Charging pile intelligent operation and maintenance method and system based on Internet of Things technology

Abstract

The invention discloses a charging pile intelligent operation and maintenance method and system based on the Internet of Things technology, and belongs to the technical field of operation and maintenance. The charging station is taken as a center, and charging piles in the charging station are planned as a whole; configuring a main topology interface and a sub topology interface for the charging station and the charging pile respectively; configuring an observation time node, and adding a period mark to the observation time node; constructing a station scanning slice matrix model, and recording the operation state of each charging pile in the form of a Boolean matrix; analyzing the abnormal association degree of the charging station at the period mark, and calibrating an abnormal trigger node through the minimum abnormal association degree; analyzing the abnormal triggering probability of the charging station at the observation time node, and locking the observation node and the charging station through the abnormal triggering probability; the operation state of the charging pile is observed based on the interface protocol technology, and the abnormal condition of the charging pile is monitored regularly by configuring the observation time node, so that centralized operation and maintenance of the charging pile of the scattered charging station are realized, and the operation and maintenance efficiency of the charging pile is improved.

Description

Technical Field

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

[0002] With the vigorous development of the new energy vehicle industry, charging piles, as key facilities for electric vehicle energy replenishment, are increasingly valued for their operation and maintenance efficiency and safety. Charging pile operation and maintenance technology is not only related to the daily operation and maintenance of equipment, but also involves the reliability, security 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; In the prior art, the application of interface protocols in the operation and maintenance of charging piles includes: 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 analysis 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 charging management systems (CMS) follows specific management protocols, such as OCPP (Open Charge Point Protocol), etc., which are used to realize remote monitoring, configuration update, billing management and fault reporting of charging piles, and improve 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 interface of third-party payment platforms, etc., to realize automatic billing, payment confirmation and settlement of charging piles, which improves the user's charging experience and payment security; In view of this, the operation of the interface protocol can reflect the operating status of the charging pile, such as realizing charging control through the communication protocol, changing the charging efficiency, realizing software upgrades through the management protocol, and closing the single charging process through the payment protocol. Therefore, different operating 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

[0003] The purpose of the present invention is to provide a charging pile intelligent operation and maintenance method and system based on Internet of Things technology to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: An intelligent operation and maintenance system for charging piles based on Internet of Things technology, the system includes: a state 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.

[0005] Further, 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 ,in, represents the i-th charging station, 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 one charging station is centrally configured with one main topology interface, and one charging pile is configured with one sub-topology interface; any i-th main topology interface is recorded as , statistics of main topology interfaces All interface protocols adapted, and interfaces generated and integrated, recorded as ,in, 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.

[0006] Further, 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 , for the observation time node Additional cycle markers , 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 At, execute the interface protocol To the charging station Charging station inside When scanning, if the charging pile If the running status is abnormal, the charging pile The serial number a and interface protocol 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 is formed. In cycle marking The Boolean matrix of the station scanning slice matrix model at is denoted as .

[0007] Further, the anomaly calibration module includes an anomaly analysis unit and a node calibration unit; The abnormality analysis unit is used to Inside, collect charging stations The Boolean matrix formed at different cycle marks and the initialization sample clusters are generated, denoted as ; Initializing the sample cluster Select Boolean matrix from Corresponding observation time node As the observation object, the observation object is taken as 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: ; 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; The node calibration unit is used to select the observation time node corresponding to the minimum abnormal correlation degree As a charging station In cycle The exception triggers the node.

[0008] Furthermore, 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 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 anomaly; The operation and maintenance push unit is used to preset a probability threshold. If an abnormality triggers the probability If it is greater than or equal to the probability threshold, the observation time node is locked. and charging stations , 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.

[0009] A charging pile intelligent operation and maintenance method based on Internet of Things technology, 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, analyze the abnormal triggering probability of the charging station at the observation time node, and lock the observation node and the charging station through the abnormal triggering probability.

[0010] Furthermore, 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 ,in, represents the i-th charging station, 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 , statistics of main topology interfaces All interface protocols adapted, and interfaces generated and integrated, recorded as ,in, 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.

[0011] Furthermore, 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 , for the observation time node Additional cycle markers , 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 At, execute the interface protocol To the charging station Charging station inside When scanning, if the charging pile If the running status is abnormal, the charging pile The serial number a and interface protocol 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 is formed. In cycle marking The Boolean matrix of the station scanning slice matrix model at is denoted as .

[0012] Furthermore, the specific implementation process of step S3 includes: In cycle Inside, collect charging stations The Boolean matrix formed at different cycle marks and the initialization sample clusters are generated, denoted as ; Initializing the sample cluster Select Boolean matrix from Corresponding observation time node As the observation object, the observation object is taken as 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: ; 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; Select the observation time node corresponding to the minimum abnormal correlation As a charging station In cycle The abnormal trigger node within; According to the above method, the abnormal correlation is calculated based on the cluster cohesion principle. By taking different observation objects as the center points in the cluster, the key observation time nodes are calibrated. The abnormal correlation is the smallest, indicating that the cycle is marked The better the clustering effect is, the higher the probability that the charging station is within the cycle mark. This can trigger a wide range of abnormal charging pile events.

[0013] Furthermore, the specific implementation process of step S4 includes: Analyze and calculate charging stations based on abnormal trigger points 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; Preset probability threshold, if the abnormal trigger probability If it is greater than or equal to the probability threshold, the observation time node is locked. and charging stations , 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.

[0014] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in the intelligent operation and maintenance method and system of charging piles based on the Internet of Things technology provided by the present invention, the charging station is taken as the center to coordinate the charging piles in the charging station; the main and sub-topology interfaces are configured for the charging station and the charging piles respectively; the observation time node is configured, and the period mark is added to the observation time node; a station scanning slice matrix model is constructed to record the operating status of each charging pile in the form of a Boolean matrix; the abnormal correlation of the charging station at the period mark is analyzed, and the abnormal trigger node is calibrated by the minimum abnormal correlation; 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 by the abnormal trigger probability; the present invention observes the operating status 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 of the scattered charging stations, thereby improving the operation and maintenance efficiency of the charging piles. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0016] Figure 1 It is a structural schematic diagram of a charging pile intelligent operation and maintenance system based on Internet of Things technology of the present invention; Figure 2 It is a schematic diagram of the steps of an intelligent operation and maintenance method of a charging pile based on Internet of Things technology of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1 , In the first embodiment of the present invention: a charging pile intelligent operation and maintenance system based on the Internet of Things technology is provided, the system comprising: a state 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; Preferably, the status 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 ,in, represents the i-th charging station, 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 and a sub-topology interface for a charging pile; any i-th main topology interface is recorded as , statistics of main topology interfaces All interface protocols adapted, and interfaces generated and integrated, recorded as ,in, 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; The abnormal recording module is used to configure the observation time nodes with a daily cycle unit and add periodic marks to the observation time nodes; based on the sample source data set and 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; Preferably, 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 duration between each two adjacent observation time nodes is equal. Any rth observation time node is recorded as , for the observation time node Additional cycle markers , 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. 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, execute the interface protocol To the charging station Charging station inside When scanning, if the charging pile If the running status is abnormal, the charging pile The serial number a and interface protocol 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 is formed. In cycle marking The Boolean matrix of the station scanning slice matrix model at is denoted as ; 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; Preferably, the anomaly calibration module includes an anomaly analysis unit and a node calibration unit; 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 ; Initializing the sample cluster Select Boolean matrix from 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: ; 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; 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; 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; Preferably, 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 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; The operation and maintenance push unit is used to preset the probability threshold. If it is greater than or equal to the probability threshold, the observation time node is locked. and charging stations , 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.

[0019] See also Figure 2 In the second embodiment, a charging pile intelligent operation and maintenance method based on the Internet of Things technology is provided, and 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; For example, 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 ,in, represents the i-th charging station, 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 , statistics of main topology interfaces All interface protocols adapted, and interfaces generated and integrated, recorded as ,in, 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.

[0020] 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; For example, the time range within a day is initialized to 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 , for the observation time node Additional cycle markers , 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. 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 At, execute the interface protocol To the charging station Charging station inside When scanning, if the charging pile If the running status is abnormal, the charging pile The serial number a and interface protocol 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 is formed. In cycle marking The Boolean matrix of the station scanning slice matrix model at is denoted as .

[0021] 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; For example, in the cycle Inside, collect charging stations The Boolean matrix formed at different cycle marks and the initialization sample clusters are generated, denoted as ; Initializing the sample cluster Select Boolean matrix from 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: ; 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; Select the observation time node corresponding to the minimum abnormal correlation As a charging station In cycle The exception triggers the node.

[0022] 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; Exemplarily, based on abnormal trigger points, analyze and calculate charging stations 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 anomaly; Preset probability threshold, if the abnormal trigger probability If it is greater than or equal to the probability threshold, the observation time node is locked. and charging stations , 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.

[0023] It should be noted that, in this article, relational terms such as first and second, etc. 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 terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0024] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in 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, analyze the abnormal triggering probability of the charging station at the observation time node, and lock the observation node and the charging station through the abnormal triggering probability.

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 ,in, represents the i-th charging station, 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 a charging station and a sub-topology interface for a charging pile; record any i-th main topology interface as , statistics of main topology interfaces All interface protocols adapted, and interfaces generated and integrated, recorded as ,in, 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 , for the observation time node Additional cycle markers , 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 At, execute the interface protocol To the charging station Charging station inside When scanning, if the charging pile If the running status is abnormal, the charging pile The serial number a and interface protocol 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 is formed. In cycle marking The Boolean matrix of the station scanning slice matrix model at is denoted as .

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 S3 includes: In cycle Inside, collect charging stations The Boolean matrix formed at different cycle marks and the initialization sample clusters are generated, denoted as ; Initializing the sample cluster Select Boolean matrix from Corresponding observation time node As the observation object, the observation object is taken as 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: ； 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; Select the observation time node corresponding to the minimum abnormal correlation As a charging station In cycle The exception triggers the node.

5. The intelligent operation and maintenance method of a charging pile based on Internet of Things technology according to claim 4 is characterized in that: The specific implementation process of step S4 includes: Analyze and calculate charging stations based on abnormal trigger points 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; Preset probability threshold, if the abnormal trigger probability If it is greater than or equal to the probability threshold, the observation time node is locked. and charging stations , 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.

6. 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.

7. The intelligent operation and maintenance system for charging piles based on Internet of Things technology according to claim 6 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 ,in, represents the i-th charging station, 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 one charging station is centrally configured with one main topology interface, and one charging pile is configured with one sub-topology interface; any i-th main topology interface is recorded as , statistics of main topology interfaces All interface protocols adapted, and interfaces generated and integrated, recorded as ,in, 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.

8. The intelligent operation and maintenance system for charging piles based on Internet of Things technology according to claim 7 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 , for the observation time node Additional cycle markers , 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 At, execute the interface protocol To the charging station Charging station inside When scanning, if the charging pile If the running status is abnormal, the charging pile The serial number a and interface protocol 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 is formed. In cycle marking The Boolean matrix of the station scanning slice matrix model at is denoted as .

9. The intelligent operation and maintenance system for charging piles based on Internet of Things technology according to claim 8 is characterized by: The abnormality calibration module includes an abnormality analysis unit and a node calibration unit; The abnormality analysis unit is used to Inside, collect charging stations The Boolean matrix formed at different cycle marks and the initialization sample clusters are generated, denoted as ; Initializing the sample cluster Select Boolean matrix from Corresponding observation time node As the observation object, the observation object is taken as 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: ； 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; The node calibration unit is used to select the observation time node corresponding to the minimum abnormal correlation degree As a charging station In cycle The exception triggers the node.

10. The intelligent operation and maintenance system for charging piles based on Internet of Things technology according to claim 9 is characterized in that: 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 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; The operation and maintenance push unit is used to preset a probability threshold. If an abnormality triggers the probability If it is greater than or equal to the probability threshold, the observation time node is locked. and charging stations , 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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