An intelligent operation and maintenance method and system for charging piles based on Internet of Things technology

Abstract

The present invention discloses an intelligent operation and maintenance method and system for charging piles based on Internet of Things technology, belonging to the technical field of operation and maintenance. Centering on the charging station, the charging piles within the charging station are overall planned; the charging station and the charging piles are respectively configured with main and sub-topology interfaces; the observation time nodes are configured, and cycle marks are attached to the observation time nodes; a station scanning slice matrix model is constructed, and the operating states of each charging pile are recorded in the form of a Boolean matrix; the abnormal correlation degree of the charging station at the cycle mark is analyzed, and the abnormal trigger node is calibrated through the minimum abnormal correlation degree; the abnormal trigger probability of the charging station at the observation time node is analyzed, and the observation node and the charging station are locked through the abnormal trigger probability; the present invention observes the operating state of the charging pile based on the interface protocol technology, and regularly monitors the abnormal conditions of the charging pile by configuring the observation time node, so as to realize the centralized operation and maintenance of the charging piles in the scattered charging stations and improve the operation and maintenance efficiency of the charging piles.

Description

Technical Field

[0001] The present invention relates to the technical field of operation and maintenance, and particularly to an intelligent operation and maintenance method and system for charging piles based on Internet of Things technology. Background Art

[0002] With the booming development of the new energy vehicle industry, as a key facility for energy supply of electric vehicles, the operation and maintenance efficiency and safety of charging piles have been increasingly emphasized. The operation and maintenance technology of charging piles not only concerns the daily operation and maintenance of equipment, but also involves the reliability, safety and compatibility of data communication during the charging process. In this context, interface protocol technology has become an indispensable key element in the field of charging pile operation and maintenance;

[0003] In the prior art, the applications of interface protocols in the operation and maintenance of charging piles include: communication protocols. The communication between charging piles and electric vehicles usually follows specific communication protocols, such as CAN bus, TCP / IP, etc., to ensure the accurate transmission and parsing of data during the charging process, which is the basis for realizing functions such as charging control, status monitoring and fault diagnosis; management protocols. The communication between charging piles and the charging management system (CMS) follows specific management protocols, such as OCPP (Open Charge Point Protocol), etc., to realize functions such as remote monitoring, configuration update, charging management and fault reporting of charging piles, improving the efficiency and convenience of operation and maintenance; payment protocols. The communication between charging piles and payment platforms follows specific payment protocols, such as the API interfaces of third-party payment platforms, etc., to realize functions such as automatic charging, payment confirmation and settlement of charging piles, enhancing the charging experience and payment security of users;

[0004] In view of this, the operation status of the interface protocol can reflect the operation status of the charging pile. For example, charging control is achieved through the communication protocol to change the charging efficiency, software upgrade is achieved through the management protocol, and the payment protocol closes the single charging process, etc. Furthermore, different operation states of the charging pile require different interface protocols for interactive feedback, and the feedback process affects the operation and maintenance efficiency of the charging pile. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent operation and maintenance method and system for charging piles based on Internet of Things technology to solve the problems raised in the above background art.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] An intelligent operation and maintenance system for charging piles based on Internet of Things technology, the system includes: a status interface module, an exception record module, an exception calibration module and an intelligent operation and maintenance processing module;

[0008] The state interface module is used to take the charging station as the center, overall arrange all charging piles in the charging station to form a sample source data set; centrally configure the main topology interface for the charging station, configure the sub-topology interface for the charging pile, and generate an interface merge set;

[0009] The exception record module is used to configure the observation time node with a day as the cycle unit and attach a cycle mark to the observation time node; based on the sample source data set and the interface merge set, construct a station scanning slice matrix model, and record the operating status of each charging pile in the form of a Boolean matrix;

[0010] The exception calibration module analyzes the abnormal correlation degree of the charging station at the cycle mark based on the Boolean matrix, and calibrates the abnormal trigger node with the minimum abnormal correlation degree;

[0011] The intelligent operation and maintenance processing module analyzes the abnormal trigger probability of the charging station at the observation time node based on the abnormal trigger node, and locks the observation node and the charging station through the abnormal trigger probability.

[0012] Further, the state interface module includes a charging pile compilation unit and an interface configuration unit;

[0013] The charging pile compilation unit is used to uniformly number the charging stations in the city and the charging piles in each charging station respectively, and record the sample source data set as , where represents the i-th charging station, represents the a-th charging pile, and A represents the total number of charging piles;

[0014] The interface configuration unit is used to retrieve the power communication Internet of Things topology structure of each charging station, capture the main topology interface and the sub-topology interface in the power communication Internet of Things topology structure, and centrally configure one main topology interface for one charging station and one sub-topology interface for one charging pile; record any i-th main topology interface as , count all interface protocols adapted by the main topology interface and generate an interface merge set, denoted as , where represents the b-th interface protocol, B represents the total number of interface protocols, the interface protocol has a built-in instruction program, the instruction program is used to scan the operating status of the charging pile, and one interface protocol is used to execute one instruction program, and one instruction program is used to execute one operating status of scanning the charging pile.

[0015] Further, the exception record module includes a scanning behavior unit and a matrix carrier unit;

[0016] The scanning behavior unit is used to initialize the time range within a day into X consecutive observation time nodes, and the time duration between every two adjacent observation time nodes is equal. Denote any r-th observation time node as , and attach a cycle mark to the observation time node . At each observation time node, scan the operating status of each charging pile in the charging station through the interface protocol, where t is the cycle serial number;

[0017] The matrix carrier unit constructs a station scanning slice matrix model based on the sample source data set and interface fusion. The row number of the station scanning slice matrix model corresponds to the serial number a of the charging pile, and the column number of the station scanning slice matrix model corresponds to the serial number b of the interface protocol; At the cycle mark , execute the interface protocol to scan the charging piles in the charging station . If the operating status of the charging pile is abnormal, lock the a-th row and b-th column in the station scanning slice matrix model through the serial number a of the charging pile and the serial number b of the interface protocol , and record the matrix element in the a-th row and b-th column of the station scanning slice matrix model as 1, and form a Boolean matrix of the station scanning slice matrix model of the charging station at the cycle mark , denoted as .

[0018] Furthermore, the abnormal calibration module includes an abnormal analysis unit and a node calibration unit;

[0019] The abnormal analysis unit is used to collect the Boolean matrices formed by the charging station at different cycle marks within the cycle , and generate an initial sample cluster, denoted as ;

[0020] Select the observation time node corresponding to the Boolean matrix in the initial sample cluster as the observation object. Taking the observation object as the cluster center point, based on the Boolean matrix , analyze and calculate the abnormal correlation degree of the charging station at the cycle mark , and the formula is as follows:

[0021] ;

[0022] In the formula, represents the charging station The abnormal correlation degree at the cycle mark represents the number of 1s contained in the logical intersection operation between the Boolean matrix and the Boolean matrix ; represents the nth observation time node and n≠r, represents the charging station The Boolean matrix of the station scanning slice matrix model at the cycle mark;

[0023] The node calibration unit is used to select the observation time node corresponding to the minimum abnormal correlation degree as the abnormal trigger node of the charging station within the cycle ;

[0024] Furthermore, the intelligent operation and maintenance processing module includes an abnormal probability statistics unit and an operation and maintenance push unit;

[0025] The abnormal probability statistics unit analyzes and calculates the abnormal trigger probability of the charging station at the observation time node based on the abnormal trigger point, where , in the formula, represents the cycle serial number, represents the number of observation time nodes that are abnormal trigger nodes within the cycle range [1, Y];

[0026] The operation and maintenance push unit is used to preset a probability threshold. If the abnormal trigger probability is greater than or equal to the probability threshold, the observation time node and the charging station are locked and sent to the intelligent operation and maintenance port; the intelligent operation and maintenance port collects all the charging stations locked at the same time node and pushes them to the operation and maintenance personnel port.

[0027] An intelligent operation and maintenance method for charging piles based on Internet of Things technology, this method includes the following steps:

[0028] Step S1: Centering on the charging station, overall arrange all the charging piles in the charging station to form a sample source data set; centrally configure the main topology interface for the charging station, configure the sub-topology interface for the charging piles, and generate an interface and fusion set;

[0029] Step S2: Taking days as the cycle unit, configure the observation time nodes and attach cycle marks to the observation time nodes; based on the sample source data set and the interface and fusion set, construct a station scanning slice matrix model, and record the operating status of each charging pile in the form of a Boolean matrix;

[0030] Step S3: Based on the Boolean matrix, analyze the abnormal correlation degree of the charging station at the cycle mark, and calibrate the abnormal trigger node through the minimum abnormal correlation degree;

[0031] Step S4: Based on the abnormal trigger node, analyze the abnormal trigger probability of the charging station at the observation time node, and lock the observation node and the charging station through the abnormal trigger probability.

[0032] Further, the specific implementation process of the step S1 includes:

[0033] Uniformly number the charging stations in the city and the charging piles in each charging station respectively, and record the sample source data set as , where represents the i-th charging station, represents the a-th charging pile, and A represents the total number of charging piles;

[0034] Retrieve the power communication Internet of Things topology structure of each charging station, capture the main topology interface and the sub-topology interface in the power communication Internet of Things topology structure, and a main topology interface is centrally configured for one charging station, and a sub-topology interface is configured for one charging pile; Denote any i-th main topology interface as , and count all the interface protocols adapted by the main topology interface , and generate an interface merge set, denoted as , where represents the b-th interface protocol, B represents the total number of interface protocols, the interface protocol has a built-in instruction program, the instruction program is used to scan the running status of the charging pile, and one interface protocol is used to execute one instruction program, and one instruction program is used to execute one running status of scanning the charging pile.

[0035] Further, the specific implementation process of the step S2 includes:

[0036] Initialize the time range within a day as X consecutive observation time nodes, and the time duration between every two adjacent observation time nodes is equal. Denote any r-th observation time node as , and attach a cycle mark to the observation time node , and scan the running status of each charging pile in the charging station through the interface protocol at each observation time node, where t is the cycle serial number;

[0037] Based on the sample source data set and the interface merge set, construct a station scan slice matrix model. The row number of the station scan slice matrix model corresponds to the serial number a of the charging pile, and the column number of the station scan slice matrix model corresponds to the serial number b of the interface protocol; At the cycle mark At this point, execute the interface protocol to scan the charging piles in the charging station . When scanning the charging piles, if the operating status of a charging pile is abnormal, then lock the a-th row and b-th column in the station scanning slice matrix model of the charging station through the serial number a of the charging pile and the serial number b of the interface protocol . Record the matrix element in the a-th row and b-th column of the station scanning slice matrix model as 1, and form a Boolean matrix of the station scanning slice matrix model of the charging station at the cycle mark , denoted as .

[0038] Furthermore, the specific implementation process of step S3 includes:

[0039] During the cycle , collect the Boolean matrices formed by the charging station at different cycle marks, and generate an initial sample cluster, denoted as ;

[0040] Select the observation time node corresponding to the Boolean matrix in the initial sample cluster as the observation object. Taking the observation object as the cluster center point, based on the Boolean matrix , analyze and calculate the abnormal correlation degree of the charging station at the cycle mark . The formula is as follows:

[0041] ;

[0042] In the formula, represents the abnormal correlation degree of the charging station at the cycle mark , represents the number of 1s included in the logical intersection operation between the Boolean matrix and the Boolean matrix , represents the n-th observation time node and n≠r, represents the charging station at the cycle mark ;

[0043] Select the observation time node corresponding to the minimum abnormal correlation degree as the abnormal trigger node of the charging station during the cycle ;

[0044] According to the above method, the anomaly correlation degree is calculated based on the cluster cohesion principle. By taking different observation objects as the center points within the cluster, the key observation time nodes are calibrated. If the cycle mark has the smallest anomaly correlation degree, it means that taking the cycle mark as the center point within the cluster, the better the clustering effect is obtained. Furthermore, it means that there is a high probability that the charging station will trigger a large-scale charging pile anomaly event at the cycle mark .

[0045] Furthermore, the specific implementation process of step S4 includes:

[0046] Based on the anomaly trigger point, analyze and calculate the anomaly trigger probability of the charging station at the observation time node , where , in the formula, represents the cycle number, represents the number of anomaly trigger nodes of the observation time node within the cycle range [1, Y];

[0047] Preset a probability threshold. If the anomaly trigger probability is greater than or equal to the probability threshold, then lock the observation time node and the charging station , and send them to the intelligent operation and maintenance port; the intelligent operation and maintenance port collects all the charging stations locked at the same time node and pushes them to the operation and maintenance personnel port.

[0048] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: In a charging pile intelligent operation and maintenance method and system based on the Internet of Things technology provided by the present invention, taking the charging station as the center, the charging piles within the charging station are coordinated; the main and sub-topology interfaces are respectively configured for the charging station and the charging piles; the observation time nodes are configured, and cycle marks are attached to the observation time nodes; a station scanning slice matrix model is constructed, and the operating states of each charging pile are recorded in the form of a Boolean matrix; the anomaly correlation degree of the charging station at the cycle mark is analyzed, and the anomaly trigger node is calibrated through the smallest anomaly correlation degree; the anomaly trigger probability of the charging station at the observation time node is analyzed, and the observation node and the charging station are locked through the anomaly trigger probability; the present invention observes the operating state of the charging pile based on the interface protocol technology, and regularly monitors the anomalies of the charging pile by configuring the observation time node, so as to realize the centralized operation and maintenance of the charging piles in the scattered charging stations and improve the operation and maintenance efficiency of the charging piles. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention.

[0050] Figure 1 It is a schematic structural diagram of an intelligent operation and maintenance system for charging piles based on Internet of Things technology according to the present invention;

[0051] Figure 2 It is a schematic diagram of the steps of an intelligent operation and maintenance method for charging piles based on Internet of Things technology according to the present invention. Specific embodiments

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0053] Please refer to Figure 1 , in the first embodiment: An intelligent operation and maintenance system for charging piles based on Internet of Things technology is provided. The system includes: a status interface module, an exception record module, an exception calibration module, and an intelligent operation and maintenance processing module;

[0054] The status interface module is used to take the charging station as the center, overall arrange all the charging piles in the charging station to form a sample source data set; centrally configure a main topology interface for the charging station, configure a sub-topology interface for the charging pile, and generate an interface and integration;

[0055] Preferably, the status interface module includes a charging pile compilation unit and an interface configuration unit;

[0056] The charging pile compilation unit is used to uniformly number the charging stations in the city and the charging piles in each charging station respectively, and record the sample source data set as , where represents the i-th charging station, represents the a-th charging pile, and A represents the total number of charging piles;

[0057] The interface configuration unit is used to retrieve the power communication Internet of Things topology structure of each charging station, capture the main topology interface and sub-topology interface in the power communication Internet of Things topology structure, and centrally configure one main topology interface for one charging station and one sub-topology interface for one charging pile; record any i-th main topology interface as , count all the interface protocols adapted by the main topology interface , and generate an interface and integration, denoted as , where Denote the b-th interface protocol, where B represents the total number of interface protocols. The interface protocol has a built-in instruction program for scanning the operating status of the charging pile, and one interface protocol is used to execute one instruction program, and one instruction program is used to execute scanning one operating status of the charging pile;

[0058] An exception recording module, which is used to configure the observation time nodes with a day as the cycle unit and attach cycle marks to the observation time nodes; based on the sample source data set and the interface fusion set, construct a station scanning slice matrix model, and record the operating status of each charging pile in the form of a Boolean matrix;

[0059] Preferably, the exception recording module includes a scanning behavior unit and a matrix carrier unit;

[0060] The scanning behavior unit is used to initialize the time range within a day into X consecutive observation time nodes, and the time duration between any two adjacent observation time nodes is equal. Denote any r-th observation time node as , and attach a cycle mark to the observation time node , and scan the operating status of each charging pile in the charging station through the interface protocol at each observation time node, where t is the cycle serial number;

[0061] The matrix carrier unit constructs a station scanning slice matrix model based on the sample source data set and the interface fusion set. The row number of the station scanning slice matrix model corresponds to the serial number a of the charging pile, and the column number of the station scanning slice matrix model corresponds to the serial number b of the interface protocol; at the cycle mark , execute the interface protocol to scan the charging piles in the charging station . When the operating status of the charging pile is abnormal, lock the a-th row and b-th column in the station scanning slice matrix model through the serial number a of the charging pile and the serial number b of the interface protocol , and record the matrix element in the a-th row and b-th column in the station scanning slice matrix model as 1, and form the Boolean matrix of the station scanning slice matrix model of the charging station at the cycle mark , denoted as ;

[0062] The exception calibration module analyzes the abnormal correlation degree of the charging station at the cycle mark based on the Boolean matrix, and calibrates the abnormal trigger node through the minimum abnormal correlation degree;

[0063] Preferably, the exception calibration module includes an exception analysis unit and a node calibration unit;

[0064] ​Abnormal analysis unit, used to analyze the cycle Inside, collect charging stations The Boolean matrix formed at different cycle marks and the initialization sample clusters are generated, denoted as ;

[0065] Initializing the sample cluster Select the Boolean matrix Corresponding observation time node As the observation object, the observation object is the cluster center point, based on the Boolean matrix , analyze and calculate charging stations In cycle marking The abnormal correlation degree at is as follows:

[0066] ;

[0067] In the formula, Indicates charging station In cycle marking The abnormal correlation at Represents a Boolean matrix With Boolean matrices The logical intersection operation contains the number of 1s, represents the nth observation time node and n≠r, Indicates charging station In cycle marking Boolean matrix of the station scan slice matrix model at;

[0068] Node calibration unit, used to select the observation time node corresponding to the minimum abnormal correlation As a charging station In cycle The abnormal trigger node within;

[0069] The intelligent operation and maintenance processing module analyzes the abnormal trigger probability of the charging station at the observation time node based on the abnormal trigger node, and locks the observation node and charging station according to the abnormal trigger probability;

[0070] Preferably, the intelligent operation and maintenance processing module includes an abnormal probability statistics unit and an operation and maintenance push unit;

[0071] The abnormal probability statistics unit analyzes and calculates the charging station based on the abnormal trigger point At the observation time point The probability of abnormal triggering , where Indicates the cycle number, Indicates observing time nodes within the period range [1, Y] The number of nodes that trigger the exception;

[0072] An operation and maintenance push unit is used to preset a probability threshold. If the abnormal trigger probability is greater than or equal to the probability threshold, the observation time node and the charging station will be locked and sent to the intelligent operation and maintenance port; the intelligent operation and maintenance port collects all the charging stations locked at the same time node and pushes them to the operation and maintenance personnel port.

[0073] Please refer to Figure 2 In the second embodiment: A method for intelligent operation and maintenance of charging piles based on Internet of Things technology is provided. The method includes the following steps:

[0074] Step S1: Centering on the charging station, overall planning all the charging piles in the charging station to form a sample source data set; centrally configuring a main topology interface for the charging station, configuring a sub-topology interface for the charging pile, and generating an interface merge set;

[0075] Exemplarily, the charging stations in the city and the charging piles in each charging station are uniformly numbered, and the sample source data set is denoted as , where represents the i-th charging station, represents the a-th charging pile, and A represents the total number of charging piles;

[0076] Retrieve the power communication Internet of Things topology structure of each charging station, capture the main topology interface and sub-topology interface in the power communication Internet of Things topology structure, and centrally configure one main topology interface for one charging station and one sub-topology interface for one charging pile; denote any i-th main topology interface as , count all the interface protocols adapted to the main topology interface and generate an interface merge set, denoted as , where represents the b-th interface protocol, B represents the total number of interface protocols, the interface protocol has a built-in instruction program, the instruction program is used to scan the operating status of the charging pile, and one interface protocol is used to execute one instruction program, and one instruction program is used to execute one operating status of scanning the charging pile.

[0077] Step S2: Using a day as a cycle unit, configure the observation time node and attach a cycle mark to the observation time node; based on the sample source data set and the interface merge set, construct a station scanning slice matrix model and record the operating status of each charging pile in the form of a Boolean matrix;

[0078] Exemplarily, initialize the time range within a day as X consecutive observation time nodes, and the time duration between every two adjacent observation time nodes is equal. Denote any r-th observation time node as , for the observation time node Attach a cycle marker , and scan the operating status of each charging pile in the charging station through the interface protocol at each observation time node, where t is the cycle number;

[0079] Based on the sample source data set and the interface fusion set, construct a station scan slice matrix model. The row number of the station scan slice matrix model corresponds to the serial number a of the charging pile, and the column number of the station scan slice matrix model corresponds to the serial number b of the interface protocol; At the cycle marker , execute the interface protocol to scan the charging piles in the charging station . When scanning, if the operating status of the charging pile is abnormal, lock the a-th row and b-th column in the station scan slice matrix model through the serial number a of the charging pile and the serial number b of the interface protocol , and record the matrix element in the a-th row and b-th column of the station scan slice matrix model as 1, and form a boolean matrix of the station scan slice matrix model of the charging station at the cycle marker , denoted as .

[0080] Step S3: Based on the boolean matrix, analyze the abnormal correlation degree of the charging station at the cycle marker, and calibrate the abnormal trigger node through the minimum abnormal correlation degree;

[0081] Exemplarily, within the cycle , collect the boolean matrices formed by the charging station at different cycle markers, and generate an initial sample cluster, denoted as ;

[0082] Select the observation time node corresponding to the boolean matrix in the initial sample cluster as the observation object. Taking the observation object as the cluster center point, based on the boolean matrix , analyze and calculate the abnormal correlation degree of the charging station at the cycle marker , and the formula is as follows:

[0083] ;

[0084] In the formula, represents the abnormal correlation degree of the charging station at the cycle marker , represents the boolean matrix and the boolean matrix The number of 1s included in the logical intersection operation among them Indicates the nth observation time node and n≠r Indicates the charging station At the cycle mark The Boolean matrix of the station scan slice matrix model at the place;

[0085] Select the observation time node corresponding to the smallest abnormal correlation degree As the charging station During the cycle The abnormal trigger node within it.

[0086] Step S4: Based on the abnormal trigger node, analyze the abnormal trigger probability of the charging station at the observation time node, and lock the observation node and the charging station through the abnormal trigger probability;

[0087] Exemplarily, based on the abnormal trigger point, analyze and calculate the charging station At the observation time node The abnormal trigger probability at the time , where Represents the cycle number Represents the observation time node within the cycle range [1, Y] Is the number of abnormal trigger nodes;

[0088] The preset probability threshold. If the abnormal trigger probability Is greater than or equal to the probability threshold, then lock the observation time node And the charging station , and send it to the intelligent operation and maintenance port; The intelligent operation and maintenance port collects all the charging stations locked at the same time node and pushes them to the operation and maintenance personnel port.

[0089] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0090] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A charging pile intelligent operation and maintenance method based on Internet of Things technology, characterized in that: The method comprises the following steps: Step S1: With the charging station as the center, coordinate all charging piles in the charging station to form a sample source data set; centrally configure the main topology interface for the charging station, configure the sub-topology interface for the charging pile, and generate and integrate the interface; Step S2: Taking the day as the cycle unit, configure the observation time node, and add a period mark to the observation time node; based on the sample source data set and interface fusion set, build the station scanning slice matrix model, and record the operating status of each charging pile in the form of a Boolean matrix; Step S3: Based on the Boolean matrix, analyze the abnormal correlation of the charging station at the cycle mark, and calibrate the abnormal trigger node by the minimum abnormal correlation; Step S4: Based on the abnormal triggering node, analyzing the abnormal triggering probability of the charging station at the observation time node, and locking the observation node and the charging station by the abnormal triggering probability; The specific implementation process of step S3 includes: In the tth cycle T t In the i-th charging station S i The Boolean matrix formed at different cycle marks generates an initialization sample cluster, denoted as CS(S i |T t )={S i (V r |T t )|r∈[1,X]}; In the initialization sample cluster CS(S i |T t ) select the Boolean matrix S i (V r |T t ) corresponds to the rth observation time node V r As the observation object, take the observation object as the cluster center point, based on the Boolean matrix S i (V r |T t ), analyze and calculate the charging station S i During the cycle V r |T t The abnormal correlation degree at is as follows: In the formula, AD(V r |T t ) indicates charging station S i During the cycle V r |T t Abnormal correlation degree at, NUM[S i (V r |T t )∩S i (V n |T t )] represents the Boolean matrix S i (V r |T t ) and the Boolean matrix S i (V n |T t ) contains the number of 1s in the logical intersection operation, V n represents the nth observation time node and n≠r, S i (V n |T t ) indicates charging station S i During the cycle V n |T t Boolean matrix of the station scan slice matrix model at; Select the observation time node V corresponding to the minimum abnormal correlation r As a charging station S i In period T t The exception triggers the node.

2. According to claim 1, a charging pile intelligent operation and maintenance method based on Internet of Things technology is characterized in that: The specific implementation process of step S1 includes: The charging stations in the city and the charging piles in each charging station are uniformly numbered, and the sample source data set is recorded as U(S i )={C a |a∈[1,A]}, where S i represents the i-th charging station, C a represents the ath charging pile, and A represents the total number of charging piles; Retrieve the power communication Internet of Things topology structure of each charging station, capture the main topology interface and sub-topology interface in the power communication Internet of Things topology structure, and configure a main topology interface for each charging station and a sub-topology interface for each charging pile; record any i-th main topology interface as P i , statistics of main topology interface P i All interface protocols adapted, and interfaces generated and integrated, recorded as O(P i )={K b |b∈[1,B]}, where K b represents the bth interface protocol, B represents the total number of interface protocols, the interface protocol has a built-in instruction program, the instruction program is used to scan the operating status of the charging pile, and one interface protocol is used to execute one instruction program, and one instruction program is used to execute one operating status of the scanning charging pile.

3. According to claim 2, a charging pile intelligent operation and maintenance method based on Internet of Things technology is characterized in that: The specific implementation process of step S2 includes: Initialize the time range within a day to X consecutive observation time nodes, and the duration between each two adjacent observation time nodes is equal. Record any rth observation time node as V r , for the observation time node V r Additional cycle marker V r |V t , and scan the operating status of each charging pile in the charging station through the interface protocol at each observation time node, where t is the cycle number; Based on the sample source data set and the interface fusion set, a station scanning slice matrix model is constructed, wherein the row number of the station scanning slice matrix model corresponds to the serial number a of the charging pile, and the column number of the station scanning slice matrix model corresponds to the serial number b of the interface protocol; in the period mark V r |T t At, execute interface protocol K b To the charging station S i Charging pile C a When scanning, if charging pile C a If the running status is abnormal, the charging pile C a The serial number a and interface protocol K b The serial number b locks the row a and column b in the station scanning slice matrix model, and the matrix element in the row a and column b in the station scanning slice matrix model is recorded as 1, and a charging station S is formed. i During the cycle V r |T t The Boolean matrix of the station scanning slice matrix model at is denoted as S i (V r |T t ).

4. The intelligent operation and maintenance method of a charging pile based on Internet of Things technology according to claim 3 is characterized in that: The specific implementation process of step S4 includes: Analyze and calculate the charging station S based on abnormal trigger points i At the observation time node V r The probability of abnormal triggering Where Y represents the cycle number, NUM{V r |T t , t∈[1,Y]} means observing time node V in the period range [1,Y] r The number of nodes that trigger the anomaly; Preset probability threshold, if the abnormal trigger probability EP(V r ) is greater than or equal to the probability threshold, then the observation time node V is locked. r and charging station S i , and send it to the intelligent operation and maintenance port; the intelligent operation and maintenance port collects all charging stations locked at the same time node and pushes them to the operation and maintenance personnel port.

5. An intelligent operation and maintenance system for charging piles based on Internet of Things technology, characterized in that: The system includes: a status interface module, an abnormality recording module, an abnormality calibration module and an intelligent operation and maintenance processing module; The state interface module is used to coordinate all charging piles in the charging station with the charging station as the center to form a sample source data set; centrally configure the main topology interface for the charging station, configure the sub-topology interface for the charging pile, and generate and integrate the interface; The abnormal recording module is used to configure the observation time node with a day as the cycle unit, and add a period mark to the observation time node; based on the sample source data set and the interface fusion set, the station scanning slice matrix model is constructed, and the operating status of each charging pile is recorded in the form of a Boolean matrix; The abnormal calibration module analyzes the abnormal correlation of the charging station at the cycle mark based on the Boolean matrix, and calibrates the abnormal triggering node through the minimum abnormal correlation; The intelligent operation and maintenance processing module analyzes the abnormal trigger probability of the charging station at the observation time node based on the abnormal trigger node, and locks the observation node and the charging station according to the abnormal trigger probability; The abnormality calibration module includes an abnormality analysis unit and a node calibration unit; The abnormality analysis unit is used to t In the i-th charging station S i The Boolean matrix formed at different cycle marks generates an initialization sample cluster, denoted as CS(S i |T t )={S i (V r |T t )|r∈[1,X]}; In the initialization sample cluster CS(S i |T t ) select the Boolean matrix S i (V r |T t ) corresponds to the rth observation time node V r As the observation object, take the observation object as the cluster center point, based on the Boolean matrix S i (V i |T t ), analyze and calculate the charging station S i During the cycle V r |T t The abnormal correlation degree at is as follows: In the formula, AD(V r |T t ) indicates charging station S i During the cycle V r |T t Abnormal correlation degree at, NUM[S i (V r |T t )∩S i (V n |T t )] represents the Boolean matrix S i (V r |T t ) and the Boolean matrix S i (V n |T t ) contains the number of 1s in the logical intersection operation, V n represents the nth observation time node and n≠r, S i (V n |T t ) indicates charging station S i During the cycle V n |T t Boolean matrix of the station scan slice matrix model at; The node calibration unit is used to select the observation time node V corresponding to the minimum abnormal correlation degree r As a charging station S i In period T t The exception triggers the node.

6. The intelligent operation and maintenance system for charging piles based on Internet of Things technology according to claim 5 is characterized by: The state interface module includes a charging pile compilation unit and an interface configuration unit; The charging pile compilation unit is used to uniformly number the charging stations in the city and the charging piles in each charging station, and record the sample source data set as U(S i )={C a |a∈[1,A]}, where S i represents the i-th charging station, C a represents the ath charging pile, and A represents the total number of charging piles; The interface configuration unit is used to retrieve the power communication Internet of Things topology structure of each charging station, capture the main topology interface and sub-topology interface in the power communication Internet of Things topology structure, and configure a main topology interface for a charging station in a centralized manner, and configure a sub-topology interface for a charging pile; any i-th main topology interface is recorded as P i , statistics of main topology interface P i All interface protocols adapted, and interfaces generated and integrated, recorded as O(P i )={K b |b∈[1,B]}, where K b represents the bth interface protocol, B represents the total number of interface protocols, the interface protocol has a built-in instruction program, the instruction program is used to scan the operating status of the charging pile, and one interface protocol is used to execute one instruction program, and one instruction program is used to execute one operating status of the scanning charging pile.

7. The intelligent operation and maintenance system for charging piles based on Internet of Things technology according to claim 6 is characterized by: The abnormal recording module includes a scanning behavior unit and a matrix carrier unit; The scanning behavior unit is used to initialize the time range within a day into X consecutive observation time nodes, and the time length between each two adjacent observation time nodes is equal. Any rth observation time node is recorded as V r , for the observation time node V r Additional cycle marker V r |T t , and scan the operating status of each charging pile in the charging station through the interface protocol at each observation time node, where t is the cycle number; The matrix carrier unit constructs a station scanning slice matrix model based on the sample source data set and the interface fusion set, wherein the row number of the station scanning slice matrix model corresponds to the serial number a of the charging pile, and the column number of the station scanning slice matrix model corresponds to the serial number b of the interface protocol; in the period mark V r |T t At, execute interface protocol K b To the charging station S i Charging pile C a When scanning, if charging pile C a If the running status is abnormal, the charging pile C a The serial number a and interface protocol K b The serial number b locks the row a and column b in the station scanning slice matrix model, and the matrix element in the row a and column b in the station scanning slice matrix model is recorded as 1, and a charging station S is formed. i During the cycle V r |T t The Boolean matrix of the station scanning slice matrix model at is denoted as S i (V r |T t ).

8. The intelligent operation and maintenance system for charging piles based on Internet of Things technology according to claim 7 is characterized by: The intelligent operation and maintenance processing module includes an abnormal probability statistics unit and an operation and maintenance push unit; The abnormal probability statistics unit analyzes and calculates the charging station S based on the abnormal trigger point. i At the observation time node V r The probability of abnormal triggering Where Y represents the cycle number, NUM{V r |T t , t∈[1,Y]} means observing time node V in the period range [1,Y] r The number of nodes that trigger the exception; The operation and maintenance push unit is used to preset a probability threshold. If the abnormal trigger probability EP(V r ) is greater than or equal to the probability threshold, then the observation time node V is locked. r and charging station S i , and send it to the intelligent operation and maintenance port; the intelligent operation and maintenance port collects all charging stations locked at the same time node and pushes them to the operation and maintenance personnel port.

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