the embodiment of the present application, by determining the transmission type of the running status data, selecting the corresponding transmission protocol for the compressed data of the running status data, and sending the compressed data of the running status data to the server through the optimal transmission protocol, the transmission efficiency of the running status data is further improved.

[0014] In combination with the first aspect, in a possible embodiment, determining the data feature type of the operating status data includes: analyzing the operating status data according to one or more analysis methods of power spectrum density analysis, energy concentration analysis, and statistical analysis of non-zero coefficients to obtain analysis results of the operating status data; determining the data feature type of the operating status data according to the analysis results.

[0015] It can be seen that in the embodiments of the present application, the operating status data is analyzed based on one or more analysis methods including rate spectrum density analysis, energy concentration analysis, and statistical analysis of non-zero coefficients to determine the data feature type of the operating status data, and then the compression and transmission method with the highest transmission efficiency can be determined according to the data feature type, thereby improving the transmission efficiency of the operating status data.

[0016] In combination with the first aspect, in a possible embodiment, obtaining the operating status data of the charging pile includes: obtaining the installation scenario information of the charging pile, determining a first frequency and a second frequency for obtaining the operating status data according to the installation scenario information, the first frequency being higher than the second frequency; obtaining time information, and determining a current operating stage of the charging pile according to the time information; if the current operating stage of the charging pile is a daytime operating stage, obtaining the operating status data of the charging pile according to the second frequency; if the operating stage of the charging pile is a nighttime operating stage, obtaining the operating status data of the charging pile according to the first frequency.

[0017] It can be seen that in the embodiment of the present application, by determining different operating status acquisition frequencies for the charging pile in different operating environments and time periods, the operating status data is acquired at a lower frequency under the premise that the operating environment is relatively safe, thereby reducing the amount of data acquired in a relatively safe operating environment, thereby improving the transmission efficiency of the operating status data in a relatively safe operating environment.

[0018] In a second aspect, an embodiment of the present application provides a charging pile, which belongs to a charging pile management system. The charging pile management system also includes a server. The charging pile includes:

[0019] Acquisition unit: used to obtain the operating status data of the charging pile.

[0020] Determining unit: used to determine the data feature type of the running status data, where the data feature type is one of a smoothing data type, a local feature data type and a specific frequency data type.

[0021] Generating unit: used to generate compressed data corresponding to the running status data according to the sparse method corresponding to the data feature type.

[0022] Sending unit: used to send compressed data to the server.

[0023] In a third aspect, an embodiment of the present application provides an electronic device comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and one or more instructions are suitable for being loaded by the processor and executing part or all of the method of the first aspect.

[0024] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the method of the first aspect.

[0025] In a fifth aspect, the present application provides a computer program product. When a computer reads and executes the computer program product, the computer executes part or all of the method of the first aspect.

[0026] It can be understood that the beneficial effects of the embodiments described in the second to fifth aspects can refer to the beneficial effects of the method described in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 A schematic diagram of an application scenario of a method for sending operating status data of a charging pile provided in an embodiment of the present application;

[0029] Figure 2 A schematic diagram of a flow chart of a method for sending operating status data of a charging pile provided in an embodiment of the present application;

[0030] Figure 3 A schematic diagram of voltage operating status data provided by an embodiment of the present application;

[0031] Figure 4 A schematic diagram of fault operation status data provided by an embodiment of the present application;

[0032] Figure 5 A schematic diagram of determining a measurement matrix of operating status data provided in an embodiment of the present application;

[0033] Figure 6 A schematic diagram of a process for determining a sparse processing method for operating status data provided in an embodiment of the present application;

[0034] Figure 7 A schematic diagram of a flow chart of another method for sending operating status data of a charging pile provided in an embodiment of the present application;

[0035] Figure 8 A schematic diagram of the structure of a second charging pile provided in an embodiment of the present application;

[0036] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0037] Description of Figure Numbers

[0038] Application scenario 100; first charging pile 101; server 102; second charging pile 800; acquisition unit 801; determination unit 802; generation unit 803; sending unit 804; electronic device 900; memory 901; processor 902; communication interface 903; bus 904. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0040] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. 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 limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0041] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0042] The embodiments of the present application are described below in conjunction with the accompanying drawings.

[0043] See also Figure 1 , Figure 1 A schematic diagram of an application scenario of a method for sending operation status data of a charging pile provided in an embodiment of the present application, as shown in the figure, the application scenario 100 includes a first charging pile 101 and a server 102, wherein the first charging pile 101 is used to be configured in parking lots, gas stations, shopping malls, and communities to provide charging services for new energy vehicles such as electric vehicles and electric vehicles, and at the same time, the first charging pile 101 obtains the operation status data of the first charging pile 101 based on an internal sampling device or detection device, and sends the operation status data to the server 102. The server 102 is used to receive the operation status data sent by the first charging pile 101, and analyze the operation status, fault cause, charging and discharging peak period distribution, etc. of the first charging pile 101 based on the operation status data.

[0044] In an embodiment of the present application, the first charging pile 101 obtains the operating status data of the charging pile, where the operating status data refers to voltage data, current data, fault data, temperature data, power data, or harmonic data, which are data that can be measured and recorded and generated during the operation of the charging pile.

[0045] The first charging post 101 determines a data feature type of the running status data, where the data feature type is one of a smooth data type, a local feature data type, and a specific frequency data type. The first charging post 101 analyzes multiple data features of the running status data, such as time domain features, period features, frequency domain features, and the like.

[0046] According to the combination of different data features of the running status data, the running status data is divided into different data feature types, namely, the smoothing data type, the local feature data type and the specific frequency data type.

[0047] The smooth data type here specifically refers to data with multiple data values ​​collected over a continuous period of time, which are distributed smoothly and have strong periodicity, and the spectrum is concentrated in the low-frequency band, such as voltage data, current data, and power data. The local feature data type here refers to data with multi-scale characteristics and transient changes, which are used to capture the local characteristics of the operating status of the charging pile, such as fault data, noise data, and load fluctuation data. The specific frequency data type here refers to data with multiple data values ​​collected over a continuous period of time, which have strong periodicity in distribution and the spectrum is concentrated in a specific frequency.

[0048] The first charging pile 101 generates compressed data corresponding to the running state data according to the sparseness method corresponding to the data feature type. The charging pile generates corresponding compressed data using the most appropriate sparseness method based on the data feature type of the running state data, thereby improving the compression rate of the compressed data.

[0049] The first charging post 101 sends the compressed data to the server 102 .

[0050] It can be seen that in the embodiment of the present application, by analyzing the data feature types of the operating status data collected by the charging pile, different sparse processing methods are used for the operating status data of different data feature types, thereby generating compressed data of the operating status data, and sending the compressed data to the server, thereby improving the transmission efficiency of the operating status data of the charging pile.

[0051] See also Figure 2 , Figure 2 A flow chart of a method for sending operating status data of a charging pile provided in an embodiment of the present application can be based on Figure 1 The application scenario 100 shown is implemented as Figure 2 As shown, steps S201-S204 are included:

[0052] S201: The charging pile obtains the operating status data of the charging pile.

[0053] Specifically, the operating status data here include multiple data collected by the charging pile within a continuous period of time, specifically voltage data, current data, fault data, temperature data, power data or harmonic data and other data.

[0054] The operating status data is collected by the charging pile's measuring equipment or sampling equipment. Before further processing the operating status data, the charging pile also needs to preprocess the operating status data. The preprocessing here includes sorting, filtering, denoising, etc.

[0055] For example, for voltage data, the preprocessing here is specifically to remove high-frequency interference through a low-pass filter. For current data, the preprocessing here is specifically to remove direct current components through a high-pass filter.

[0056] Further, after obtaining the operating status data of the charging pile, the charging pile further processes the operating status data to obtain new operating status data, and the charging pile performs subsequent steps based on the new operating status data. The further processing here specifically calculates the mean and variance of the operating status data. In other words, the operating status data here specifically includes one or all of the multiple data collected by the charging pile within a continuous period of time and the data calculated based on the multiple data collected by the charging pile within a continuous period of time.

[0057] S202: The charging pile determines a data feature type of the operating status data, where the data feature type is one of a smoothing data type, a local feature data type, and a specific frequency data type.

[0058] Specifically, the data feature type here specifically refers to the distribution of multiple data values ​​in the running status data. The same data type may also reflect different data feature types in different situations. The charging pile first determines the data feature type of the running status data before sending the running status data each time to determine the most efficient sparse processing method.

[0059] In a possible embodiment, determining the data feature type of the operating status data includes: analyzing the operating status data according to one or more analysis methods selected from power spectrum density analysis, energy concentration analysis, and statistical analysis of non-zero coefficients to obtain analysis results of the operating status data; and determining the data feature type of the operating status data according to the analysis results.

[0060] Specifically, by analyzing the operating status data according to power spectrum density analysis, energy concentration analysis, and statistical analysis of non-zero coefficients, it is possible to determine the frequency characteristics of the data values ​​in the operating status data, the energy concentration level, and the proportion of the number of non-zero coefficients, and then determine the data feature type of the operating status data.

[0061] Exemplarily, for the running status data X[n] including 1024 data values, statistical analysis of non-zero coefficients is used to perform data feature type analysis. First, a threshold ε=0.01 is set. Here, coefficients greater than 0.01 are considered to be non-zero coefficients. The number of non-zero coefficients of the running status data X[n] satisfies the following formula (1):

[0062]

[0063] Among them, X filtered [n] represents the preprocessed operating status data X[n]. Based on formula (1), it is determined that the coefficients of the operating status data X[n] at 50 Hz and 100 Hz (and their harmonics) are significantly non-zero, and the coefficients of the other frequency components are close to zero. It can be judged that the operating status data X[n] signal is smooth and has strong periodicity. The small number of non-zero coefficients indicates that the signal has sparse characteristics. The operating status data X[n] belongs to the local feature data type.

[0064] It can be seen that in the embodiments of the present application, the operating status data is analyzed based on one or more analysis methods including rate spectrum density analysis, energy concentration analysis, and statistical analysis of non-zero coefficients to determine the data feature type of the operating status data, and then the compression and transmission method with the highest transmission efficiency can be determined according to the data feature type, thereby improving the transmission efficiency of the operating status data.

[0065] Furthermore, the sorted data feature types are determined based on the analysis of multiple continuous data values ​​of the operating status data over a period of time. For the same type of operating status data, different features may be presented at different times. For example, voltage data may present data characteristics in normal operating state, faulty operating state, special charging mode and other states, so it may be determined to belong to a smooth data type, a local feature data type or a specific frequency data type. Since the operating status data may include various types of data such as current, voltage, and power, if the charging pile is configured to determine the data feature type of the operating status data before each transmission of the operating status data, when there are many types of operating status data that need to be transmitted at a time, it is necessary to perform multiple determinations of the data feature type of the operating status data.

[0066] In a possible embodiment, if the operating status data is specifically mutation data, determining the data feature type of the operating status data specifically also includes: determining whether the charging pile fails based on the mutation data; if the charging pile fails, determining the data feature type of the fault-related operating status data as a local feature type; if the charging pile does not fail, maintaining the data feature type of the mutation-related operating status data; the mutation data includes one or more of fault data and temperature data; the fault-related operating status data here includes one or more of voltage data, current data, power data and temperature data.

[0067] It can be seen that whether a charging pile fails is determined by the fault data in the operating status data, and the data feature type of the fault-related operating status data is directly determined based on whether the charging pile fails. There is no need to perform feature analysis on the fault-related operating status data, which improves the sparsification efficiency of the fault-related operating status data, and thus improves the transmission efficiency of the charging pile's operating status data.

[0068] S203: The charging pile generates compressed data corresponding to the operating status data according to a sparse method corresponding to the data feature type.

[0069] Specifically, the sparse processing method here corresponds to the state characteristics, and the corresponding relationship here is specifically set in advance, or it can be determined during the charging operation according to the processing effect of the sparse processing method. In short, the corresponding sparse processing method here is the sparse processing method with the highest compression rate of the compressed data.

[0070] For example, see Figure 3 , Figure 3 A schematic diagram of voltage operation status data provided in an embodiment of the present application, in which it can be seen that Figure 3 During the time period shown, the voltage data value does not change much, and the voltage data value continues to remain near v during the time period from 0 to t. Therefore, its data feature type is a smooth data type, and sparse processing through discrete cosine transform can achieve the highest processing efficiency.

[0071] See also Figure 4 , Figure 4 A schematic diagram of a fault operation status data provided in an embodiment of the present application, it can be seen that Figure 4 During the time period 0-t shown, the data value is usually 0, except for t 0 The data value suddenly changes to 1 and then to 0 at time t 0 The charging pile has a fault at that moment, so its data feature type is a local feature data type. Sparse processing through discrete wavelet transform can achieve the highest processing efficiency.

[0072] In a possible embodiment, the compressed data includes sampled data and sparse data of operating status data, and the compressed data corresponding to the operating status data is generated according to the sparse processing method corresponding to the data feature type, including: determining the measurement matrix of the operating status data according to the data feature type of the operating status data; compressing and sampling the operating status data according to the measurement matrix of the operating status data to generate the sampled data of the operating status data; determining the sparse processing method of the operating status data according to the data feature type of the operating status data; and sparse processing the operating status data according to the sparse processing method of the operating status data to generate the sparse data of the operating status data.

[0073] Specifically, the compressed data here includes the sampling data and sparse data of the corresponding running status data, that is, the sparse processing method includes the sampling method and the sparse processing method. The sampling data is obtained by the charging pile based on the running status data, and the sparse data is obtained by the charging pile based on the sparse processing method.

[0074] In a possible embodiment, a sparse processing method for the running status data is determined according to the data feature type of the running status data, including: if the data feature type of the running status data is a smooth data type, one of a random Gaussian matrix, a random Bernoulli matrix or a Toeplitz matrix is ​​determined as a measurement matrix of the running status data; if the data feature type of the running status data is a local feature data type, one of a random Gaussian matrix or a partial Fourier matrix is ​​determined as the measurement matrix of the running status data; if the data feature type of the running status data is a specific frequency data type, one of a random Gaussian matrix or a partial Fourier matrix is ​​determined as the measurement matrix of the running status data.

[0075] Specifically, in the embodiment of the present application, based on the data feature type of the running status data, different measurement matrices are used for running status data of different data feature types to obtain sampled data of the running status data.

[0076] For the running status data whose data feature type is a smooth data type, the corresponding sampling data is generated based on any one of a random Gaussian matrix, a random Bernoulli matrix or a Toeplitz matrix.

[0077] For the running status data whose data feature type is a local feature data type, the corresponding sampling data is generated based on any one of a random Gaussian matrix or a partial Fourier matrix.

[0078] For the operation status data whose data feature type is a specific frequency data type, the corresponding sampling data is generated based on any one of the random Gaussian matrix and the partial Fourier matrix.

[0079] See also Figure 5 , Figure 5 A schematic diagram of determining a measurement matrix of operating status data provided in an embodiment of the present application, including voltage data, current data, fault data, temperature data, power data and harmonic data. Figure 5 In the scenario shown, the data feature types of voltage data, current data, temperature data, and power data are all smooth data types, so one of the random Gaussian matrix, random Bernoulli matrix, or Toeplitz matrix is ​​used as the measurement matrix of the above data; the data feature type of fault data is the local feature data type, so one of the Gaussian matrix or the partial Fourier matrix is ​​used as the measurement matrix of fault data. The data feature type of harmonic data is the specific frequency data type, so one of the Gaussian matrix or the partial Fourier matrix is ​​used as the measurement matrix of harmonic data.

[0080] It can be seen that in the embodiment of the present application, a corresponding measurement matrix is ​​selected for the running status data according to the data feature type of the running status data to generate sampling data corresponding to the running status data, thereby improving the generation efficiency of the sampling data and further improving the transmission efficiency of the running status data.

[0081] In a possible embodiment, a sparse processing method for the running status data is determined according to the data feature type of the running status data, including: if the data feature type of the running status data is a smooth data type, discrete cosine transform is determined as the sparse processing method for the running status data; if the data feature type of the running status data is a local feature data type, discrete wavelet transform is determined as the sparse processing method for the running status data; if the data feature type of the running status data is a specific frequency data type, Fourier transform is determined as the sparse processing method for the running status data.

[0082] Specifically, in an embodiment of the present application, with respect to the data feature type of the running status data, the running status data is subjected to sparse processing according to a sparse processing method that best matches the data feature type of the running status data to generate sparse data of the running status data.

[0083] For the running status data whose data feature type is smooth data type, discrete cosine transform is used to perform sparse processing on the running status data.

[0084] For the operation status data whose data feature type is the local feature data type, discrete wavelet transform is used to perform sparse processing on the operation status data.

[0085] For the operation status data whose data feature type is a specific frequency data type, Fourier transform is used to perform sparse processing on the operation status data.

[0086] See also Figure 6 , Figure 6 A schematic diagram of a process for determining a sparse processing method for operating status data provided in an embodiment of the present application, in Figure 6 In the process shown, the data feature type of each data is first determined. The data feature type of voltage data, current data and power data is a smooth data type; the data feature type of fault data and temperature data is a local feature data type, and the feature of harmonic data is a specific frequency data type. Then, the corresponding sparse processing method is determined according to the data feature type of each data. For data feature types characterized as smooth data types, the sparse processing method of voltage data, current data and power data is discrete cosine transform; the sparse processing method of fault data and temperature data characterized as local feature data types is discrete wavelet transform; the sparse processing method of harmonic data characterized as specific frequency data types is Fourier transform.

[0087] For example, if the data feature type of the state data X[n] including 1024 data values ​​is a smooth data type, a discrete cosine transform is used to perform a sparse processing on it. The processed sparse data X[k] satisfies the following formula (2):

[0088]

[0089] Wherein, k = 0, 1, 2, 3, ..., 1023. The sparse data X[k] includes 1024 discrete cosine transform coefficients.

[0090] It can be seen that in the embodiment of the present application, a corresponding sparse processing method is selected for the running status data according to the data feature type of the running status data to obtain sparse data corresponding to the running status data, thereby improving the processing efficiency and compression rate of the sparse data, and further improving the transmission efficiency of the running status data.

[0091] S204: The charging pile sends the compressed data to the server.

[0092] Specifically, the charging pile here sends compressed data to the server, and the compressed data includes sampling data and sparse data of the operating status data. The server reconstructs the operating status data according to the sampling data and sparse data of the operating status data, and then obtains the operating status data.

[0093] In a possible embodiment, sending compressed data to a server includes: determining a data type of the operating status data, where the data type is one of voltage data, current data, fault data, temperature data, power data, or harmonic data; determining a transmission type of the operating status data according to the data type of the operating status data, where the transmission type is a periodic transmission type, a real-time transmission type, or a bidirectional transmission type; if the transmission type of the operating status data is a periodic transmission type, sending the compressed data to the server according to the HTTP protocol; if the transmission type of the operating status data is a real-time transmission type, sending the compressed data to the server according to the MQRR protocol; if the transmission type of the operating status data is a bidirectional transmission type, sending the compressed data to the server according to the WebSocket protocol.

[0094] Specifically, in the embodiment of the present application, in the process of sending the compressed data corresponding to the operation data to the server, the sending method is different for different operation status data.

[0095] The charging pile first determines the data type of the row status data, where the data type includes one of voltage data, current data, fault data, temperature data, power data or harmonic data. Based on the actual operation settings, the data type here may be more or less.

[0096] The charging pile determines the transmission type of different operating status data based on the server's demand for different types of operating status data.

[0097] For the operation status data of the periodic transmission type, such as voltage, current and other operation status data, the corresponding compressed data is sent to the server according to the HTTP protocol.

[0098] If the transmission type of the running status data is a real-time transmission type, the compressed data is sent to the server according to the MQRR protocol.

[0099] If the transmission type of the running status data is a bidirectional transmission type, the compressed data is sent to the server according to the WebSocket protocol.

[0100] It can be seen that in the embodiment of the present application, by determining the transmission type of the running status data, selecting the corresponding transmission protocol for the compressed data of the running status data, and sending the compressed data of the running status data to the server through the optimal transmission protocol, the transmission efficiency of the running status data is further improved.

[0101] In a possible embodiment, obtaining the operating status data of the charging pile includes: obtaining the installation scenario information of the charging pile, determining a first frequency and a second frequency for obtaining the operating status data according to the installation scenario information, the first frequency being higher than the second frequency; obtaining time information, and determining the current operating stage of the charging pile according to the time information; if the current operating stage of the charging pile is the daytime operating stage, obtaining the operating status data of the charging pile according to the second frequency; if the operating stage of the charging pile is the nighttime operating stage, obtaining the operating status data of the charging pile according to the first frequency.

[0102] In the above application embodiment, a method for improving the transmission efficiency of the running status data by performing sparse processing on the running status data and then sending it to the server is described. Based on this, a method for improving the transmission efficiency in terms of obtaining the running status data is also provided in the embodiment of the present application. Figure 7 , Figure 7 A flow chart of another method for sending operating status data of a charging pile provided in an embodiment of the present application includes steps S701-S707:

[0103] S701: The charging pile obtains installation scenario information of the charging pile, and determines a first frequency and a second frequency for obtaining operation status data according to the installation scenario information, wherein the first frequency is higher than the second frequency.

[0104] Specifically, during the operation of the charging pile, the charging pile will obtain the values ​​of the operating status data at different time points at a certain frequency, and send multiple operating status data of the same type to the server at regular intervals, or send the obtained operating status data to the server in real time. The first frequency here is higher than the second frequency. The first frequency is suitable for scenarios with higher safety operation requirements, and the second frequency is suitable for scenarios with daily safety operation requirements.

[0105] Here, the appropriate acquisition frequency for the corresponding scenario is determined according to the installation scenario information of the charging pile. Specifically, the safety factor of the charging pile is determined by the installation scenario information of the charging pile. The higher the safety factor, the more stringent the safe operation requirements of the charging pile. For example, if the installation scenario information includes a safety factor for installation in a public scenario, it is greater than the safety factor for installation in a single-person use scenario. After determining the safety factor according to the installation scenario information of the charging pile, the first frequency and the second frequency are determined based on the corresponding relationship between the safety factor and the acquisition frequency. The safety factor is positively correlated with the acquisition frequency.

[0106] S702: The charging pile obtains time information, and determines the current operation stage of the charging pile according to the time information.

[0107] Specifically, the time information here is the current time, and the time information is used to determine the operation stage of the charging pile, as well as the daytime operation stage and the nighttime operation stage.

[0108] Furthermore, if the charging pile records the current operating stage, the current operating stage of the charging pile is directly determined.

[0109] S703: If the current operation stage of the charging pile is the daytime operation stage, the charging pile obtains the operation status data of the charging pile according to the second frequency.

[0110] Specifically, during the daytime operation stage, a large number of relevant personnel are able to monitor the charging piles during their operation, so the potential safety hazard during the daytime operation stage is relatively low. At this time, the operation status data is obtained according to the lower second frequency.

[0111] S704: If the operation phase of the charging pile is the night operation phase, the charging pile obtains the operation status data of the charging pile according to the first frequency.

[0112] Specifically, during the night operation stage, there are fewer relevant personnel who can detect the operating status of the charging pile, and there are higher safety risks at this time. Therefore, it is necessary to obtain the operating status data according to the higher second frequency.

[0113] It can be seen that in the embodiment of the present application, by determining different operating status acquisition frequencies for the charging pile in different operating environments and time periods, the operating status data is acquired at a lower frequency under the premise that the operating environment is relatively safe, thereby reducing the amount of data acquired in a relatively safe operating environment, thereby improving the transmission efficiency of the operating status data in a relatively safe operating environment.

[0114] S705: The charging pile determines a data feature type of the operating status data, where the data feature type is one of a smoothing data type, a local feature data type, and a specific frequency data type.

[0115] S706: The charging pile generates compressed data corresponding to the operating status data according to the sparse method corresponding to the data feature type.

[0116] S707: The charging pile sends the compressed data to the server.

[0117] For the detailed description of steps S705-S707, please refer to the description of steps S202-S204 and related descriptions, which will not be repeated here.

[0118] Furthermore, after the compressed data is sent to the server, the server reconstructs the sampled data and sparse data in the compressed data to obtain the corresponding running status data. The server specifically reconstructs the data by using methods such as orthogonal matching pursuit algorithm, compressed sampling matching pursuit, and inverse transformation.

[0119] Exemplarily, the server performs an inverse discrete cosine transform on the compressed data of the sparse running status data by an inverse transform to obtain the reconstructed running status data X r [n] satisfies the following formula (3):

[0120]

[0121] For example, the calculation process of the specific values ​​of some operating status data in formula (3) is shown in the following formulas (4) and (5):

[0122] Xr[0]=sin(0)+0.5sin(0)=0(4)

[0123] Xr[1]=sin(2T×50×0.001)+0.5sin(2π×100×0.001)≈0.58788+0.5(5)

[0124] By implementing the method in the above application embodiment, it can be seen that by analyzing the data feature types of the operating status data collected by the charging pile, different sparse processing methods are used for operating status data of different data feature types to improve the processing method of the operating status data; by adopting a transmission protocol for different data transmission types, the transmission efficiency is improved. By determining different operating status acquisition frequencies for the charging pile in different operating environments and time periods, the amount of data acquired in a safer operating environment is reduced and the acquisition efficiency is improved. This improves the overall transmission efficiency of the operating status data of the charging pile.

[0125] Based on the description of the above configuration method embodiment, the present application also provides a second charging pile 800, which can be operated at Figure 1 The first charging post 101 shown is used to perform Figure 2 and Figure 7 See the method shown in Figure 8 , Figure 8 A schematic diagram of the structure of a second charging pile provided in an embodiment of the present application, the second charging pile 800 includes:

[0126] The acquisition unit 801 is used to acquire the operation status data of the charging pile.

[0127] The determining unit 802 is used to determine the data feature type of the running status data, where the data feature type is one of a smoothing data type, a local feature data type and a specific frequency data type.

[0128] Generating unit 803: used to generate compressed data corresponding to the running status data according to the sparseness method corresponding to the data feature type.

[0129] The sending unit 804 is used to send the compressed data to the server.

[0130] In a possible embodiment, in terms of the compressed data including sampled data and sparse data of operating status data, and generating compressed data corresponding to the operating status data according to the sparse method corresponding to the data feature type, the generation unit 803 is further specifically used to: determine the measurement matrix of the operating status data according to the data feature type of the operating status data; perform compressive sampling on the operating status data according to the measurement matrix of the operating status data to generate sampled data of the operating status data; determine the sparse processing method for the operating status data according to the data feature type of the operating status data; perform sparse processing on the operating status data according to the sparse processing method for the operating status data to generate sparse data of the operating status data.

[0131] In a possible embodiment, in terms of determining the sparse processing method of the running status data according to the data feature type of the running status data, the determination unit 802 is also specifically used to: if the data feature type of the running status data is a smooth data type, then one of the random Gaussian matrix, the random Bernoulli matrix or the Toeplitz matrix is ​​determined as the measurement matrix of the running status data; if the data feature type of the running status data is a local feature data type, then one of the random Gaussian matrix or the partial Fourier matrix is ​​determined as the measurement matrix of the running status data; if the data feature type of the running status data is a specific frequency data type, then one of the random Gaussian matrix or the partial Fourier matrix is ​​determined as the measurement matrix of the running status data.

[0132] In a possible embodiment, in terms of determining the sparse processing method for the operating status data according to the data feature type of the operating status data, the determination unit 802 is also specifically used to: if the data feature type of the operating status data is a smooth data type, then determine the discrete cosine transform as the sparse processing method for the operating status data; if the data feature type of the operating status data is a local feature data type, then determine the discrete wavelet transform as the sparse processing method for the operating status data; if the data feature type of the operating status data is a specific frequency data type, then determine the Fourier transform as the sparse processing method for the operating status data.

[0133] In a possible embodiment, in terms of sending compressed data to a server, the sending unit 804 is further specifically used to: determine a data type of the operating status data, the data type being one of voltage data, current data, fault data, temperature data, power data or harmonic data; determine a transmission type of the operating status data according to the data type of the operating status data, the transmission type being a periodic transmission type, a real-time transmission type or a bidirectional transmission type; if the transmission type of the operating status data is a periodic transmission type, the compressed data is sent to the server according to the HTTP protocol; if the transmission type of the operating status data is a real-time transmission type, the compressed data is sent to the server according to the MQRR protocol; if the transmission type of the operating status data is a bidirectional transmission type, the compressed data is sent to the server according to the WebSocket protocol.

[0134] In a possible embodiment, in terms of determining the data feature type of the operating status data, the determination unit 802 is also specifically used to: analyze the operating status data according to one or more analysis methods of power spectrum density analysis, energy concentration analysis, and statistical analysis of non-zero coefficients to obtain analysis results of the operating status data; and determine the data feature type of the operating status data according to the analysis results.

[0135] In a possible embodiment, in terms of obtaining the operating status data of the charging pile, the acquisition unit 801 is also specifically used to: obtain the installation scenario information of the charging pile, determine the first frequency and the second frequency for obtaining the operating status data according to the installation scenario information, and the first frequency is higher than the second frequency; obtain time information, and determine the current operating stage of the charging pile according to the time information; if the current operating stage of the charging pile is the daytime operating stage, obtain the operating status data of the charging pile according to the second frequency; if the operating stage of the charging pile is the nighttime operating stage, obtain the operating status data of the charging pile according to the first frequency.

[0136] Based on the description of the above method embodiment and device embodiment, please refer to Fig. 9 , Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Fig. 9 The electronic device 900 shown (the electronic device 900 may be a computer device, Figure 1 The first charging pile 101 or server 102 shown includes a memory 901, a processor 902, a communication interface 903 and a bus 904. The memory 901, the processor 902 and the communication interface 903 are connected to each other through the bus 904.

[0137] The memory 901 may be a read-only memory (ROM), a static storage device, a dynamic storage device or a random access memory (RAM).

[0138] The memory 901 can store programs. When the program stored in the memory 901 is executed by the processor 902, the processor 902 and the communication interface 903 are used to execute the various steps of the method for sending the operating status data of the charging pile in the embodiment of the present application.

[0139] The processor 902 can adopt a general central processing unit (CPU), a microprocessor, an application specific integrated circuit (ASIC), a graphics processing unit (GPU) or one or more integrated circuits to execute relevant programs to implement the functions required to be performed by the units in the electronic device 900 of the embodiment of the present application, or to execute the method for sending the operating status data of the charging pile of the method embodiment of the present application.

[0140] The processor 902 may also be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the method for sending the operating status data of the charging pile of the present application may be completed by the hardware integrated logic circuit or software instructions in the processor 902. The above-mentioned processor 902 may also be a general-purpose processor, a digital signal processor (Digital Signal Processing, DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application may be directly embodied as being executed by a hardware decoding processor, or may be executed by a combination of hardware and software modules in a decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 901, and the processor 902 reads the information in the memory 901, and combines its hardware to complete the functions required to be performed by the units included in the electronic device 900 of the embodiment of the present application, or executes the method for sending the operating status data of the charging pile of the method embodiment of the present application.

[0141] The communication interface 903 uses a transceiver such as, but not limited to, a transceiver to implement communication between the electronic device 900 and other devices or a communication network. For example, data can be acquired through the communication interface 903 .

[0142] The bus 904 may include a path for transmitting information between various components of the electronic device 900 (eg, the memory 901 , the processor 902 , and the communication interface 903 ).

[0143] It should be noted that although Fig. 9 The electronic device 900 shown only shows a memory 901, a processor 902, and a communication interface 903. However, in the specific implementation process, those skilled in the art should understand that the electronic device 900 also includes other devices necessary for normal operation. At the same time, according to specific needs, those skilled in the art should understand that the electronic device 900 may also include hardware devices for implementing other additional functions. In addition, those skilled in the art should understand that the electronic device 900 may also only include the devices necessary for implementing the embodiments of the present application, and does not necessarily include Fig. 9 All devices shown in .

[0144] An embodiment of the present application further provides a chip, which includes a processor and a data interface. The processor reads instructions stored in a memory through the data interface to implement the method for sending operating status data of the charging pile.

[0145] Optionally, as an implementation method, the chip may further include a memory, in which instructions are stored, and the processor is used to execute the instructions stored in the memory. When the instructions are executed, the processor is used to execute the method for sending operating status data of the charging pile.

[0146] An embodiment of the present application also provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is executed on a computer or a processor, the computer or the processor executes one or more steps in any of the above methods.

[0147] The embodiment of the present application further provides a computer program product including instructions. When the computer program product is executed on a computer or a processor, the computer or the processor executes one or more steps in any of the above methods.

[0148] Those skilled in the art will appreciate that the functions described in conjunction with the various illustrative logic blocks, modules, and algorithm steps disclosed herein can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions described in the various illustrative logic blocks, modules, and steps can be stored or transmitted as one or more instructions or codes on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media, which corresponds to tangible media, such as data storage media, or includes any media (e.g., based on a communication protocol) that facilitates the transfer of a computer program from one place to another. In this manner, computer-readable media may generally correspond to (1) non-temporary tangible computer-readable storage media, or (2) communication media, such as signals or carrier waves. Data storage media may be any available media that can be accessed by one or more computers or one or more processors to retrieve instructions, codes, and / or data structures for implementing the techniques described in this application. A computer program product may include computer-readable media.

[0149] As an example and not limitation, such computer-readable storage media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage, flash memory, or any other medium that can be used to store the desired program code in the form of instructions or data structures and can be accessed by a computer. Also, any connection is properly referred to as a computer-readable medium. For example, if instructions are transmitted from a website, server or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio and microwave are included in the definition of media. However, it should be understood that the computer-readable storage media and data storage media do not include connections, carrier waves, signals or other temporary media, but are actually directed to non-temporary tangible storage media. As used herein, disks and optical disks include compact disks (CDs), laser optical disks, optical optical disks, digital versatile disks (DVDs), and Blu-ray disks, where disks typically reproduce data magnetically, while optical disks reproduce data optically using lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0150] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Thus, the term "processor" as used herein may refer to any of the aforementioned structures or any other structures suitable for implementing the techniques described herein. Additionally, in some aspects, the functions described by the various illustrative logic blocks, modules, and steps described herein may be provided within dedicated hardware and / or software modules configured for encoding and decoding, or incorporated in a combined codec. Moreover, the techniques may be fully implemented in one or more circuits or logic elements.

[0151] The techniques of the present application may be implemented in a variety of devices or apparatuses, including a wireless handset, an integrated circuit (IC), or a set of ICs (e.g., a chipset). Various components, modules, or units are described in this application to emphasize functional aspects of devices for performing the disclosed techniques, but do not necessarily need to be implemented by different hardware units. In fact, as described above, the various units may be combined in an encoded hardware unit in conjunction with appropriate software and / or firmware, or provided by interoperating hardware units (including one or more processors as described above).

[0152] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the specific descriptions of the corresponding steps in the aforementioned method embodiments, and will not be repeated here.

[0153] It should be understood that in the description of the present application, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship, for example, A / B can represent A or B; wherein A and B can be singular or plural. Also, in the description of the present application, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, wherein a, b, c can be single or multiple. In addition, in order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, the words "first", "second", etc. are used to distinguish the same items or similar items with substantially the same functions and effects. Those skilled in the art can understand that the words "first", "second", etc. do not limit the quantity and execution order, and the words "first", "second", etc. do not limit them to be necessarily different. Meanwhile, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete manner for ease of understanding.

[0154] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the division of the unit is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The mutual coupling, direct coupling, or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0155] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0156] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media integrations. The available medium may be a read-only memory (ROM), or a random access memory (RAM), or a magnetic medium, such as a floppy disk, a hard disk, a tape, a disk, or an optical medium, such as a digital versatile disc (DVD), or a semiconductor medium, such as a solid state disk (SSD), etc.

[0157] The above is only a specific implementation of the embodiment of the present application, but the protection scope of the embodiment of the present application is not limited thereto, and any changes or replacements within the technical scope disclosed in the embodiment of the present application should be included in the protection scope of the embodiment of the present application. Therefore, the protection scope of the embodiment of the present application should be based on the protection scope of the claims.

[0158] The device embodiments described above are merely illustrative, wherein the units and modules described as separate components may or may not be physically separated. In addition, some or all of the units and modules may be selected according to actual needs to achieve the purpose of the present embodiment. Those of ordinary skill in the art may understand and implement the present invention without creative effort.

[0159] The above is only a specific implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for sending operating status data of a charging pile, characterized in that: A charging pile used in a charging pile management system, wherein the charging pile management system further includes a server, and the method includes: Obtaining operating status data of the charging pile; Determine a data feature type of the running status data, wherein the data feature type is one of a smoothing data type, a local feature data type, and a specific frequency data type; Generate compressed data corresponding to the running status data according to a sparseness method corresponding to the data feature type; The compressed data is sent to the server.

2. The method according to claim 1, characterized in that The compressed data includes sampled data and sparse data of the running status data, and the generating of compressed data corresponding to the running status data according to the sparse mode corresponding to the data feature type includes: Determining a measurement matrix of the operating status data according to a data feature type of the operating status data; Performing compression sampling on the operating status data according to the measurement matrix of the operating status data to generate sampling data of the operating status data; Determining a sparse processing method for the operating status data according to a data feature type of the operating status data; The operating status data is subjected to sparse processing according to the sparse processing method of the operating status data to generate sparse data of the operating status data.

3. The method according to claim 2, characterized in that The determining of the sparse processing method of the running status data according to the data feature type of the running status data includes: If the data feature type of the operating status data is a smooth data type, determining one of a random Gaussian matrix, a random Bernoulli matrix or a Toeplitz matrix as a measurement matrix of the operating status data; If the data feature type of the operating status data is a local feature data type, determining one of a random Gaussian matrix or a partial Fourier matrix as a measurement matrix of the operating status data; If the data feature type of the operating status data is a specific frequency data type, one of a random Gaussian matrix or a partial Fourier matrix is ​​determined as a measurement matrix of the operating status data.

4. The method according to claim 2, characterized in that: The determining of the sparse processing method of the running status data according to the data feature type of the running status data includes: If the data feature type of the operating status data is a smooth data type, discrete cosine transform is determined as a sparse processing method for the operating status data; If the data feature type of the operating status data is a local feature data type, discrete wavelet transform is determined as a sparse processing method for the operating status data; If the data feature type of the operating status data is a specific frequency data type, Fourier transform is determined as the sparse processing method for the operating status data.

5. The method according to claim 1, characterized in that The sending the compressed data to the server comprises: Determine a data type of the operating status data, the data type being one of voltage data, current data, fault data, temperature data, power data or harmonic data; Determine a transmission type of the running status data according to a data type of the running status data, wherein the transmission type is a periodic transmission type, a real-time transmission type, or a bidirectional transmission type; If the transmission type of the running status data is a periodic transmission type, sending the compressed data to the server according to the HTTP protocol; If the transmission type of the running status data is a real-time transmission type, sending the compressed data to the server according to the MQRR protocol; If the transmission type of the running status data is a bidirectional transmission type, the compressed data is sent to the server according to the WebSocket protocol.

6. The method according to any one of claims 1 to 5, characterized in that: The determining of the data feature type of the running status data includes: Analyze the operating status data according to one or more analysis methods selected from power spectrum density analysis, energy concentration analysis, and statistical analysis of non-zero coefficients to obtain analysis results of the operating status data; The data feature type of the operating status data is determined according to the analysis result.

7. The method according to any one of claims 1 to 5, characterized in that: The obtaining of the operating status data of the charging pile includes: Acquire installation scenario information of the charging pile, and determine a first frequency and a second frequency for acquiring the operating status data according to the installation scenario information, wherein the first frequency is higher than the second frequency; Acquire time information, and determine the current operation stage of the charging pile according to the time information; If the current operation stage of the charging pile is the daytime operation stage, obtaining the operation status data of the charging pile according to the second frequency; If the operation stage of the charging pile is the night operation stage, the operation status data of the charging pile is obtained according to the first frequency.

8. A charging pile, characterized in that: The charging pile belongs to a charging pile management system, which also includes a server. The charging pile includes: Acquisition unit: used to acquire the operation status data of the charging pile; A determination unit: used for determining a data feature type of the running status data, wherein the data feature type is one of a smoothing data type, a local feature data type and a specific frequency data type; A generating unit: used for generating compressed data corresponding to the running status data according to a sparse mode corresponding to the data feature type; Sending unit: used for sending the compressed data to the server.

9. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for executing the steps in the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 7.

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