CN115496372B - Charging system safety risk prediction method, device, equipment and readable storage medium - Google Patents

Charging system safety risk prediction method, device, equipment and readable storage medium Download PDF

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CN115496372B
CN115496372B CN202211176007.XA CN202211176007A CN115496372B CN 115496372 B CN115496372 B CN 115496372B CN 202211176007 A CN202211176007 A CN 202211176007A CN 115496372 B CN115496372 B CN 115496372B
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charging system
risk
fault
fault alarm
generating
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CN115496372A (en
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黄禄满
张国立
蔡洪惜
张敏
陈云
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Guangdong Shunfeng Intelligent Energy Research Institute Co ltd
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Guangdong Shunfeng Intelligent Energy Research Institute Co ltd
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063Operations research, analysis or management
    • G06Q10/0635Risk analysis of enterprise or organisation activities
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/20Administration of product repair or maintenance
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06Electricity, gas or water supply
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00Road transport of goods or passengers
    • Y02T10/60Other road transportation technologies with climate change mitigation effect
    • Y02T10/70Energy storage systems for electromobility, e.g. batteries
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00Road transport of goods or passengers
    • Y02T10/60Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10Technologies relating to charging of electric vehicles
    • Y02T90/12Electric charging stations

Abstract

The invention discloses a charging system security risk prediction method, a device, equipment and a readable storage medium, wherein the charging system security risk prediction method comprises the following steps: acquiring fault alarm information in historical data of related equipment of a charging system; generating a risk feature number of the charging system according to the fault alarm information; predicting the safety risk of the charging system based on the risk feature number to generate a prediction result; and sending the prediction result to a charging system management platform to output prompt information. The operation and maintenance resource allocation is more reasonable, the utilization rate and the operation and maintenance efficiency of the existing operation and maintenance resources are improved, and the normal operation of the charging pile and the foundation matched with the electric pile is ensured.

Description

Charging system safety risk prediction method, device, equipment and readable storage medium
Technical Field
The invention relates to the field of electric vehicle charging piles, in particular to a charging system safety risk prediction method, a charging system safety risk prediction device, charging system safety risk prediction equipment and a readable storage medium.
Background
With the rapid development of new energy industry in China, the energy revolution continues to go deep, the number of new energy automobiles is continuously increased, and the requirement on the number of automobile charging piles is also continuously increased. However, because the charging piles have the characteristics of scattered position distribution, wide coverage, frequent faults and the like, the difficulty of maintenance of the charging piles and the supporting infrastructure of the charging piles is increased, the charging piles are not friendly to operation and maintenance personnel, and the operation cost of an operation enterprise of the charging piles is increased if the number of the operation and maintenance personnel is increased correspondingly. And for new energy automobile users, the high-efficiency, stable and safe charging infrastructure can greatly improve the experience of using the new energy automobile, thereby indirectly popularizing the use of the new energy automobile. Therefore, there is a need to improve the operation and maintenance efficiency of operation and maintenance personnel to ensure that the charging pile and the charging pile supporting infrastructure are operating properly.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a charging system safety risk prediction method, which aims to solve the problems that the existing charging pile and the charging pile matched infrastructure have large operation and maintenance difficulty and are not friendly to operation and maintenance personnel.
In order to achieve the above object, the present invention provides a charging system security risk prediction method, which includes the following steps:
acquiring fault alarm information in historical data of related equipment of a charging system;
generating a risk feature number of the charging system according to the fault alarm information;
predicting the safety risk of the charging system based on the risk feature number to generate a prediction result;
and sending the prediction result to a charging system management platform to output prompt information.
Further, the historical data is a fault alarm of the charging system, and before the step of obtaining the fault alarm information in the historical data of the related equipment of the charging system, the method includes:
monitoring each working parameter generated by the related equipment when the charging system is in an operating state;
comparing each working parameter with a plurality of preset working parameter thresholds;
and when the working parameter is larger than the preset working parameter threshold, generating the fault alarm of the corresponding grade.
Further, the fault alarm information includes a fault alarm frequency, the risk feature number includes a first risk feature number, and the step of generating the risk feature number of the charging system according to the fault alarm information includes:
generating fault alarm frequency in a preset time period according to the fault alarm times, and taking the fault alarm frequency as a first risk feature number of the charging system.
Further, the fault alarm times include a first-level fault alarm times, a second-level fault alarm times and a third-level fault alarm times, and the step of generating a fault alarm frequency in a preset time period according to the fault alarm times, and taking the fault alarm frequency as a first risk feature number of the charging system includes:
generating primary failure alarm frequency in a preset time period according to the primary failure alarm times, generating secondary failure alarm frequency in the preset time period according to the secondary failure alarm times, and generating tertiary failure alarm frequency in the preset time period according to the tertiary failure alarm times;
and taking the sum of the primary fault alarm frequency, the secondary fault alarm frequency and the tertiary fault alarm frequency multiplied by different weight values as the first risk characteristic number, wherein the weight values are increased along with the increase of the fault level.
Further, the risk feature number further includes a second risk feature number, and the step of generating the risk feature number of the charging system according to the fault alarm information includes:
generating a fault grade conversion rate of the charging system according to the first-level fault alarming times, the second-level fault alarming times and the third-level fault alarming times, and taking the fault grade conversion rate as a second risk characteristic number of the charging system.
Further, the step of predicting the safety risk of the charging system based on the risk feature number to generate a prediction result includes:
inputting the first risk feature number and the second risk feature number into a preset charging system safety risk prediction model, generating a first probability that the charging system has safety risk, and generating a second probability that the charging system does not have safety risk;
and comparing the first probability with the second probability, and generating a prediction result of the charging system with safety risk when the first probability is larger than the second probability.
Further, the step of sending the prediction result to a charging system management platform includes:
the method comprises the steps of sending a prediction result of the existence of safety risk and a risk factor report corresponding to the safety risk to a charging system management platform or a mobile phone of an operation and maintenance person;
wherein the risk factor report is generated based on the operating parameter being greater than the preset operating parameter threshold.
In addition, in order to achieve the above object, the present invention also provides a charging system security risk prediction apparatus, including:
the acquisition module is used for acquiring fault alarm information in historical data of related equipment of the charging system;
the generation module is used for generating risk feature numbers of the charging system according to the fault alarm information;
the prediction module is used for predicting the safety risk of the charging system based on the risk feature number to generate a prediction result;
and the sending module is used for sending the prediction result to a charging system management platform so as to output prompt information.
In addition, to achieve the above object, the present invention also provides a charging system security risk prediction apparatus including: the charging system security risk prediction method comprises a memory, a processor and a charging system security risk prediction program which is stored in the memory and can run on the processor, wherein the charging system security risk prediction program realizes the steps of the charging system security risk prediction method when being executed by the processor.
In addition, in order to achieve the above object, the present invention further provides a readable storage medium, on which a charging system security risk prediction program is stored, which when executed by a processor, implements the steps of the charging system security risk prediction method as described above.
According to the charging system safety risk prediction method provided by the embodiment of the invention, fault alarm information in historical data of related equipment of a charging system is obtained; generating a risk feature number of the charging system according to the fault alarm information; predicting the safety risk of the charging system based on the risk feature number to generate a prediction result; and sending the prediction result to a charging system management platform to output prompt information. The method comprises the steps of generating corresponding risk feature numbers based on fault information of the charging system, predicting whether the charging system has safety risk based on the risk feature numbers, and sending a prediction result to a charging system management platform to play a role in prompting. Operation and maintenance personnel can work in the charging idle period An Paiyun in advance based on a prediction result, the charging system with safety risk is maintained preferentially, so that operation and maintenance resource allocation is more reasonable, the utilization rate and operation and maintenance efficiency of the existing operation and maintenance resources are improved, safe and stable normal operation of a charging pile and a foundation matched with the electric pile is ensured, the high-efficiency utilization of a station yard in the peak charging period is ensured, the utilization rate of the site pile is improved, the economic benefit of the station yard is improved, the user experience is improved, and the fault condition discovered by a user is reduced.
Drawings
FIG. 1 is a schematic diagram of a device architecture of a hardware operating environment according to an embodiment of the present invention;
fig. 2 is a flowchart of a first embodiment of a charging system security risk prediction method according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The main solutions of the embodiments of the present invention are: acquiring fault alarm information in historical data of related equipment of a charging system; generating a risk feature number of the charging system according to the fault alarm information; predicting the safety risk of the charging system based on the risk feature number to generate a prediction result; and sending the prediction result to a charging system management platform to output prompt information.
The existing charging pile and the infrastructure matched with the charging pile have the technical problems of high operation and maintenance difficulty and unfriendly operation and maintenance personnel.
The invention provides a solution, so that operation and maintenance personnel can select the priority of equipment for maintenance according to the evaluated risk level, the allocation of operation and maintenance resources is more reasonable, the utilization rate and operation and maintenance efficiency of the existing operation and maintenance resources are improved, and the normal operation of the charging pile and the foundation matched with the electric pile is ensured.
Referring to fig. 1, fig. 1 is a schematic device structure of a hardware running environment according to an embodiment of the present invention.
The device of the embodiment of the invention can be a charging pile, and also can be mobile terminal devices with data receiving, data processing and data sending functions such as a smart phone, a PC, a tablet personal computer, a cloud server, a portable computer and the like.
As shown in fig. 1, the apparatus may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Optionally, the device may also include a camera, RF (Radio Frequency) circuitry, sensors, audio circuitry, wiFi modules, and the like. Among other sensors, such as light sensors, motion sensors, and other sensors. In particular, the light sensor may include an ambient light sensor that may adjust the brightness of the display screen according to the brightness of ambient light, and a proximity sensor that may turn off the display screen and/or backlight when the mobile device is moved to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and the direction when the mobile terminal is stationary, and the mobile terminal can be used for recognizing the gesture of the mobile terminal (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; of course, the mobile device may also be configured with other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, and the like, which are not described herein.
It will be appreciated by those skilled in the art that the device structure shown in fig. 1 is not limiting of the device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a charging system security risk prediction method program may be included in a memory 1005 as one type of computer storage medium.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to invoke the charging system security risk prediction program stored in the memory 1005, and perform the following operations:
acquiring fault alarm information in historical data of related equipment of a charging system;
generating a risk feature number of the charging system according to the fault alarm information;
predicting the safety risk of the charging system based on the risk feature number to generate a prediction result;
and sending the prediction result to a charging system management platform to output prompt information.
Further, the processor 1001 may call the charging system security risk prediction program stored in the memory 1005, and further perform the following operations:
the historical data is fault alarm of the charging system, and before the step of acquiring fault alarm information in the historical data of related equipment of the charging system, the method comprises the following steps:
monitoring each working parameter generated by the related equipment when the charging system is in an operating state;
comparing each working parameter with a plurality of preset working parameter thresholds;
and when the working parameter is larger than the preset working parameter threshold, generating the fault alarm of the corresponding grade.
Further, the processor 1001 may call the charging system security risk prediction program stored in the memory 1005, and further perform the following operations:
the fault alarm information comprises fault alarm times, the risk feature number comprises a first risk feature number, and the step of generating the risk feature number of the charging system according to the fault alarm information comprises the following steps:
generating fault alarm frequency in a preset time period according to the fault alarm times, and taking the fault alarm frequency as a first risk feature number of the charging system.
Further, the processor 1001 may call the charging system security risk prediction program stored in the memory 1005, and further perform the following operations:
the fault alarming times comprise primary fault alarming times, secondary fault alarming times and tertiary fault alarming times, the fault alarming frequency in a preset time period is generated according to the fault alarming times, and the step of taking the fault alarming frequency as a first risk characteristic number of the charging system comprises the following steps:
generating primary failure alarm frequency in a preset time period according to the primary failure alarm times, generating secondary failure alarm frequency in the preset time period according to the secondary failure alarm times, and generating tertiary failure alarm frequency in the preset time period according to the tertiary failure alarm times;
and taking the sum of the primary fault alarm frequency, the secondary fault alarm frequency and the tertiary fault alarm frequency multiplied by different weight values as the first risk characteristic number, wherein the weight values are increased along with the increase of the fault level.
Further, the processor 1001 may call the charging system security risk prediction program stored in the memory 1005, and further perform the following operations:
the risk feature number further comprises a second risk feature number, and the step of generating the risk feature number of the charging system according to the fault alarm information comprises the following steps:
generating a fault grade conversion rate of the charging system according to the first-level fault alarming times, the second-level fault alarming times and the third-level fault alarming times, and taking the fault grade conversion rate as a second risk characteristic number of the charging system.
Further, the processor 1001 may call the charging system security risk prediction program stored in the memory 1005, and further perform the following operations:
the step of predicting the safety risk of the charging system based on the risk feature number to generate a prediction result comprises the following steps:
inputting the first risk feature number and the second risk feature number into a preset charging system safety risk prediction model, generating a first probability that the charging system has safety risk, and generating a second probability that the charging system does not have safety risk;
and comparing the first probability with the second probability, and generating a prediction result of the charging system with safety risk when the first probability is larger than the second probability.
Further, the processor 1001 may call the charging system security risk prediction program stored in the memory 1005, and further perform the following operations:
the step of sending the prediction result to a charging system management platform comprises the following steps:
the method comprises the steps of sending a prediction result of the existence of safety risk and a risk factor report corresponding to the safety risk to a charging system management platform or a mobile phone of an operation and maintenance person;
wherein the risk factor report is generated based on the operating parameter being greater than the preset operating parameter threshold.
Referring to fig. 2, a first embodiment of a charging system security risk prediction method according to the present invention includes:
step S10, fault alarm information in historical data of related equipment of a charging system is obtained;
in this embodiment, the charging system refers to a system for providing charging service for an electric automobile, and related devices of the charging system include a charging pile and a transformer connected to the charging pile. The working parameters of the related equipment during operation are obtained, the working states are normally divided into normal and abnormal states, the operation data of the two states are recorded as historical data by the equipment, and the abnormal working state generates fault alarm and also exists in the historical data. The fault alarm information about the vehicle refers to a fault alarm generated by the BATTERY of the vehicle during charging, and may be acquired through a BATTERY BMS system (BATTERY MANAGEMENT SYSTEM) of the BATTERY of the vehicle, and the fault alarm information of the transformer refers to a fault alarm generated by the low voltage side of the transformer.
Further, the historical data is a fault alarm of the charging system, and before the step of obtaining the fault alarm information in the historical data of the related equipment of the charging system, the method includes: monitoring each working parameter generated by the related equipment when the charging system is in an operating state; comparing each working parameter with a plurality of preset working parameter thresholds; and when the working parameter is larger than the preset working parameter threshold, generating the fault alarm of the corresponding grade.
Specifically, the related devices are all configured with a safety monitoring system, and the safety monitoring system is used for detecting daily working parameters of the monitoring device, wherein the working parameters can include: parameters such as operating current, operating voltage, operating power, operating temperature, and insulation value may also include, for a vehicle battery, the voltage condition of an individual cell in the battery pack, which refers to whether the voltage of each cell is consistent with the overall voltage of the battery pack. And comparing the working current, the working voltage, the working power, the working temperature and the insulation value with corresponding preset working parameter thresholds respectively, and giving out fault alarm when the working current, the working voltage, the working power, the working temperature and the insulation value are larger than the preset working parameter thresholds, wherein the specific preset working parameter thresholds can be set according to the hardware attribute of the equipment or set by the manufacturer of the equipment. The preset working parameter threshold value can be set into a plurality of grades, and when the actual working parameter exceeds the preset working parameter threshold value of different grades, fault alarms of different grades are sent out. In addition, in the process of comparing the working parameter with the preset working parameter threshold, the working parameter may be the change rate of each parameter, for example, the corresponding preset working parameter threshold is the parameter threshold of the parameter change rate, for example, the rising rate of the battery temperature during charging, and when the rising rate of the battery temperature is too fast to be greater than the preset rising rate threshold, a fault alarm is also sent.
Step S20, generating risk feature numbers of the charging system according to the fault alarm information;
specifically, in this embodiment, the historical data is a fault alarm of the charging system. I.e. the history data records the fault alarm of the charging system in the past preset time period.
Further, step S20 further includes step S21, step S22, and step S23.
And S21, generating a fault alarm frequency in a preset time period according to the fault alarm times, and taking the fault alarm frequency as a first risk characteristic number of the charging system.
Specifically, the fault alarm information includes the number of fault alarms, and the risk feature number includes a first risk feature number. In the actual operation and maintenance process, the preset time period can be set for one week or one month, and is set as the current time to the last maintenance time under the normal condition. If the number of times of alarming occurring in the charging system after the last maintenance is 18 times, the corresponding failure alarming frequency is 18 times, and the 18 times are the first risk feature number of the charging system.
Step S22, generating primary failure alarm frequency in a preset time period according to the primary failure alarm times, generating secondary failure alarm frequency in the preset time period according to the secondary failure alarm times, and generating tertiary failure alarm frequency in the preset time period according to the tertiary failure alarm times; and taking the sum of the primary fault alarm frequency, the secondary fault alarm frequency and the tertiary fault alarm frequency multiplied by different weight values as the first risk characteristic number, wherein the weight values are increased along with the increase of the fault level.
Specifically, the failure alarm times comprise primary failure alarm times, secondary failure alarm times and tertiary failure alarm times. In this embodiment, three levels of working parameter thresholds, namely, a three-level parameter threshold, a two-level parameter threshold and a one-level parameter threshold are set, wherein the three-level parameter threshold is greater than the two-level parameter threshold, the two-level parameter threshold is greater than the one-level parameter threshold, and when the working parameter is greater than the parameter threshold of the corresponding level, a fault alarm of the corresponding level is generated, wherein the fault severity increases with the increase of the parameter threshold, namely, the greater the fault level, the greater the fault severity. And respectively generating a primary fault alarm frequency, a secondary fault alarm frequency and a tertiary fault alarm frequency in a preset time period. Further, under the condition of facing multiple levels of fault alarms, the first risk feature number is calculated by the following steps: first risk feature number=primary fault alarm frequency×a+secondary fault alarm frequency×b+tertiary fault alarm frequency×c, wherein A, B and C are weight values corresponding to primary fault alarm, secondary fault alarm and tertiary fault alarm, respectively, and weight values a are smaller than B and B is smaller than C. Thus representing the severity of different fault alarm levels.
And S23, generating a fault grade conversion rate of the charging system according to the primary fault alarm times, the secondary fault alarm times and the tertiary fault alarm times, and taking the fault grade conversion rate as a second risk characteristic number of the charging system.
Specifically, the risk feature number further includes a second risk feature number. The second risk feature number is derived based on the conversion rate of the fault alert level. It will be appreciated that in this embodiment, when the relevant device issues a fault alarm, the corresponding device will perform a corresponding protection action. Taking a charging process as an example for explanation, the protection action is used for reducing charging power or charging current during primary fault alarming, the protection action is used for stopping charging during secondary fault alarming, the protection action is used for cutting off a charging pile power supply and starting a fire-fighting action during tertiary fault alarming, and the actions are used for protecting equipment safety and avoiding further expansion of faults. And the conversion rate of the failure alarm level includes: the primary failure alarm to secondary failure alarm rate (the ratio of the number of secondary failure alarms to the number of primary failure alarms, wherein the number of secondary failure alarms is the number of secondary failure alarms when the primary failure is converted to the secondary failure), the secondary failure alarm to tertiary failure alarm (the ratio of the number of tertiary failure alarms to the number of secondary failure alarms, wherein the number of tertiary failure alarms is the number of tertiary alarms when the secondary failure is converted to the tertiary failure), the normal to secondary failure alarm rate (the ratio of the number of secondary failure alarms to all secondary failure alarms is directly sent out), and the normal to tertiary failure alarm rate (the ratio of the number of tertiary failure alarms to all tertiary failure alarms is directly sent out). The failure conversion rate can reflect whether the actual protection action is in place or not, or whether the protection action has a protection effect or not. The fault conversion rates are multiplied by the corresponding coefficients respectively and then added to obtain the second risk feature number, and the specific process is similar to the first risk feature number generation process, and will not be repeated here.
Step S30, predicting the safety risk of the charging system based on the risk feature number to generate a prediction result;
further, the first risk feature number and the second risk feature number are input into a preset charging system safety risk prediction model, a first probability that the charging system has safety risk is generated, and a second probability that the charging system does not have safety risk is generated; and comparing the first probability with the second probability, and generating a prediction result of the charging system with safety risk when the first probability is larger than the second probability.
Specifically, the charging system safety risk prediction model is trained by charging system training samples, wherein each training sample is marked as having safety risk/not having safety risk, and each training sample has a corresponding first risk feature number and second risk feature number, and each sample can be obtained based on the charging pile system historical data mark, if a charging system has a safety accident, the first risk feature number and the second risk feature number are generated based on the historical data of the charging system before the safety accident occurs, and the first risk feature number and the second risk feature number are marked as having safety risk, so that a training sample having safety risk is obtained. Further, taking the first risk feature number as an example, if the first risk feature number generated at this time is a, obtaining the number of samples with the first risk feature number being a (or the number of samples between intervals [ a-a, a+a ] in training samples with the safety risk type, where a may be set by a technician according to the actual situation) in the number of samples with the first risk feature number being a (or between intervals [ a-a, a+a ]), and if the total number of training samples with the safety risk type is 100, the number of samples with the first risk feature number being a (or between intervals [ a-a, a+a ]) is 10, and obtaining the frequency of the first risk feature number being a in the training samples with the safety risk type as 10/100. Correspondingly, calculating the frequency of the second risk feature number in the training sample with the safety risk type, and multiplying the frequency of the first risk feature number and the frequency of the second risk feature number in the training sample with the safety risk type by the sum of the corresponding weight coefficients to obtain a first probability value. The calculation formula that can be referred to the first probability value is as follows:
wherein n is 1 For the number of samples which are the same as or similar to the first risk feature number generated at the time in the training samples with the safety risk type, n 2 The number of samples which are the same as or similar to the second risk feature number generated at the time in the training samples with the safety risk typeZ is the total amount of training samples of the type where there is a security risk, c 1 A weight coefficient for the first risk feature number, c 2 And the weight coefficient is the second risk feature number. The weight of each electrical state characteristic quantity can be set by the skilled person.
Similarly, the second probability value is calculated in a similar manner to the first probability value, and the training sample with the safety risk type is replaced by the training sample without the safety risk type. The second probability value is calculated as follows:
wherein m is 1 M is the number of samples which are the same as or similar to the first risk feature number generated at the moment in the training samples without the safety risk type 2 The number of samples which are the same as or similar to the second risk feature number generated at the time in the training samples without the safety risk type, y is the total number of the training samples without the safety risk type, c 1 A weight coefficient for the first risk feature number, c 2 And the weight coefficient is the second risk feature number.
And comparing the obtained first probability with the second probability, and predicting that the charging system will have a safety accident when the first probability is larger than the second probability, wherein the corresponding prediction result is that the safety risk exists.
And step S40, the prediction result is sent to a charging system management platform so as to output prompt information.
Further, a prediction result of the safety risk and a risk factor report corresponding to the safety risk are sent to a charging system management platform or a mobile phone of an operation and maintenance person; wherein the risk factor report is generated based on the operating parameter being greater than the preset operating parameter threshold.
Specifically, after predicting that the charging system has a safety risk, a risk factor report generating the safety risk is further generated. The risk factor report includes the operating parameters of the fault alarms for the different devices, as well as the frequency of the fault alarms for each parameter and the level of the fault alarms. Such as charge over-temperature alarm of the charge pile, thermal runaway alarm (temperature increase is too fast) of the charge pile, overcurrent alarm of the charge pile and the like. The prediction result and the risk factor report are sent to a charging system management platform or a mobile phone of an operation and maintenance personnel, the operation and maintenance personnel can download the information through a mobile client, and the charging system with risks can be maintained, and the risk factor report can be used as a reference for maintenance.
In this embodiment, failure alarm information in history data of related devices of the charging system is obtained; generating a risk feature number of the charging system according to the fault alarm information; predicting the safety risk of the charging system based on the risk feature number to generate a prediction result; and sending the prediction result to a charging system management platform to output prompt information. The method comprises the steps of generating corresponding risk feature numbers based on fault information of the charging system, predicting whether the charging system has safety risk based on the risk feature numbers, and sending a prediction result to a charging system management platform to play a role in prompting. Operation and maintenance personnel can work in the charging idle period An Paiyun in advance based on a prediction result, the charging system with safety risk is maintained preferentially, so that operation and maintenance resource allocation is more reasonable, the utilization rate and operation and maintenance efficiency of the existing operation and maintenance resources are improved, safe and stable normal operation of a charging pile and a foundation matched with the electric pile is ensured, the high-efficiency utilization of a station yard in the peak charging period is ensured, the utilization rate of the site pile is improved, the economic benefit of the station yard is improved, the user experience is improved, and the fault condition discovered by a user is reduced.
In addition, the embodiment also provides a charging system security risk prediction device, which includes:
the acquisition module is used for acquiring fault alarm information in historical data of related equipment of the charging system;
the generation module is used for generating risk feature numbers of the charging system according to the fault alarm information;
the prediction module is used for predicting the safety risk of the charging system based on the risk feature number to generate a prediction result;
and the sending module is used for sending the prediction result to a charging system management platform so as to output prompt information.
Optionally, the acquiring module is further configured to:
monitoring each working parameter generated by the related equipment when the charging system is in an operating state;
comparing each working parameter with a plurality of preset working parameter thresholds;
and when the working parameter is larger than the preset working parameter threshold, generating the fault alarm of the corresponding grade.
Optionally, the fault alarm information includes a number of fault alarms, the risk feature number includes a first risk feature number, and the generating module is further configured to:
generating fault alarm frequency in a preset time period according to the fault alarm times, and taking the fault alarm frequency as a first risk feature number of the charging system.
Optionally, the number of fault alarms includes a number of primary fault alarms, a number of secondary fault alarms, and a number of tertiary fault alarms, and the generating module is further configured to:
generating primary failure alarm frequency in a preset time period according to the primary failure alarm times, generating secondary failure alarm frequency in the preset time period according to the secondary failure alarm times, and generating tertiary failure alarm frequency in the preset time period according to the tertiary failure alarm times;
and taking the sum of the primary fault alarm frequency, the secondary fault alarm frequency and the tertiary fault alarm frequency multiplied by different weight values as the first risk characteristic number, wherein the weight values are increased along with the increase of the fault level.
Optionally, the risk feature number further includes a second risk feature number, and the generating module is further configured to:
generating a fault grade conversion rate of the charging system according to the first-level fault alarming times, the second-level fault alarming times and the third-level fault alarming times, and taking the fault grade conversion rate as a second risk characteristic number of the charging system.
Optionally, the prediction module is further configured to:
inputting the first risk feature number and the second risk feature number into a preset charging system safety risk prediction model, generating a first probability that the charging system has safety risk, and generating a second probability that the charging system does not have safety risk;
and comparing the first probability with the second probability, and generating a prediction result of the charging system with safety risk when the first probability is larger than the second probability.
Optionally, the sending module is further configured to:
the method comprises the steps of sending a prediction result of the existence of safety risk and a risk factor report corresponding to the safety risk to a charging system management platform or a mobile phone of an operation and maintenance person;
wherein the risk factor report is generated based on the operating parameter being greater than the preset operating parameter threshold.
The charging system safety risk prediction device provided by the invention adopts the charging system safety risk prediction method in the embodiment, and solves the technical problem that the screen throwing function is difficult to use for part of special people or old users. Compared with the prior art, the beneficial effects of the charging system security risk prediction device provided by the embodiment of the invention are the same as those of the charging system security risk prediction method provided by the embodiment, and other technical features of the charging system security risk prediction device are the same as those disclosed by the method of the embodiment, so that redundant description is omitted herein.
In addition, the embodiment also provides a charging system security risk prediction device, including: the charging system safety risk prediction method comprises a memory, a processor and a charging system safety risk prediction program which is stored in the memory and can run on the processor, wherein the charging system safety risk prediction program realizes the steps of the charging system safety risk prediction method when being executed by the processor.
The specific implementation manner of the charging system security risk prediction device of the present invention is substantially the same as the embodiments of the charging system security risk prediction method described above, and will not be described herein.
In addition, the embodiment also provides a readable storage medium, where a charging system security risk prediction program is stored, where the charging system security risk prediction program, when executed by a processor, implements the steps of the charging system security risk prediction method described above.
The specific implementation manner of the medium of the present invention is basically the same as the above embodiments of the new method for predicting the safety risk of the charging system, and will not be described herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above, including several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, a charging pile, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.

Claims (5)

1. The charging system safety risk prediction method is characterized by comprising the following steps of:
acquiring fault alarm information in historical data of related equipment of a charging system, wherein the related equipment comprises a charging pile and a transformer connected with the charging pile;
generating a risk feature number of the charging system according to the fault alarm information;
predicting the safety risk of the charging system based on the risk feature number to generate a prediction result;
the prediction result is sent to a charging system management platform to output prompt information;
the historical data is fault alarm of the charging system, and before the step of acquiring fault alarm information in the historical data of related equipment of the charging system, the method comprises the following steps:
monitoring each working parameter generated by the related equipment when the charging system is in an operating state;
comparing each working parameter with a plurality of preset working parameter thresholds;
when the working parameter is larger than the preset working parameter threshold, generating the fault alarm of a corresponding grade;
the fault alarm information comprises fault alarm times, the fault alarm times comprise primary fault alarm times, secondary fault alarm times and tertiary fault alarm times, the risk feature numbers comprise first risk feature numbers, and the step of generating the risk feature numbers of the charging system according to the fault alarm information comprises the following steps:
generating primary failure alarm frequency in a preset time period according to the primary failure alarm times, generating secondary failure alarm frequency in the preset time period according to the secondary failure alarm times, and generating tertiary failure alarm frequency in the preset time period according to the tertiary failure alarm times;
taking the sum of the primary fault alarming frequency, the secondary fault alarming frequency and the tertiary fault alarming frequency multiplied by different weight values as the first risk characteristic number, wherein the weight values are increased along with the increase of fault grades;
the risk feature number further comprises a second risk feature number, and the step of generating the risk feature number of the charging system according to the fault alarm information comprises the following steps:
generating a fault grade conversion rate of the charging system according to the primary fault alarming times, the secondary fault alarming times and the tertiary fault alarming times, and taking the fault grade conversion rate as a second risk characteristic number of the charging system;
the step of predicting the safety risk of the charging system based on the risk feature number to generate a prediction result comprises the following steps:
inputting the first risk feature number and the second risk feature number into a preset charging system safety risk prediction model, generating a first probability that the charging system has safety risk, and generating a second probability that the charging system does not have safety risk;
and comparing the first probability with the second probability, and generating a prediction result of the charging system with safety risk when the first probability is larger than the second probability.
2. The charging system security risk prediction method according to claim 1, wherein the step of transmitting the prediction result to a charging system management platform comprises:
the method comprises the steps of sending a prediction result of the existence of safety risk and a risk factor report corresponding to the safety risk to a charging system management platform or a mobile phone of an operation and maintenance person;
wherein the risk factor report is generated based on the operating parameter being greater than the preset operating parameter threshold.
3. A charging system security risk prediction apparatus, characterized in that the charging system security risk prediction apparatus comprises:
the acquisition module is used for acquiring fault alarm information in historical data of related equipment of the charging system and monitoring all working parameters generated by the related equipment when the charging system is in an operating state; comparing each working parameter with a plurality of preset working parameter thresholds; when the working parameter is larger than the preset working parameter threshold, generating the fault alarm with a corresponding grade, wherein the related equipment comprises a charging pile and a transformer connected with the charging pile;
the generation module is used for generating a risk feature number of the charging system according to the fault alarm information, wherein the fault alarm information comprises fault alarm times, the fault alarm times comprise primary fault alarm times, secondary fault alarm times and tertiary fault alarm times, the risk feature number comprises a first risk feature number and a second risk feature number, the generation module is also used for generating primary fault alarm frequency in a preset time period according to the primary fault alarm times, generating secondary fault alarm frequency in the preset time period according to the secondary fault alarm times and generating tertiary fault alarm frequency in the preset time period according to the tertiary fault alarm times; the sum of the primary fault alarming frequency, the secondary fault alarming frequency and the tertiary fault alarming frequency which are multiplied by different weight values is used as the first risk feature number, wherein the weight values are increased along with the increase of fault grades, and the generating module is further used for generating the fault grade conversion rate of the charging system according to the primary fault alarming times, the secondary fault alarming times and the tertiary fault alarming times, and the fault grade conversion rate is used as the second risk feature number of the charging system;
the prediction module is used for predicting the safety risk of the charging system based on the risk feature number to generate a prediction result, and specifically, the prediction module is used for inputting the first risk feature number and the second risk feature number into a preset charging system safety risk prediction model to generate a first probability that the charging system has safety risk and generate a second probability that the charging system does not have safety risk; comparing the first probability with the second probability, and generating a prediction result of the charging system with safety risk when the first probability is larger than the second probability;
and the sending module is used for sending the prediction result to a charging system management platform so as to output prompt information.
4. A charging system security risk prediction apparatus, characterized in that the charging system security risk prediction apparatus comprises: memory, a processor and a charging system security risk prediction program stored on the memory and executable on the processor, which charging system security risk prediction program, when executed by the processor, implements the steps of the charging system security risk prediction method of any one of claims 1 to 2.
5. A readable storage medium, characterized in that a charging system security risk prediction program is stored on the readable storage medium, which when executed by a processor implements the steps of the charging system security risk prediction method according to any one of claims 1 to 2.
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