CN113175947A - Charging station abnormity early warning method, intelligent operation and maintenance gateway and early warning system - Google Patents

Charging station abnormity early warning method, intelligent operation and maintenance gateway and early warning system Download PDF

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CN113175947A
CN113175947A CN202110315520.1A CN202110315520A CN113175947A CN 113175947 A CN113175947 A CN 113175947A CN 202110315520 A CN202110315520 A CN 202110315520A CN 113175947 A CN113175947 A CN 113175947A
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charging
charging station
representing
power battery
characteristic
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姜燕
王奔
李露
张一丹
邱凯义
赵毓鹏
保拉
贾云杰
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Beijing Zhongdian Feihua Communication Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/18—Status alarms
    • G08B21/185—Electrical failure alarms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/66—Arrangements for connecting between networks having differing types of switching systems, e.g. gateways
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
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Abstract

The invention provides a charging station abnormity early warning method, an intelligent operation and maintenance gateway and an early warning system, wherein the method is implemented by the intelligent operation and maintenance gateway and comprises the steps of obtaining a characteristic vector for representing the operation state of a charging station, wherein the characteristic vector comprises a first characteristic set for representing the state of a power battery of a user, a second characteristic set for representing the state of a charging pile and a third characteristic set for representing environmental factors of the charging station; identifying whether the charging station is abnormal or not according to the characteristic vector and a preset safe charging model; the safe charging model represents the mapping relation between the characteristic vector and the identification result of whether the characteristic vector belongs to the abnormity; in response to determining that the charging station is abnormal, a set processing mechanism is enabled. The method can automatically and effectively early warn the operation risk of the charging station and realize the risk control of the charging station.

Description

Charging station abnormity early warning method, intelligent operation and maintenance gateway and early warning system
Technical Field
The disclosure relates to the technical field of data processing, in particular to a charging station abnormity early warning method, and also relates to an intelligent operation and maintenance gateway and a charging station intelligent early warning system.
Background
In recent years, with the rapid development of the electric automobile industry, the construction requirements of related supporting infrastructures are continuously increased. In the process of building an electric vehicle charging station, charging equipment, sensors, monitoring equipment and the like in the station are various and different in standard, so that the 'integration' level is poor, and unified monitoring of a single platform is difficult to realize. At present, each charging station is respectively supervised on different platforms in a manner of separating charging transaction data from local environment monitoring and video monitoring data, which is not beneficial to realizing data sharing of the charging station and is contrary to the development trend of 'unified standardization of internet of things' of the national power grid.
Disclosure of Invention
In view of this, the present disclosure aims to provide a charging station abnormality early warning method, an intelligent operation and maintenance gateway, and a charging station intelligent early warning system.
Based on the above purpose, according to a first aspect of the present disclosure, there is provided a charging station abnormity early warning method, where the method is implemented by an intelligent operation and maintenance gateway, and includes:
the method comprises the steps that a characteristic vector used for representing the running state of a charging station is obtained, and the characteristic vector comprises a first characteristic set representing the state of a power battery of a user, a second characteristic set representing the state of a charging pile and a third characteristic set representing environmental factors of the charging station;
identifying whether the charging station is abnormal or not according to the characteristic vector and a preset safe charging model; the safety charging model represents the mapping relation between the characteristic vector and the identification result of whether the characteristic vector belongs to an abnormity or not;
in response to determining that the charging station is abnormal, a set processing mechanism is enabled.
In one embodiment of the present disclosure, the first set of features includes at least one of the following features characterizing the state of the user's power cell:
the temperature change rate represents the temperature change condition of the power battery within a preset time interval;
input voltage characteristics: representing the relation between the actual input voltage in the power battery and the voltage requirement of the power battery;
input current characteristics: and representing the relation between the actual input current in the power battery and the current demand of the power battery.
In one embodiment of the present disclosure, the temperature change rate is calculated by the following formula:
Figure BDA0002991347200000021
m11as rate of change of temperature, TkA is the temperature at time k, a is a fixed value, i.e. a predetermined time interval, T1Is a first temperature threshold, T2Is the second temperature threshold.
In one embodiment of the present disclosure, the second set of features includes at least one of the following features characterizing charging post status:
output voltage characteristics: representing the relation between the actual output voltage of the charging pile and the voltage requirement of the power battery;
output current characteristics: representing the relation between the actual output current of the charging pile and the current demand of the power battery;
current sharing unbalance degree: the fluctuation condition of the output current of the charging pile is represented.
In one embodiment of the present disclosure, the third feature set includes at least one of a temperature parameter collected by a temperature sensor, a humidity parameter collected by a humidity sensor, and a water immersion parameter collected by a water immersion sensor within the charging station.
In one embodiment of the present disclosure, the method further comprises:
acquiring the total power consumption collected by a gateway meter;
acquiring the transaction electric quantity of a charging pile controller;
and calculating the proportion of the difference between the total electricity consumption and the transaction electricity consumption in the total electricity consumption to obtain the loss rate of the charging station.
In one embodiment of the present disclosure, the step of generating the safe charging model includes:
determining a reference index matrix M of the safe charging model;
establishing a weight coefficient matrix A related to a reference index system according to an analytic hierarchy process;
determining a safe charging model as follows: c ═ a · M.
In an embodiment of the present disclosure, the setting-enabled processing mechanism includes at least one of:
sending warning information to a remote monitoring terminal or/and a local monitoring terminal, wherein the warning information comprises abnormal charging pile information;
and controlling the charging pile to be powered off when abnormality occurs.
According to a second aspect of the present disclosure, there is also provided an intelligent operation and maintenance gateway, including:
the charging station comprises a characteristic extraction module, a charging station charging module and a charging station charging module, wherein the characteristic extraction module is used for acquiring a characteristic vector for representing the running state of the charging station, and the characteristic vector comprises a first characteristic set for representing the state of a power battery of a user, a second characteristic set for representing the state of a charging pile and a third characteristic set for representing environmental factors of the charging station;
the abnormity identification module is used for identifying whether the charging station is abnormal or not according to the characteristic vector and a preset safe charging model; the safety charging model represents the mapping relation between the characteristic vector and the identification result of whether the characteristic vector belongs to an abnormity or not; and the number of the first and second groups,
and the abnormity processing module enables a set processing mechanism in response to determining that the charging station is abnormal.
According to a third aspect of the present disclosure, there is also provided a charging station intelligent early warning system, including:
the environment sensor is used for monitoring environmental parameters in the charging station;
the charging pile controller is used for monitoring parameters of a charging pile and a power battery connected to the charging pile;
the intelligent operation and maintenance gateway is communicated with the environment sensor and the charging pile controller to receive the environment parameters collected by the environment sensor and the parameters of the charging pile and the power battery collected by the charging pile controller;
and the monitoring terminal is communicated with the intelligent operation and maintenance gateway and is configured to receive and display the data processed by the intelligent operation and maintenance gateway.
As can be seen from the above, according to the charging station abnormity early warning method provided by the disclosure, the user power battery state, the charging pile state and the feature vector of the charging station environment, which characterize the charging station operation risk, are extracted, so that the early warning can be automatically and effectively performed on the charging station operation risk, the risk control on the charging station is realized, and the loss of operators and users is reduced.
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In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings needed to be used in the description of the embodiments or related technologies are briefly introduced below, and it is obvious that the drawings in the following description are only embodiments of the present disclosure, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a charging station abnormality warning method according to an embodiment of the present disclosure.
Fig. 2 is a schematic flow chart illustrating a process of generating a safe charging model according to an embodiment of the disclosure.
Fig. 3 is a schematic block diagram of an intelligent operation and maintenance gateway according to an embodiment of the present disclosure.
Fig. 4 is a schematic block diagram of a hardware configuration of an intelligent operation and maintenance gateway according to an embodiment of the present disclosure.
Fig. 5 is a hardware schematic block diagram of a charging station intelligent warning system according to an embodiment of the present disclosure.
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
It is to be noted that technical terms or scientific terms used in the embodiments of the present disclosure should have a general meaning as understood by those having ordinary skill in the art to which the present disclosure belongs, unless otherwise defined. The use of "first," "second," and similar terms in the embodiments of the disclosure is not intended to indicate any order, quantity, or importance, but rather to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
< method examples >
According to the charging station abnormity early warning method, the operating state of the charging station is monitored and early warned by integrating the power supply parameters of the charging pile, the environmental factors in the charging station, the parameters of the automobile power battery and other factors, and the safe operation of the charging station is guaranteed. In addition, the charging station abnormity early warning method is implemented through the intelligent operation and maintenance gateway, so that the gateway has the capacity of local data processing besides the communication function, has the function of an edge computing node, and provides technical accumulation for further building an electric power internet of things.
Fig. 1 is a schematic flow chart of a charging station abnormality warning method according to an embodiment of the present disclosure, where the method is implemented by an intelligent operation and maintenance gateway.
According to fig. 1, the method of the present embodiment may include the following steps S2100 to S2300.
Step S2100, obtaining a feature vector for representing the operation state of the charging station, wherein the feature vector comprises a first feature set representing the state of the power battery of a user, a second feature set representing the state of the charging pile, and a third feature set representing the environmental factors of the charging station.
In this embodiment, according to the characteristics of the application scenario of the charging station, the characteristics capable of characterizing the safe operation state of the charging station are set to form a characteristic vector M characterizing the operation state of the charging station. The feature vector X comprises at least one feature M characterizing the operating state of the charging stationjJ takes a natural number from 1 to N, and N represents the total number of features of the feature vector M.
The feature vector M comprises a first set of features M1Second feature set M2Third feature set M3. Wherein the first feature set M1For characterizing the state of the user's power cell, a second set of features M2Used for representing the state of the charging pile and a third characteristic set M3A charging station environment. The user power battery state, the state of charging stake, charging station environment all are relevant with the operation safety of charging station, consequently synthesize three aspect factor and come as the eigenvector X of the safe operation of sign charging station.
In this embodiment, by obtaining the feature value of each feature set, the vector values of the user power battery, the charging pile and the charging station environment for the feature vector X are obtained.
When a user's electric vehicle is charged in a charging station through a charging pile, the state of the power battery of the electric vehicle during charging affects the safety of the charging station, such as the temperature, current, voltage, and other parameters of the power battery. Therefore, the power battery of the user can be monitored, and the first feature set M for representing the state of the power battery of the user1As part of a characteristic vector M characterizing the operating state of the charging station.
In one embodiment of the present disclosure, a first set of features M characterizing the state of a user's power battery1May include a rate of temperature change m11Input voltage characteristic m12Input current characteristic m13At least one of (a). Therefore, the temperature change rate m of the user power battery during charging can be obtained11Input voltage characteristic m12Input current characteristic m13To construct the first feature set M described above1。
With respect to the characteristic temperature change rate m11Characteristic rate of change in temperature m11And characterizing the temperature change condition of the power battery in a preset time interval. For consumer power batteries, their temperature during charging can increase due to time, voltage, current, and the like. If the temperature of the user's power cell changes too quickly, there are significant safety concerns, such as spontaneous combustion.
In one embodiment of the invention, the rate of temperature change m11Can be calculated by the following formula:
Figure BDA0002991347200000051
the value calculated by the above formula is the temperature change rate m11The corresponding vector value. Wherein a is a fixed value, namely a preset time interval; t iskTemperature, T, acquired at time kk+aThe temperature collected at time k + a.
In one other embodiment of the present disclosure, when the current temperature of the user power battery is small or large, the temperature variation thereof is not indicative of the current safety risk condition. For example, when the current temperature of the user power battery is too low, the safety of the user power battery is not affected even if the temperature change rate in the preset time interval is large; however, when the temperature at a certain time is too high, the battery has a large safety risk even if the current temperature change rate is small.
Based on this, in one embodiment of the present disclosure, the rate of temperature change m characterizing the current temperature state of the user power cell11Can be calculated by the following formula:
Figure BDA0002991347200000061
wherein k is 0, 1, 2,…，Tkthe temperature collected at the moment k; a is a fixed value, namely a preset time interval; t isk+aThe temperature collected at time k + a. T is1Is a first threshold value; t is2Is the second threshold.
Acquiring the temperature of the user power battery in real time, and detecting the temperature T at the moment kkLess than a first threshold value T1When it is, then let m110. When the temperature T at the moment k is detectedkGreater than a second threshold value T2When it is, then let m111. Temperature T at time kkAt a first threshold value T1A second threshold value T2In between, then according to
Figure BDA0002991347200000062
And (6) performing calculation.
In one embodiment of the present disclosure, the first threshold value T1Can be selected from 0-30 deg.C, and has second threshold value T1Can be selected from 40-60 deg.C.
In one embodiment of the present disclosure, when following
Figure BDA0002991347200000063
Calculating the current temperature change rate m11Then, it is judged whether the temperature change rate calculated according to the formula reaches the third threshold value T3If it is, m is111 is ═ 1; if not, according to the current calculated value as m11The vector value of (a).
In one embodiment of the present disclosure, the third threshold value T3Can be selected from 0.1-0.6; for example when the third threshold value T3Taking 0.5 as the calculated temperature change rate m11When the value is more than 0.5, let m11＝1。
Calculating the current temperature change rate m11The appropriate time interval may be selected as desired. In one embodiment of the present invention, the preset time interval a may optionally be one value in 1-5 min.
About input voltage characteristic m12It refers to the difference between the actual input voltage and the required voltage. If the actual input value is smaller than the requirement, the current charging setting cannot meet the requirement of the charging vehicleIf so, the charging time can be prolonged; if the actual input value is greater than the demand, it indicates that the battery of the charging vehicle is over-voltage, and there may be a charging risk.
In one embodiment of the present disclosure, an input voltage characteristic m of a consumer-powered battery is characterized12Can be calculated by the following formula:
Figure BDA0002991347200000071
wherein, UiIs the actual input voltage, U 'of the user power battery'iThe voltage requirement of the power battery for the user.
About input current characteristic m13It refers to the difference between the actual input current and the demanded current. If the actual value is smaller than the requirement, the current charging setting cannot meet the requirement of the charging vehicle, and the charging time length is prolonged; if the actual value is larger than the requirement, the situation that the battery of the charging vehicle is in an overcurrent state is indicated, and the charging risk may exist.
In one embodiment of the present disclosure, an input current characteristic m of a consumer-powered battery is characterized13 can beCalculated by the following formula:
Figure BDA0002991347200000072
wherein, IiIs the actual input current, I 'of the user power battery'iThe demand for current from the battery is made available to the user.
Through the above embodiments, it is possible to determine the first feature set M obtained to characterize the current state of the user's power battery1I.e. M1＝(m11，m12，m13) And calculating the vector value of each characteristic through the formula.
Each parameter of the user power battery can be obtained through a designated interface, for example, after the electric vehicle of the user is connected to the charging pile, the corresponding parameter can be obtained through communication between the charging pile controller and a BMS (battery management system) of the electric vehicle.
When a user's electric vehicle is connected to the charging pile for charging, the current state of the charging pile influences the safety of the charging station, for example, when the output current and the output voltage of the charging pile are too large, a large potential safety hazard exists. Therefore, the state of the charging pile can be monitored, and a second characteristic set M of the state of the charging pile is represented2As part of a feature vector M characterizing the operating state of the charging station.
In one embodiment of the invention, a second feature set M for characterizing the state of the charging pile2May include an output voltage characteristic m21Output current characteristic m22Current sharing unbalance m23At least one of (a). Therefore, the output voltage characteristic m of the charging pile during charging can be obtained21Output current characteristic m22Current sharing unbalance m23To construct the second feature set M as described above2。
About the output voltage characteristic m21It refers to the difference between the actual output voltage and the required voltage. If the actual value is smaller than the requirement, the current charging setting cannot meet the requirement of the charging vehicle, and the charging time length is prolonged; if the actual value is greater than the demand, it indicates that the battery of the charging vehicle is over-voltage, and there may be a risk of charging.
In one embodiment of the present disclosure, a charging pile output voltage characteristic m is characterized21Can be calculated by the following formula:
Figure BDA0002991347200000081
wherein, UoIs the actual output voltage, U 'of the charging pile during power supply'oThe voltage requirements for the user's power cell.
About output current characteristic m22It refers to the difference between the actual output current and the demand current. If the actual value is smaller than the requirement, the charging setting cannot meet the requirement of the charging vehicle, and the charging time is prolonged; if it is actualA value greater than the demand indicates an overcurrent condition in the battery of the charging vehicle, which may present a safety risk.
In one embodiment of the present disclosure, a characteristic m of an output current of a charging pile is characterized22Can be calculated by the following formula:
Figure BDA0002991347200000082
wherein, IoIs the actual output current, I 'of the charging pile during charging'oThe demand for current from the battery is made available to the user.
With respect to the current sharing unbalance m23And the condition of fluctuation of the output current of the charging pile is reflected. If the current fluctuation condition of the charging pile is large, the potential safety hazard of the charging pile is indicated.
In one embodiment of the invention, the reactive charging pile current sharing unbalance m23Can be calculated by the following formula:
Figure BDA0002991347200000083
wherein, IMFor limiting the output current of the charging pile, IAAverage value of output current for charging pile, INThe rated current of the charging pile.
Through the embodiment, the second feature set M for representing the current state of the charging pile can be determined and obtained2I.e. M2＝(m21，m22，m23) And calculating vector values corresponding to the characteristics through the formula.
Each parameter accessible of the interior electric pile that charges of charging station fills electric pile controller and obtains, fills electric pile controller and manages the operation of filling electric pile to information such as output current, output voltage, the charging time of monitoring the electric pile that fills. The charging pile controller interacts with the intelligent operation and maintenance gateway in a wired or wireless mode to acquire related information.
In this embodiment, ideally, the actual output voltage and current of the charging pile should be equal to the actual input voltage and current of the user power battery. However, in practical applications, the difference in the above values may be caused by a problem of connection of wires, a problem of deterioration of wires or electronic devices, or other various factors.
Environmental factors within the charging station affect the operational safety of the charging station, such as humidity, water accumulation, temperature of the area, etc. within the charging station. When water is accumulated in some positions of the charging station, a short circuit of a line can be caused, which seriously threatens the safe operation of the charging station, so that the environmental factors in the charging station need to be monitored, and the third characteristic set M for representing the environmental characteristics of the charging station3As part of a feature vector M characterizing the operating state of the charging station.
In one embodiment of the present disclosure, a third feature set M characterizing the environmental state of the charging station3May include a temperature parameter m collected via a temperature sensor31Humidity parameter m acquired by humidity sensor32Water immersion parameter m collected by water immersion sensor33At least one feature of (1). The temperature parameter m in the charging station environment can thus be determined31Humidity parameter m32Water immersion parameter m33To construct the above-mentioned third feature set M3。
With respect to the temperature parameter m31The temperature in the charging station can be monitored by a temperature sensing device, such as an infrared camera, which is well known to those skilled in the art, and can generate a thermal profile in the charging station and analyze the temperature profile in the predetermined area as needed. In an embodiment of the present disclosure, the temperature of the charging parking space region can be monitored, including the electric vehicle in the parking space, the charging pile and the like.
In one embodiment of the invention, the temperature parameter m31Can be calculated by the following formula:
Figure BDA0002991347200000091
the calculated value is the temperature parameter m31The corresponding vector value. Wherein a is a fixed value, namely a preset time interval; t'kIs the temperature, T 'acquired at time k'k+aThe temperature collected at time k + a.
In one other embodiment of the present disclosure, when the current temperature in the environment is small or large, the temperature change condition may not be sufficient to characterize the current safety risk condition. For example, when the current temperature in the environment is too low, even if the current temperature changes greatly, the safety of the charging station is not affected; when the temperature is too high, the safety risk of the charging station is great even if the current temperature change is small. Thus characterizing the temperature parameter m of the current temperature state in the environment31Can be calculated by the following formula:
Figure BDA0002991347200000101
wherein k is 0, 1, 2, …, T'kIs the temperature at time k; a is a fixed value, namely a preset time interval; t'k+aIs the temperature at time k + a. T is4Is a first threshold value; t is5Is the second threshold.
Acquiring the temperature in the environment in real time, and acquiring the temperature T ' at the moment k when the temperature T ' is detected 'kLess than a first threshold value T4When it is, then let m310. When the temperature T 'at the moment k is detected'kGreater than a second threshold value T5When it is, then let m311. Temperature T 'at time k'kAt a first threshold value T4A second threshold value T5In between, then according to
Figure BDA0002991347200000102
And (6) performing calculation.
In the present embodiment, the temperature T 'at time k'kThe maximum value of the predetermined area in the thermal distribution map or the average value of the temperature of the predetermined area can be selected for calculation.
Humidity parameter m collected by humidity sensor32Humidity couple in charging stationThe safe operation of the charging station is also affected to a certain extent. In one embodiment of the present disclosure, the humidity parameter m32Is the humidity percentage of the current environment detected by the sensor. For example, when the humidity sensor detects that the humidity in the charging station is 50%, the humidity parameter m is32Is 0.5.
Water immersion parameter m collected by water immersion sensor33And the accumulated water in the environment of the charging station can seriously influence the safe operation of the charging station. When the water sensor detects that water is accumulated in a preset area, the m is controlled33When no water is detected, let m equal to 133＝0。
In this embodiment, the above first feature set M is included by setting the feature vector M1Second feature set M2Third feature set M3And the characteristic vector M can represent the safety of the charging station operation more accurately through the characteristic dimensions as few as possible.
Step S2200, identifying whether the charging station is abnormal or not according to the characteristic vector and a preset safe charging model; the safe charging model represents the mapping relation between the characteristic vector and the identification result of whether the characteristic vector belongs to the abnormity.
The safe charging model may be a mapping rule, such as a mapping table, in which different vector values of the feature vector and corresponding recognition results are recorded.
The safe charging model may also be a mapping function f (x), the independent variable of the mapping function f (x) is the feature vector, and the dependent variable f (x) is the recognition result determined by the feature vector.
The safe charging model may be any two-classification model, such as a logistic regression model, and the like, and is not limited herein.
Through the step S2200, the vector value of the feature vector M corresponding to the charging station is input into the safe charging model, so that the corresponding early warning result can be obtained. And judging whether the current charging station belongs to a safe operation state or a dangerous state according to the result output by the safe charging model.
In one embodiment of the present disclosureDividing the early warning result output by the safe charging model into three levels including good v according to the actual running condition of the charging station1V. attract attention2Severe warning v3. Good v1The operation state of the current charging station is characterized to be safe, and attention is attracted to v2The method includes the steps that a certain potential safety hazard exists in a current charging station, but the current charging station does not reach a serious state, and observation is only needed; severe warning v3Then there is a serious potential safety hazard characterizing the charging station, and measures need to be taken to eliminate the current potential safety hazard.
Step S2300, in response to determining the abnormal condition, enabling the set processing mechanism.
In step 2300, if there is a safety hazard in the charging station, the abnormality may be handled according to the type of abnormality. The set exception handling may include at least one of: the first item: sending warning information to a remote monitoring terminal or/and a local monitoring terminal, wherein the warning information comprises abnormal charging pile information; the second term is: and controlling the charging pile to be powered off when abnormality occurs.
Regarding the first item, when it is monitored that the charging station is abnormal, warning information is sent to the remote monitoring terminal and/or the local monitoring terminal in a wired or/and wireless mode so as to warn a manager that potential safety hazards exist in the current charging station. The warning information may include charging pile information with an abnormality, for example, a specific charging pile identifier, and each reference index of the abnormality.
In one embodiment of the present disclosure, the warning may be performed in an acousto-optic manner, or may be highlighted on the remote monitoring terminal or the local monitoring terminal.
In one embodiment of the disclosure, the operation states of the devices in the charging station are all displayed on the remote monitoring terminal or/and the local monitoring terminal, and the early warning result obtained by the intelligent operation and maintenance gateway according to the safe charging model is also sent to the remote monitoring terminal or/and the local monitoring terminal for displaying. For example, the current charging station is displayed on the remote monitoring terminal or/and the local monitoring terminal as operating well v1V. attract attention2Serious policeNotice v3And so on for different operating conditions.
Regarding to the second item, when the danger level of the early warning result is higher and the operation safety of the charging station is threatened, the abnormal power failure of the charging pile can be controlled, or all the charging piles are controlled to be powered off. And after the corresponding risk is eliminated, controlling the charging pile to continue supplying power.
In this embodiment, set processing mechanism is enabled through the abnormity in the charging station, so that the operation risk of the charging station can be effectively reduced, and the loss of an operator is reduced.
According to the steps S2100 to S2300, in the method for early warning the charging station abnormality according to the embodiment, the operation risk of the charging station can be automatically and effectively early warned by extracting the user power battery state, the charging pile state and the feature vector of the charging station environment, which represent the operation risk of the charging station, so that risk control of the charging station is realized, and loss of an operator is reduced.
In one embodiment of the present disclosure, the method for warning the abnormality of the charging station may be monitoring in units of a single charging pile. The single target charging pile, the user power battery corresponding to the target charging pile and the surrounding environment information of the target charging pile are combined to judge whether the target charging pile runs safely or not. When the target charging pile is identified to exist, for example, the grade is serious warning v3When the target charging pile is abnormal, the target charging pile is controlled to be powered off, or the target charging pile and the charging pile adjacent to the target charging pile are controlled to be powered off, so that danger is prevented.
In one embodiment of the present disclosure, the first feature set, the second feature set, and the third feature set as the charging station feature vector may be collected by the corresponding sensor or the controller in real time, or may be collected at predetermined time intervals.
In one embodiment, the method of the present disclosure may further include the step of generating a safe charging model used in step S2200.
The step of generating the safe charging model may include: acquiring training samples, wherein different training samples correspond to different running states of the charging station, and each training sample comprises a vector value of the corresponding sample for the feature vector and an identification result of whether the corresponding sample is abnormal or not; and training to obtain the safe charging model according to the training sample.
In one embodiment, the step of training the obtained anomaly recognition model according to the training samples may include:
in step S3100, a reference index matrix M of the safe charging model is determined.
The safe charging model is an evaluation model established based on the current charging condition of a single charging pile, and covers a unity evaluation system of the regional environments of the user power battery, the charging pile and the charging station, namely a reference index matrix M comprising M1Temperature change rate m of user power battery11Input voltage characteristic m12Input current characteristic m13，M2Output voltage characteristic m of medium charging pile21Output current characteristic m22Current sharing unbalance m23，M3Temperature parameter m of medium charging station area environment31Humidity parameter m32Water immersion parameter m33The total number of the indexes is 9.
The reference index matrix M is established as follows:
Figure BDA0002991347200000131
step S3200, a weight coefficient matrix A related to the reference index system is established according to an analytic hierarchy process.
The corresponding weight of each feature in the reference index system can be established by adopting an analytic hierarchy process. Firstly, establishing pairwise comparison judgment matrixes, comparing the importance of two characteristics according to a preset comparison strategy, and giving corresponding scales, wherein the two characteristics are as follows:
1. represents a reference index miAnd mjFor comparison, the case of equal importance is marked as scale 1.
2. Represents a reference index miAnd mjComparison, miBimjSlightly important, noted as scale 3.
3. Represents a reference index miAnd mjComparison, miBimjClearly important, noted as scale 5.
4. Represents a reference index miAnd mjComparison, miBimjStrongly important, denoted as scale 7.
5. Represents a reference index miAnd mjComparison, miBimjOf extreme importance, noted as scale 9.
The elements in the matrix are judged to have the following properties:
mij＞0；mji＝1/mij；mii＝1。
and obtaining a target factor judgment matrix P of the system to be evaluated according to the judgment:
Figure BDA0002991347200000132
wherein u is1～u9Respectively corresponding to 9 indexes in the reference index matrix M; u. of11～u99The scales are respectively obtained after two indexes are compared.
For example, in one embodiment of the present disclosure, the rate of temperature change m11Input voltage characteristic m12Respectively corresponding to u in the target factor determination matrix P1、u2. When u is1And u1In contrast, with equal importance, u111 is ═ 1; when u is1Biu is a ratio of2In contrast, u1Biu is a ratio of2Obviously important, then u12＝5。
And performing hierarchical sequencing, namely solving the eigenvector corresponding to the maximum eigenvalue of the target factor matrix P, wherein the normalized eigenvector is the weight distribution vector of each factor. And finally, checking whether the weight distribution represented by the feature vector is reasonable or not, namely checking the consistency, wherein the checking formula is as follows:
CR＝CI/RI。
wherein, CI is an index for judging the consistency of the matrix, and the calculation formula is as follows:
Figure BDA0002991347200000141
RI is the average random consistency index of the decision matrix.
And when the consistency check formula result CR is less than 0.10, the judgment matrix is reasonable and can be used for determining weight distribution, otherwise, the judgment matrix needs to be adjusted to meet the consistency check.
By the above method, the weight coefficient matrix a is thus obtained as follows:
Figure BDA0002991347200000142
wherein, a11To a33Are respectively m11To m33The weight coefficient of (2).
Step S3300, determining that the safe charging model is: c ═ a · M, can also be expressed by the following formula:
Figure BDA0002991347200000143
and determining which grade belongs to the evaluation set V according to the value of the safe charging model C, and making corresponding early warning and control operation, wherein the larger the value of C is, the worse the safety of the charging station is, and the evaluation score of the charging station can be the maximum value calculated by charging piles in the station.
According to the embodiment of the disclosure, in the intelligent operation and maintenance gateway, a safe charging model is established by using collected parameters of the charging pile, the user power battery, the charging station environment and the like, the change condition of key indexes in the charging process is quantized, and the charging pile controller sends an exception handling instruction when corresponding preset conditions are met, so that the potential safety hazard in the charging process is reduced, and the safe operation reliability of the charging station is improved.
According to the embodiment of the disclosure, the intelligent operation and maintenance gateway takes charge of the functions of the edge nodes, and completes calculation and response of local data, so that the purpose of regulating and controlling integration of the charging station is achieved.
In an embodiment of this disclosure, electric quantity loss analysis can also be done to the charging station to electric quantity data such as the transaction electric quantity that intelligent operation and maintenance gateway collected total power consumption, the transaction platform that charges or fill electric pile controller and gathered according to the gateway table, include: acquiring the total power consumption collected by a gateway meter; acquiring the transaction electric quantity of a charging transaction platform or a charging pile controller; and calculating the proportion of the difference between the total power consumption and the transaction power consumption in the total power consumption to obtain the loss rate of the charging station, wherein the calculation formula is as follows:
Figure BDA0002991347200000151
the transaction electric quantity refers to electric quantity capable of obtaining business income in the operation process of the charging station, for example, the transaction electric quantity of each charging pile in the charging station is collected. The gateway meter is installed in an access box of the whole charging station, and the power consumption of all loads of the charging station is counted. The loss rate represents the proportion of the amount of un-acquired commercial revenue, and the proportion will directly affect the charging station operating revenue. Therefore, the intelligent operation and maintenance gateway calculates the loss rate of the charging station according to the acquired corresponding data, and sends the loss rate to the remote monitoring terminal or/and the local monitoring terminal for displaying.
In an embodiment of the present invention, the intelligent operation and maintenance gateway may count a ratio graph of monthly charging amount to the power loss in the charging station in a monthly manner.
Referring to fig. 2, it can be seen that there is a direct but non-linear relationship between the power loss and the monthly charge amount by analyzing the variation relationship between the power loss and the monthly charge amount. The typical data is 2020 and 2 months, the monthly charge is the minimum value in the statistical year, and the loss ratio is the maximum value, which indicates that in addition to the variable power loss caused by the charging transaction, a larger proportion of fixed power loss exists in the operation of the charging station, and the fixed power loss ratio is larger, so that the fixed loss is reduced, the corresponding cost expenditure is reduced, and the operation benefit of the charging station is effectively improved.
In this embodiment, a corresponding operation suggestion may be provided according to the analysis result to improve the operation efficiency.
In an embodiment of the disclosure, the intelligent operation and maintenance gateway identifies information such as license plates and vehicle types of charging vehicles in the charging station by using an artificial intelligence algorithm according to a video file sent by the video monitoring terminal, summarizes the information and the information sent by the charging pile controller and analyzes the operation state of the charging station, and displays the result on the local monitoring terminal and the remote monitoring terminal in real time, so that the intelligent level of the operation and maintenance of the system is improved.
< apparatus embodiment >
Based on the same inventive concept, corresponding to the method of any embodiment, the disclosure further provides a charging station abnormity early warning device, and the charging station abnormity early warning device is configured in the intelligent operation and maintenance gateway. As shown in fig. 3, the charging station abnormality warning apparatus 4000 includes a feature extraction module 4100, an abnormality recognition module 4200, and an abnormality processing module 4300.
The feature extraction module 4100 is configured to acquire a feature vector representing an operating state of a charging station, where the feature vector includes a first feature set representing a state of a power battery of a user, a second feature set representing a state of a charging pile, and a third feature set representing an environmental factor of the charging station.
The anomaly identification module 4200 is configured to identify whether the charging station is anomalous according to the feature vector and a preset safe charging model; the safe charging model represents the mapping relation between the characteristic vector and the identification result of whether the characteristic vector belongs to the abnormity.
The exception handling module 4300 is configured to enable a set handling mechanism in response to determining an exception.
In one embodiment, the first set of features characterizing the state of the user's power cell extracted by the feature extraction module 4100 includes at least one of a rate of temperature change, an input voltage characteristic, and an input current characteristic. Wherein, the temperature change rate expresses the temperature change condition of the power battery in a preset time interval; the input voltage characteristic expresses the relation between the actual input voltage in the power battery and the voltage requirement of the power battery; the input current characteristic expresses the relation between the actual input current in the power battery and the current demand of the power battery.
In one embodiment, the second feature set extracted by the feature extraction module 4100 to characterize the charging pile state includes at least one feature of output voltage characteristics, output current characteristics, and current sharing unbalance degree. The output voltage characteristic expresses the relation between the actual output voltage of the charging pile and the voltage requirement of the power battery; the output current characteristic expresses the relation between the actual output current of the charging pile and the current demand of the power battery; the expression of the current sharing unbalance degree represents the fluctuation condition of the output current of the charging pile.
In one embodiment, the third set of features characterizing charging station environmental factors extracted by the feature extraction module 4100 includes at least one feature of a temperature parameter collected by a temperature sensor, a humidity parameter collected by a humidity sensor, and a water immersion parameter collected by a water immersion sensor.
In one embodiment, the charging station abnormality warning apparatus 4000 further includes a model generation module configured to generate the above-mentioned safe charging model, and when generating the safe charging model, configured to: determining a reference index matrix M of the safe charging model; establishing a weight coefficient matrix A related to a reference index system according to an analytic hierarchy process; determining a safe charging model as follows: c ═ a · M.
In one embodiment, the exception handling module 4300, when enabling the set handling mechanism, may perform at least one of: the first item is that warning information is sent to a remote monitoring terminal or/and a local monitoring terminal, wherein the warning information comprises abnormal charging pile information; and the second item is used for controlling the power failure of the charging pile with abnormal occurrence.
In one embodiment, the charging station abnormality warning device 4000 includes a loss calculation module, and the loss calculation module is configured to obtain a total power consumption collected by the gateway meter and a transaction power consumption obtained from the charging pile controller, and calculate a ratio of a difference between the total power consumption and the transaction power consumption to the total power consumption to obtain a loss rate of the charging station.
< apparatus embodiment >
Based on the same inventive concept, corresponding to the method of any embodiment described above, the present disclosure further provides an intelligent operation and maintenance gateway, which may include the charging station abnormality early warning apparatus 4000 according to any embodiment of the present invention, so as to implement the method of charging station abnormality early warning according to any embodiment of the present invention.
Fig. 4 is a schematic diagram illustrating a more specific hardware structure of the intelligent operation and maintenance gateway provided in this embodiment, where the intelligent operation and maintenance gateway may include: a processor 1010, a memory 1020, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, and communication interface 1040 are communicatively coupled to each other within the device via a bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
In an embodiment of the present invention, the communication interface 1040 of the intelligent operation and maintenance gateway may include a network interface, a consolid interface, an RS485 interface, a 4G or 5G communication module, a USB interface, an HDMI interface, an RS232 interface, an LoRa interface, and the like, which are not described in detail herein.
Bus 1050 includes a path that transfers information between the various components of the device, such as processor 1010, memory 1020, and communication interface 1040.
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
In one embodiment of the intelligent operation and maintenance gateway, the intelligent operation and maintenance gateway comprises a processor 1010 and a memory 1020, wherein the memory 1020 is used for storing executable computer instructions; the processor 1010 is configured to operate the intelligent operation and maintenance gateway according to the control of the instruction, so as to execute the charging station abnormality early warning method according to any embodiment of the present invention.
Based on the same inventive concept, corresponding to any of the above-described embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the charging station abnormality warning method according to any of the above-described embodiments.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
The storage medium of the above embodiment stores computer instructions for causing the computer to execute the method for charging station abnormality warning according to any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein again.
Fig. 5 is a block diagram of a hardware configuration of a charging station smart warning system that can be used to implement an embodiment of the present invention.
As shown in fig. 5, the charging station intelligent early warning system includes an intelligent operation and maintenance gateway 10 implementing the charging station abnormality early warning method of the present invention, and a charging pile controller 16, a monitoring terminal, and an environmental sensor, which are in communication with the intelligent operation and maintenance gateway 10.
Fill electric pile controller 16 and be used for the monitoring to fill electric pile and insert the parameter of filling electric pile's power battery. The charging post controller 16 controls the overall operation of the charging post, for example, controlling and monitoring the voltage, current, time, etc. of the charging post. In addition, when the electric vehicle of the user is connected with the charging pile for charging, the power battery parameters of the electric vehicle of the user can also be monitored by the charging pile controller, that is, the BMS (battery management system) of the power battery of the user is communicated with the charging pile controller 16, so that the charging pile controller 16 can send the received corresponding parameters of the power battery BMS of the user to the intelligent operation and maintenance gateway 10 for processing, or/and transmit the received corresponding parameters to the monitoring terminal for displaying and monitoring through the intelligent operation and maintenance gateway 10.
The environment sensor is used for collecting environment information in the charging station, sending the collected environment information to the intelligent operation and maintenance gateway 10 for processing, or/and transmitting the collected environment information to the monitoring terminal through the intelligent operation and maintenance gateway 10 for displaying and monitoring.
In one embodiment of the present invention, the environmental sensor may include at least one of a temperature and humidity monitoring device 13 and a fire monitoring device 14. The temperature and humidity monitoring device 13 may include a temperature sensor and a humidity sensor. The temperature sensor may be, for example, a distributed infrared camera, and the distributed infrared camera periodically transmits temperature parameters such as a thermal distribution diagram of a predetermined area of the charging station to the intelligent operation and maintenance gateway 10. The humidity sensor is used for acquiring humidity parameters in the charging station and sending the humidity parameters to the intelligent operation and maintenance gateway 10 for processing.
The fire monitoring device 14 may include a water immersion sensor. The water logging sensor is used for collecting water logging information in a preset area of the charging station and sending collected data to the intelligent operation and maintenance gateway 10 for processing.
The intelligent operation and maintenance gateway 10 can receive the relevant information from the temperature monitoring device 13 and the fire monitoring device 14 in a wired or wireless communication mode, and can perform early warning on the operation safety of the charging station by using the charging station abnormity early warning method.
The monitoring terminal is in communication with the intelligent operation and maintenance gateway 10 and configured to receive and display data processed by the intelligent operation and maintenance gateway 10. For example, data collected by each sensor or controller is sent to the monitoring terminal through the intelligent operation and maintenance gateway 10 to be directly displayed, for example, the operation parameters, the charging parameters, and the like of each charging pile can be displayed on the monitoring terminal. In addition, the early warning result of the intelligent operation and maintenance gateway 10 may be sent to the monitoring terminal for displaying. For example, early warning information such as good operation, attention calling, serious warning and the like can be displayed on the monitoring terminal.
In one embodiment of the present disclosure, the monitoring terminal may include a local monitoring terminal 11 or/and a remote monitoring terminal 12.
The local monitoring terminal 11 may be configured in a charging station, which may be a mobile phone, a laptop, a tablet, a palmtop, a wearable device, etc. The local monitoring terminal 11 may include a processor, memory, interface devices, communication devices, display devices, input devices, speakers, microphones, and so forth.
The processor may be a mobile version processor. The memory includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device includes, for example, a USB interface, a headphone interface, and the like. The communication means can be, for example, capable of wired or wireless communication, the communication means can include short-range communication means, such as any means for short-range wireless communication based on short-range wireless communication protocols such as the Hilink protocol, WiFi (IEEE 802.11 protocol), Mesh, bluetooth, ZigBee, Thread, Z-Wave, NFC, UWB, LiFi, etc., and the communication means can also include remote communication means, such as any means for WLAN, GPRS, 2G/3G/4G/5G remote communication. The display device is, for example, a liquid crystal display panel, a touch panel, or the like. The input means may comprise, for example, a touch screen, a keyboard, etc. A user can input/output voice information through the speaker and the microphone.
The remote monitoring terminal 12 may be a mobile phone, a portable computer, a tablet computer, a palm computer, a wearable device, etc. The remote monitoring terminal 12 may include a processor, memory, interface devices, communication devices, display devices, input devices, speakers, microphones, and so forth, which will not be described in detail herein.
In this embodiment, the intelligent operation and maintenance gateway 10 receives parameters from each monitoring device, and after data processing, sends the warning information and each currently operating parameter to the local monitoring terminal 11 or/and the remote monitoring terminal 12 for display, so that a manager can monitor the current operating state of the charging station in real time.
In one embodiment of the invention, the intelligent operation and maintenance gateway further comprises a video monitoring terminal 15, an intelligent electric meter terminal 17 and the like which are in communication with the intelligent operation and maintenance gateway 10.
The video monitoring terminal 5 can collect video content in the charging station and upload the video content to the intelligent operation and maintenance gateway 10. For example, the video monitoring terminal 5 may collect information of a license plate, a vehicle type, and the like of the electric vehicle in the charging station, and the intelligent operation and maintenance gateway 10 may recognize the license plate and the vehicle type through a corresponding recognition algorithm in the received related data, and upload the recognized information to the local monitoring terminal 11 or/and the remote monitoring terminal 12, so as to facilitate management and monitoring of the electric vehicle in the charging station.
The intelligent operation and maintenance gateway 10 may obtain data such as power consumption from the intelligent electric meter terminal 17 to complete the calculation of the loss rate, or parameters such as current power consumption, etc., which are not described in detail herein.
The charging station intelligent early warning system of this embodiment has realized including unified access and the control of hardware such as environmental monitoring, fire control, video monitoring, the electric pile of filling, smart electric meter in the charging station to make corresponding rating and early warning operation according to data acquisition analysis charging station running state, improved the security of charging station operation. In addition, the function of the gateway is improved, so that the gateway has the capacity of local data processing besides the communication function, and the technical accumulation is provided for further building the power internet of things.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the idea of the present disclosure, also technical features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of brevity.
In addition, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown in the provided figures for simplicity of illustration and discussion, and so as not to obscure the embodiments of the disclosure. Furthermore, devices may be shown in block diagram form in order to avoid obscuring embodiments of the present disclosure, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the embodiments of the present disclosure are to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the disclosure, it should be apparent to one skilled in the art that the embodiments of the disclosure can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
The disclosed embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalents, improvements, and the like that may be made within the spirit and principles of the embodiments of the disclosure are intended to be included within the scope of the disclosure.

Claims (10)

1. A charging station abnormity early warning method is implemented by an intelligent operation and maintenance gateway and comprises the following steps:
the method comprises the steps that a characteristic vector used for representing the running state of a charging station is obtained, and the characteristic vector comprises a first characteristic set representing the state of a power battery of a user, a second characteristic set representing the state of a charging pile and a third characteristic set representing environmental factors of the charging station;
identifying whether the charging station is abnormal or not according to the characteristic vector and a preset safe charging model; the safety charging model represents the mapping relation between the characteristic vector and the identification result of whether the characteristic vector belongs to an abnormity or not;
in response to determining that the charging station is abnormal, a set processing mechanism is enabled.
2. The method of claim 1, wherein the first set of features includes at least one of the following features characterizing a user's power battery status:
the temperature change rate represents the temperature change condition of the power battery within a preset time interval;
input voltage characteristics: representing the relation between the actual input voltage in the power battery and the voltage requirement of the power battery;
input current characteristics: and representing the relation between the actual input current in the power battery and the current demand of the power battery.
3. The method of claim 2, wherein the rate of temperature change is calculated by the formula:
Figure FDA0002991347190000011
m11as rate of change of temperature, TkA is a fixed value, i.e. preset, for the temperature at time kTime interval, T1Is a first temperature threshold, T2Is the second temperature threshold.
4. The method of claim 1, wherein the second set of features includes at least one of the following features characterizing charging post status:
output voltage characteristics: representing the relation between the actual output voltage of the charging pile and the voltage requirement of the power battery;
output current characteristics: representing the relation between the actual output current of the charging pile and the current demand of the power battery;
current sharing unbalance degree: the fluctuation condition of the output current of the charging pile is represented.
5. The method of claim 1, wherein the third set of features comprises at least one of temperature parameters collected via a temperature sensor, humidity parameters collected by a humidity sensor, and water immersion parameters collected by a water immersion sensor within the charging station.
6. The method of claim 1, wherein the method further comprises:
acquiring the total power consumption collected by a gateway meter;
acquiring the transaction electric quantity of a charging pile controller;
and calculating the proportion of the difference between the total electricity consumption and the transaction electricity consumption in the total electricity consumption to obtain the loss rate of the charging station.
7. The method of claim 1, wherein generating the safe charging model comprises:
determining a reference index matrix M of the safe charging model;
establishing a weight coefficient matrix A related to a reference index system according to an analytic hierarchy process;
determining a safe charging model as follows: c ═ a · M.
8. The method of claim 1, wherein the set handling mechanism comprises at least one of:
sending warning information to a remote monitoring terminal or/and a local monitoring terminal, wherein the warning information comprises abnormal charging pile information;
and controlling the charging pile to be powered off when abnormality occurs.
9. An intelligent operation and maintenance gateway, comprising:
the charging station comprises a characteristic extraction module, a charging station charging module and a charging station charging module, wherein the characteristic extraction module is used for acquiring a characteristic vector for representing the running state of the charging station, and the characteristic vector comprises a first characteristic set for representing the state of a power battery of a user, a second characteristic set for representing the state of a charging pile and a third characteristic set for representing environmental factors of the charging station;
the abnormity identification module is used for identifying whether the charging station is abnormal or not according to the characteristic vector and a preset safe charging model; the safety charging model represents the mapping relation between the characteristic vector and the identification result of whether the characteristic vector belongs to an abnormity or not; and the number of the first and second groups,
and the abnormity processing module enables a set processing mechanism in response to determining that the charging station is abnormal.
10. A charging station intelligent warning system, comprising:
the environment sensor is used for monitoring environmental parameters in the charging station;
the charging pile controller is used for monitoring parameters of a charging pile and a power battery connected to the charging pile;
the intelligent operation and maintenance gateway of claim 9, which is in communication with the environmental sensor and the charging pile controller to receive environmental parameters collected by the environmental sensor and parameters of the charging pile and the power battery collected by the charging pile controller;
and the monitoring terminal is communicated with the intelligent operation and maintenance gateway and is configured to receive and display the data processed by the intelligent operation and maintenance gateway.
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Application publication date: 20210727