Charging pile management method and system

Abstract

The invention provides a charging pile management method and system.The method comprises the steps that when it is detected that a charging pile enters a working state in real time, a preset data collection network in the charging pile is activated in real time, and the data collection network comprises a plurality of data collection nodes; a corresponding target identifier is added to each data collection node, a data subset collected by each data collection node is extracted in real time according to the target identifier, and each data subset corresponds to one parameter; and each data subset is converted into a corresponding data graph based on a preset rule, and whether the charging pile is abnormal or not is judged in real time according to each data graph. According to the invention, the abnormity of the charging pile can be rapidly and accurately detected, and the management efficiency of the charging pile is correspondingly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a charging pile management method and system. Background Art

[0002] With the advancement of science and technology and the rapid development of productivity, the generation technology of new energy electric vehicles has become increasingly mature and has gradually been recognized by people, making new energy electric vehicles popular in people's daily lives and greatly facilitating people's lives.

[0003] Among them, existing new energy electric vehicles need to be charged during daily use, so people have developed corresponding charging piles, which enable people to charge new energy electric vehicles directly through the charging piles to meet people's needs.

[0004] Furthermore, in order to ensure the continuous and effective use of charging piles, most of the existing technologies will set up corresponding early warning systems inside the charging piles. However, in actual application, most of the existing early warning systems can only issue alarm information after serious problems have occurred in the charging piles, so that the staff can handle it, which will cause unnecessary losses and reduce the management efficiency of the charging piles accordingly. Summary of the invention

[0005] Based on this, the purpose of the present invention is to provide a charging pile management method and system to solve the problem that the prior art can only issue corresponding alarm information after serious problems have occurred in the charging pile, resulting in unnecessary losses and reducing the efficiency of charging pile management.

[0006] The first aspect of the embodiment of the present invention proposes:

[0007] A charging pile management method, wherein the method comprises:

[0008] When it is detected in real time that the charging pile enters the working state, a preset data acquisition network inside the charging pile is activated in real time, wherein the data acquisition network includes a plurality of data acquisition nodes;

[0009] Adding a corresponding target identifier to each of the data collection nodes, and extracting a data subset collected by each of the data collection nodes in real time according to the target identifier, each of the data subsets corresponding to a parameter;

[0010] Each of the data subsets is converted into a corresponding data graph based on preset rules, and whether the charging pile is abnormal is determined in real time based on each of the data graphs.

[0011] The beneficial effect of the present invention is that by detecting the working status of the charging pile in real time, the charging information corresponding to the current charging pile can be obtained. Based on this, the required data subsets can be collected in real time inside the current charging pile according to the pre-set data acquisition network. The data subset contains specific parameters. Based on this, each current data subset can be further converted into a data graph that can be intuitively judged, and finally it can be quickly and effectively determined whether there is a problem with the current charging pile based on the generated data graph, so that the abnormality of the charging pile can be discovered in time and effectively, and the loss can be avoided accordingly, while improving the management efficiency of the charging pile.

[0012] Furthermore, the step of converting each of the data subsets into a corresponding data graph based on a preset rule, and determining in real time according to each of the data graphs whether the charging pile is abnormal includes:

[0013] When the data subset is acquired in real time, the data subset is fully scanned to detect the data type corresponding to the data subset in real time;

[0014] When it is detected in real time that the data subset belongs to a first preset data type, the data subset is converted into a corresponding target curve graph in real time;

[0015] Whether the charging pile is abnormal is determined in real time according to the target curve graph.

[0016] Furthermore, the step of determining in real time whether the charging pile is abnormal according to the target curve graph includes:

[0017] When the target curve graph is acquired in real time, a first starting point corresponding to the target curve graph is detected in real time;

[0018] According to the first starting point, a plurality of maximum value points and a plurality of minimum value points appearing in sequence in the target curve graph are detected in real time, and a target difference between adjacent maximum value points and minimum value points is calculated in real time;

[0019] Determine in real time whether the target difference is less than a preset difference threshold;

[0020] If it is determined in real time that the target difference is less than the preset difference threshold, it is correspondingly determined that there is no abnormality in the charging pile.

[0021] Furthermore, the step of converting each of the data subsets into a corresponding data graph based on a preset rule, and determining in real time according to each of the data graphs whether the charging pile is abnormal includes:

[0022] When the data subset is acquired in real time, the data subset is fully scanned to detect the data type corresponding to the data subset in real time;

[0023] When it is detected in real time that the data subset belongs to a second preset data type, the data subset is converted into a corresponding target line graph in real time;

[0024] Whether the charging pile is abnormal is determined in real time according to the target line graph.

[0025] Furthermore, the step of determining in real time whether the charging pile is abnormal according to the target line graph includes:

[0026] When the target line graph is acquired in real time, a second starting point corresponding to the target line graph is detected in real time;

[0027] Detecting in real time a plurality of target line segments appearing sequentially in the target line graph according to the second starting point, and calculating in real time the slope difference between two adjacent target line segments;

[0028] Determine in real time whether the slope difference is less than a preset slope difference threshold;

[0029] If it is determined in real time that the slope difference is less than the preset slope difference threshold, it is correspondingly determined that there is no abnormality in the charging pile.

[0030] Furthermore, the step of converting each of the data subsets into a corresponding data graph based on a preset rule, and determining in real time according to each of the data graphs whether the charging pile is abnormal includes:

[0031] When the data subset is acquired in real time, the data subset is fully scanned to detect the data type corresponding to the data subset in real time;

[0032] When it is detected in real time that the data subset belongs to a third preset data type, converting the data subset into a corresponding point-and-plot diagram in real time;

[0033] It is determined in real time whether the charging pile is abnormal according to the dot plot.

[0034] Furthermore, the step of determining in real time according to the dot plot whether the charging pile is abnormal includes:

[0035] When the point-and-drawn image is acquired in real time, a target area corresponding to the point-and-drawn image is detected in real time;

[0036] Detecting in real time a number of data points sequentially generated according to the data subset, and determining in real time whether each of the data points falls within the target area;

[0037] If it is determined in real time that each of the data points falls within the target area, it is correspondingly determined that there is no abnormality with the charging pile.

[0038] The second aspect of the embodiment of the present invention proposes:

[0039] A charging pile management system, wherein the system comprises:

[0040] A collection module, used for activating a preset data collection network inside the charging pile in real time when it is detected in real time that the charging pile enters a working state, wherein the data collection network includes a plurality of data collection nodes;

[0041] An extraction module, used to add a corresponding target identifier to each of the data collection nodes, and extract the data subsets collected by each of the data collection nodes in real time according to the target identifier, each of the data subsets corresponding to a parameter;

[0042] The judgment module is used to convert each of the data subsets into a corresponding data graph based on a preset rule, and judge in real time whether the charging pile is abnormal according to each of the data graphs.

[0043] Furthermore, the judgment module is specifically used for:

[0044] When the data subset is acquired in real time, the data subset is fully scanned to detect the data type corresponding to the data subset in real time;

[0045] When it is detected in real time that the data subset belongs to a first preset data type, the data subset is converted into a corresponding target curve graph in real time;

[0046] Whether the charging pile is abnormal is determined in real time according to the target curve graph.

[0047] Furthermore, the judgment module is specifically used for:

[0048] When the target curve graph is acquired in real time, a first starting point corresponding to the target curve graph is detected in real time;

[0049] According to the first starting point, a plurality of maximum value points and a plurality of minimum value points appearing in sequence in the target curve graph are detected in real time, and a target difference between adjacent maximum value points and minimum value points is calculated in real time;

[0050] Determine in real time whether the target difference is less than a preset difference threshold;

[0051] If it is determined in real time that the target difference is less than the preset difference threshold, it is correspondingly determined that there is no abnormality in the charging pile.

[0052] Furthermore, the judgment module is specifically used for:

[0053] When the data subset is acquired in real time, the data subset is fully scanned to detect the data type corresponding to the data subset in real time;

[0054] When it is detected in real time that the data subset belongs to a second preset data type, the data subset is converted into a corresponding target line graph in real time;

[0055] Whether the charging pile is abnormal is determined in real time according to the target line graph.

[0056] Furthermore, the judgment module is specifically used for:

[0057] When the target line graph is acquired in real time, a second starting point corresponding to the target line graph is detected in real time;

[0058] Detecting in real time a plurality of target line segments appearing sequentially in the target line graph according to the second starting point, and calculating in real time the slope difference between two adjacent target line segments;

[0059] Determine in real time whether the slope difference is less than a preset slope difference threshold;

[0060] If it is determined in real time that the slope difference is less than the preset slope difference threshold, it is correspondingly determined that there is no abnormality in the charging pile.

[0061] Furthermore, the judgment module is specifically used for:

[0062] When the data subset is acquired in real time, the data subset is fully scanned to detect the data type corresponding to the data subset in real time;

[0063] When it is detected in real time that the data subset belongs to a third preset data type, converting the data subset into a corresponding point-and-plot diagram in real time;

[0064] It is determined in real time whether the charging pile is abnormal according to the dot plot.

[0065] Furthermore, the judgment module is specifically used for:

[0066] When the point-and-drawn image is acquired in real time, a target area corresponding to the point-and-drawn image is detected in real time;

[0067] Detecting in real time a number of data points sequentially generated according to the data subset, and determining in real time whether each of the data points falls within the target area;

[0068] If it is determined in real time that each of the data points falls within the target area, it is correspondingly determined that there is no abnormality with the charging pile.

[0069] The third aspect of the embodiment of the present invention proposes:

[0070] A computer comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the charging pile management method as described above when executing the computer program.

[0071] The fourth aspect of the embodiments of the present invention proposes:

[0072] A readable storage medium stores a computer program, wherein when the program is executed by a processor, the charging pile management method as described above is implemented.

[0073] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A flowchart of a charging pile management method provided by the first embodiment of the present invention;

[0075] Figure 2 This is a structural block diagram of a charging pile management system provided in the third embodiment of the present invention.

[0076] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0077] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0078] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0079] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0080] See also Figure 1 , shown is the charging pile management method provided by the first embodiment of the present invention. The charging pile management method provided by this embodiment can quickly and effectively detect abnormalities that occur in the charging pile during operation, thereby avoiding unnecessary losses and correspondingly improving the management efficiency of the charging pile.

[0081] Specifically, this embodiment provides:

[0082] A charging pile management method specifically comprises the following steps:

[0083] Step S10, when it is detected in real time that the charging pile enters a working state, a preset data acquisition network inside the charging pile is activated in real time, wherein the data acquisition network includes a plurality of data acquisition nodes;

[0084] Step S20, adding a corresponding target identifier to each of the data collection nodes, and extracting in real time according to the target identifier a data subset collected by each of the data collection nodes, each of the data subsets corresponding to a parameter;

[0085] Step S30, converting each of the data subsets into corresponding data graphs based on preset rules, and determining in real time according to each of the data graphs whether the charging pile is abnormal.

[0086] Specifically, in this embodiment, it should be noted that in order to be able to timely and effectively discover the abnormality of the charging pile during the operation of the charging pile, it is necessary to obtain the working status of the charging pile and the corresponding working information in real time and accurately. Among them, it should be pointed out that the charging pile management method provided by the present invention is implemented based on the controller set inside the charging pile, and a number of sensors are set inside the existing charging piles to collect the required data. Based on this, in the actual application process, when the controller detects in real time that the charging pile enters the charging state, that is, enters the working state, the controller will immediately enable the charging pile management method, and at the same time, it will also synchronously activate the preset data acquisition network pre-set inside the current charging pile, wherein it should be noted that the data acquisition network provided by the present invention specifically includes a number of data acquisition nodes pre-set at specific positions, and specifically, the present invention will set corresponding sensors inside each data acquisition node to collect the required data in real time for subsequent processing.

[0087] Furthermore, in the process of actual application, each data acquisition node disclosed in the present invention is respectively used to collect different parameters, that is, the functions of each data acquisition node provided by the present invention are different. Based on this, in order to facilitate the subsequent data collection, the present invention will further add a corresponding target identifier to each current data acquisition node, and further extract the data subsets collected by each current data acquisition node in real time according to the sequence of each current target identifier, that is, each current data subset contains a corresponding parameter. Preferably, the present invention will collect three parameters of current, voltage and output power respectively inside the current charging pile, that is, the present invention will generate three data subsets accordingly, and can further convert the current three data subsets into three data graphs according to pre-set rules, and finally judge whether the current charging pile is abnormal in real time according to the current three data graphs, so that the management of the charging pile can be completed quickly and effectively, which correspondingly improves the user experience.

[0088] Second embodiment

[0089] Furthermore, the step of converting each of the data subsets into a corresponding data graph based on a preset rule, and determining in real time according to each of the data graphs whether the charging pile is abnormal includes:

[0090] When the data subset is acquired in real time, the data subset is fully scanned to detect the data type corresponding to the data subset in real time;

[0091] When it is detected in real time that the data subset belongs to a first preset data type, the data subset is converted into a corresponding target curve graph in real time;

[0092] Whether the charging pile is abnormal is determined in real time according to the target curve graph.

[0093] Furthermore, the step of determining in real time whether the charging pile is abnormal according to the target curve graph includes:

[0094] When the target curve graph is acquired in real time, a first starting point corresponding to the target curve graph is detected in real time;

[0095] According to the first starting point, a plurality of maximum value points and a plurality of minimum value points appearing in sequence in the target curve graph are detected in real time, and a target difference between adjacent maximum value points and minimum value points is calculated in real time;

[0096] Determine in real time whether the target difference is less than a preset difference threshold;

[0097] If it is determined in real time that the target difference is less than the preset difference threshold, it is correspondingly determined that there is no abnormality in the charging pile.

[0098] Furthermore, the step of converting each of the data subsets into a corresponding data graph based on a preset rule, and determining in real time according to each of the data graphs whether the charging pile is abnormal includes:

[0099] When the data subset is acquired in real time, the data subset is fully scanned to detect the data type corresponding to the data subset in real time;

[0100] When it is detected in real time that the data subset belongs to a second preset data type, the data subset is converted into a corresponding target line graph in real time;

[0101] Whether the charging pile is abnormal is determined in real time according to the target line graph.

[0102] Furthermore, the step of determining in real time whether the charging pile is abnormal according to the target line graph includes:

[0103] When the target line graph is acquired in real time, a second starting point corresponding to the target line graph is detected in real time;

[0104] Detecting in real time a plurality of target line segments appearing sequentially in the target line graph according to the second starting point, and calculating in real time the slope difference between two adjacent target line segments;

[0105] Determine in real time whether the slope difference is less than a preset slope difference threshold;

[0106] If it is determined in real time that the slope difference is less than the preset slope difference threshold, it is correspondingly determined that there is no abnormality in the charging pile.

[0107] Furthermore, the step of converting each of the data subsets into a corresponding data graph based on a preset rule, and determining in real time according to each of the data graphs whether the charging pile is abnormal includes:

[0108] When the data subset is acquired in real time, the data subset is fully scanned to detect the data type corresponding to the data subset in real time;

[0109] When it is detected in real time that the data subset belongs to a third preset data type, converting the data subset into a corresponding point-and-plot diagram in real time;

[0110] It is determined in real time whether the charging pile is abnormal according to the dot plot.

[0111] Furthermore, the step of determining in real time according to the dot plot whether the charging pile is abnormal includes:

[0112] When the point-and-drawn image is acquired in real time, a target area corresponding to the point-and-drawn image is detected in real time;

[0113] Detecting in real time a number of data points sequentially generated according to the data subset, and determining in real time whether each of the data points falls within the target area;

[0114] If it is determined in real time that each of the data points falls within the target area, it is correspondingly determined that there is no abnormality with the charging pile.

[0115] In addition, in the present embodiment, it is also necessary to explain that after the required data subset is acquired in real time through the above steps, in order to objectively and accurately complete the subsequent judgment processing, it is necessary to further perform data analysis processing on the current data subset. Preferably, the present invention will first perform a full scan on the current data subset, that is, perform a full scan on the data contained in the current data subset, so as to further determine the data type corresponding to the current data subset in real time, that is, whether the current data subset corresponds to a voltage parameter, a current parameter or an output power. Based on this, in the actual judgment process, if it is detected in real time that the current data subset corresponds to a voltage parameter, the current data subset will be immediately converted into a corresponding target curve graph. Correspondingly, if it is detected in real time that the current data subset corresponds to a current parameter, the current data subset will be immediately converted into a corresponding target line graph. Correspondingly, if it is detected in real time that the current data subset corresponds to an output power, the current data subset will be immediately converted into a corresponding dot plot, and subsequent analysis will be performed to facilitate subsequent processing.

[0116] Furthermore, after the corresponding data graph types are determined respectively through the above steps, real-time analysis and processing are required. Preferably, in order to make corresponding judgments and processing quickly and effectively, if it is determined in real time through the above steps that the current data subset corresponds to the target curve graph, the first starting point corresponding to the current target curve graph in real time will be immediately detected, and starting from the current first starting point, the current target curve graph will be detected in sequence. Several maximum points and several minimum points corresponding to the current target curve graph are detected. It should be noted that the difference between the current maximum point and the current minimum point can objectively reflect whether the working state of the current charging pile is in the normal working range. Based on this, the present invention will calculate in real time whether the target difference between adjacent maximum points and minimum points is less than the preset difference threshold. Specifically, if yes, it can correspond to that the working voltage of the current charging pile is in the normal working range. Correspondingly, if not, it can be directly determined that the current charging pile has an abnormality and needs corresponding maintenance processing. Correspondingly, if the current data subset corresponds to the target line graph in real time through the above steps, similarly, the present invention will further detect the second starting point corresponding to the current target line graph, and starting from the current second starting point, sequentially detect the corresponding target line segments contained in the current target line graph, wherein it should be noted that each target line segment has a corresponding slope, and the magnitude of the slope can objectively reflect whether the working state of the current charging pile is normal. Based on this, the present invention will further calculate the target slope value corresponding to each current target line segment, and at the same time, further calculate the target slope difference between the two adjacent target line segments. Based on this, it is also necessary to determine in real time whether the current target slope difference is within the range of the preset slope difference threshold. Specifically, if so, it can be corresponding to the current charging pile in a normal working state. Correspondingly, if not, it can be corresponding to the current charging pile in an abnormal working state, and corresponding maintenance processing is required. Correspondingly, if the current data subset corresponds to the dot plot in real time through the above steps, the present invention will further detect the target area corresponding to the current dot plot, that is, the area corresponding to the charging pile when it is in a normal working state. Based on this, the present invention will determine in real time whether several data points corresponding to the current data subset all fall within the scope of the current target area. Specifically, if so, it can be corresponding to that the current charging pile is in a normal working state. Correspondingly, if not, it can be corresponding to that the working state of the current charging pile has been abnormal and needs corresponding maintenance processing. In addition, it should be noted that the above three judgment methods will be carried out at the same time, and when one of the judgment methods is abnormal, it will be directly determined that the working state of the current charging pile has been abnormal and needs corresponding processing, thereby effectively improving the management efficiency of the charging pile.

[0117] See also Figure 2, the third embodiment of the present invention provides:

[0118] A charging pile management system, wherein the system comprises:

[0119] A collection module, used for activating a preset data collection network inside the charging pile in real time when it is detected in real time that the charging pile enters a working state, wherein the data collection network includes a plurality of data collection nodes;

[0120] An extraction module, used to add a corresponding target identifier to each of the data collection nodes, and extract the data subsets collected by each of the data collection nodes in real time according to the target identifier, each of the data subsets corresponding to a parameter;

[0121] The judgment module is used to convert each of the data subsets into a corresponding data graph based on a preset rule, and judge in real time whether the charging pile is abnormal according to each of the data graphs.

[0122] Furthermore, the judgment module is specifically used for:

[0123] When the data subset is acquired in real time, the data subset is fully scanned to detect the data type corresponding to the data subset in real time;

[0124] When it is detected in real time that the data subset belongs to a first preset data type, the data subset is converted into a corresponding target curve graph in real time;

[0125] Whether the charging pile is abnormal is determined in real time according to the target curve graph.

[0126] Furthermore, the judgment module is specifically used for:

[0127] When the target curve graph is acquired in real time, a first starting point corresponding to the target curve graph is detected in real time;

[0128] According to the first starting point, a plurality of maximum value points and a plurality of minimum value points appearing in sequence in the target curve graph are detected in real time, and a target difference between adjacent maximum value points and minimum value points is calculated in real time;

[0129] Determine in real time whether the target difference is less than a preset difference threshold;

[0130] If it is determined in real time that the target difference is less than the preset difference threshold, it is correspondingly determined that there is no abnormality in the charging pile.

[0131] Furthermore, the judgment module is specifically used for:

[0132] When the data subset is acquired in real time, the data subset is fully scanned to detect the data type corresponding to the data subset in real time;

[0133] When it is detected in real time that the data subset belongs to a second preset data type, the data subset is converted into a corresponding target line graph in real time;

[0134] Whether the charging pile is abnormal is determined in real time according to the target line graph.

[0135] Furthermore, the judgment module is specifically used for:

[0136] When the target line graph is acquired in real time, a second starting point corresponding to the target line graph is detected in real time;

[0137] Detecting in real time a plurality of target line segments appearing sequentially in the target line graph according to the second starting point, and calculating in real time the slope difference between two adjacent target line segments;

[0138] Determine in real time whether the slope difference is less than a preset slope difference threshold;

[0139] If it is determined in real time that the slope difference is less than the preset slope difference threshold, it is correspondingly determined that there is no abnormality in the charging pile.

[0140] Furthermore, the judgment module is specifically used for:

[0141] When the data subset is acquired in real time, the data subset is fully scanned to detect the data type corresponding to the data subset in real time;

[0142] When it is detected in real time that the data subset belongs to a third preset data type, converting the data subset into a corresponding point-and-plot diagram in real time;

[0143] It is determined in real time whether the charging pile is abnormal according to the dot plot.

[0144] Furthermore, the judgment module is specifically used for:

[0145] When the point-and-drawn image is acquired in real time, a target area corresponding to the point-and-drawn image is detected in real time;

[0146] Detecting in real time a number of data points sequentially generated according to the data subset, and determining in real time whether each of the data points falls within the target area;

[0147] If it is determined in real time that each of the data points falls within the target area, it is correspondingly determined that there is no abnormality with the charging pile.

[0148] A fourth embodiment of the present invention provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the charging pile management method as described above when executing the computer program.

[0149] A fifth embodiment of the present invention provides a readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the charging pile management method as described above is implemented.

[0150] In summary, the charging pile management method and system provided by the above embodiments of the present invention can quickly and effectively detect anomalies generated by the charging pile during operation, thereby avoiding unnecessary losses and correspondingly improving the management efficiency of the charging pile.

[0151] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0152] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0153] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0154] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0155] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0156] The above-described embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A charging pile management method, characterized in that: The method comprises: When it is detected in real time that the charging pile enters the working state, a preset data acquisition network inside the charging pile is activated in real time, wherein the data acquisition network includes a plurality of data acquisition nodes; Adding a corresponding target identifier to each of the data collection nodes, and extracting a data subset collected by each of the data collection nodes in real time according to the target identifier, each of the data subsets corresponding to a parameter; Each of the data subsets is converted into a corresponding data graph based on preset rules, and whether the charging pile is abnormal is determined in real time based on each of the data graphs.

2. The charging pile management method according to claim 1, characterized in that: The step of converting each of the data subsets into a corresponding data graph based on a preset rule, and determining in real time according to each of the data graphs whether the charging pile is abnormal includes: When the data subset is acquired in real time, the data subset is fully scanned to detect the data type corresponding to the data subset in real time; When it is detected in real time that the data subset belongs to a first preset data type, the data subset is converted into a corresponding target curve graph in real time; Whether the charging pile is abnormal is determined in real time according to the target curve graph.

3. The charging pile management method according to claim 2, characterized in that: The step of determining in real time whether the charging pile is abnormal according to the target curve graph comprises: When the target curve graph is acquired in real time, a first starting point corresponding to the target curve graph is detected in real time; According to the first starting point, a plurality of maximum value points and a plurality of minimum value points appearing in sequence in the target curve graph are detected in real time, and a target difference between adjacent maximum value points and minimum value points is calculated in real time; Determine in real time whether the target difference is less than a preset difference threshold; If it is determined in real time that the target difference is less than the preset difference threshold, it is correspondingly determined that there is no abnormality in the charging pile.

4. The charging pile management method according to claim 1, characterized in that: The step of converting each of the data subsets into a corresponding data graph based on a preset rule, and determining in real time according to each of the data graphs whether the charging pile is abnormal includes: When the data subset is acquired in real time, the data subset is fully scanned to detect the data type corresponding to the data subset in real time; When it is detected in real time that the data subset belongs to a second preset data type, the data subset is converted into a corresponding target line graph in real time; Whether the charging pile is abnormal is determined in real time according to the target line graph.

5. The charging pile management method according to claim 4, characterized in that: The step of determining in real time whether the charging pile is abnormal according to the target line graph comprises: When the target line graph is acquired in real time, a second starting point corresponding to the target line graph is detected in real time; Detecting in real time a plurality of target line segments appearing sequentially in the target line graph according to the second starting point, and calculating in real time the slope difference between two adjacent target line segments; Determine in real time whether the slope difference is less than a preset slope difference threshold; If it is determined in real time that the slope difference is less than the preset slope difference threshold, it is correspondingly determined that there is no abnormality in the charging pile.

6. The charging pile management method according to claim 1, characterized in that: The step of converting each of the data subsets into a corresponding data graph based on a preset rule, and determining in real time according to each of the data graphs whether the charging pile is abnormal includes: When the data subset is acquired in real time, the data subset is fully scanned to detect the data type corresponding to the data subset in real time; When it is detected in real time that the data subset belongs to a third preset data type, converting the data subset into a corresponding point-and-plot diagram in real time; It is determined in real time whether the charging pile is abnormal according to the dot plot.

7. The charging pile management method according to claim 6, characterized in that: The step of determining in real time whether the charging pile is abnormal according to the dot plot includes: When the point-and-drawn image is acquired in real time, a target area corresponding to the point-and-drawn image is detected in real time; Detecting in real time a number of data points sequentially generated according to the data subset, and determining in real time whether each of the data points falls within the target area; If it is determined in real time that each of the data points falls within the target area, it is correspondingly determined that there is no abnormality with the charging pile.

8. A charging pile management system, characterized in that: The system comprises: A collection module, used for activating a preset data collection network inside the charging pile in real time when it is detected in real time that the charging pile enters a working state, wherein the data collection network includes a plurality of data collection nodes; An extraction module, used to add a corresponding target identifier to each of the data collection nodes, and extract the data subsets collected by each of the data collection nodes in real time according to the target identifier, each of the data subsets corresponding to a parameter; The judgment module is used to convert each of the data subsets into a corresponding data graph based on a preset rule, and judge in real time whether the charging pile is abnormal according to each of the data graphs.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the charging pile management method according to any one of claims 1 to 7 is implemented.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the charging pile management method as described in any one of claims 1 to 7 is implemented.

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