Charging pile state evaluation method, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a charging pile state evaluation method, electronic equipment and a storage medium, and relates to the technical field of charging piles, and the method comprises the steps: obtaining a basic state index and operation monitoring data of a target charging pile; the operation monitoring data is divided into charging monitoring data, environment monitoring data and fault information data associated with the charging pile according to categories; based on the operation monitoring data belonging to the same category, calculating an influence coefficient of the operation monitoring data of the category on the basic state index; and according to the basic state index and the influence coefficient of each type of operation monitoring data, the current state index of the target charging pile is calculated, and the current state index is used for representing the current health state of the target charging pile. According to the embodiment of the invention, various operation monitoring data of the target charging pile are comprehensively considered, so that the health condition of the target charging pile can be evaluated more comprehensively and accurately, an operation and maintenance scheme which better conforms to the current health state of the target charging pile can be formulated, and faults of the charging pile are prevented and reduced.

Description

Technical Field

[0001] The present application relates to the technical field of charging piles, and in particular to a charging pile status assessment method, electronic device and storage medium. Background Art

[0002] With the popularity of electric vehicles around the world, the number of charging piles is also increasing rapidly. The health of charging piles has a direct impact on the charging efficiency and safety of electric vehicles. Therefore, monitoring the health of charging piles has become an indispensable part of the development of the electric vehicle industry. In related technologies, a common technical solution is to monitor the basic electrical parameters of the charging piles, such as output voltage, current, and power, and set fixed thresholds to determine whether the charging piles are working properly. This monitoring method can only be detected after a fault occurs, which can easily lead to misjudgment and omission of whether the charging piles are in normal use. Summary of the invention

[0003] The embodiments of the present application provide a charging pile status assessment method, an electronic device, and a storage medium to alleviate or solve one or more technical problems existing in the prior art.

[0004] In a first aspect, an embodiment of the present application provides a charging pile status assessment method, comprising: obtaining basic status indicators and operation monitoring data of a target charging pile; the operation monitoring data is divided into: charging monitoring data, environmental monitoring data, and fault intelligence data of an associated charging pile;

[0005] Based on the operation monitoring data belonging to the same category, calculating the influence coefficient of the operation monitoring data of the category on the basic status indicator;

[0006] According to the basic status index and the influence coefficient of each category of operation monitoring data, the current status index of the target charging pile is calculated, and the current status index is used to represent the current health status of the target charging pile.

[0007] In some implementations, the calculating, based on the operation monitoring data belonging to the same category, the influence coefficient of the operation monitoring data of the category on the basic status indicator includes:

[0008] In the preset mapping relationship corresponding to each of the operation monitoring data, searching for the influence sub-coefficient corresponding to the operation monitoring data;

[0009] For each category of operation monitoring data, the calculation formula corresponding to the category is used to calculate the influence coefficient of the operation monitoring data of the category on the basic status indicator according to the influence sub-coefficient corresponding to each operation monitoring data of the category.

[0010] In some implementations, for the charging monitoring data in the operation monitoring data, the step of obtaining the operation monitoring data of the target charging pile includes:

[0011] Acquire at least one of the following charging monitoring data collected by the target charging pile during the charging process: vibration parameters of the target charging pile, charging sound parameters of the target charging pile, appearance monitoring image of the target charging pile, hardware module temperature of the target charging pile, and charging electrical parameters of the target charging pile.

[0012] In some implementations, for the environmental monitoring data in the operation monitoring data, the step of obtaining the operation monitoring data of the target charging pile includes:

[0013] Obtain at least one of the following environmental monitoring parameters of the target charging pile deployment environment: climate condition parameters, installation condition parameters affected by the artificially provided installation environment, electrical stability parameters of the access power grid, and electromagnetic interference parameters affected by electromagnetic interference sources within a preset range of the installation location.

[0014] In some implementations, for the fault intelligence data in the operation monitoring data, the step of obtaining the operation monitoring data of the target charging pile includes:

[0015] Obtaining the number of different types of faults that occur in each of the associated charging piles;

[0016] The calculating, based on the operation monitoring data belonging to the same category, the influence coefficient of the operation monitoring data of the category on the basic status indicator comprises:

[0017] Determine the weight corresponding to each fault type according to the impact level of each fault type on the health status;

[0018] Based on the number of times different types of faults occur in each of the associated charging piles and the weight corresponding to each of the fault types, an influence coefficient representing the potential fault risk of the target charging pile is calculated.

[0019] In some implementations, obtaining a basic status indicator of a target charging pile includes:

[0020] Obtaining factory capacity data and service life of the target charging pile for different performances;

[0021] According to the preset mapping relationship corresponding to different performances, searching for the basic status sub-indicator corresponding to the factory capacity data of the target charging pile;

[0022] The basic status index is calculated according to each of the basic status sub-indicators and the years of use.

[0023] In some embodiments, obtaining the factory capability data of the target charging pile for different performances includes:

[0024] Obtaining the factory specification document of the target charging pile;

[0025] From the factory specification file, the factory capability data of the target charging pile for different performances is extracted.

[0026] In some embodiments, after calculating the current state index of the target charging pile according to the basic state index and the influence coefficient of each category of operation monitoring data, the method further includes:

[0027] Selecting a preset operation and maintenance strategy corresponding to the status indicator;

[0028] A policy identifier corresponding to the preset operation and maintenance policy is prompted.

[0029] In a second aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory, and the processor implements the method provided by any technical solution of the embodiment of the present application when executing the computer program.

[0030] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method provided by any technical solution of the embodiment of the present application is implemented.

[0031] Based on the above-mentioned charging pile status assessment method, electronic device and storage medium, the present application has at least the following beneficial effects or advantages:

[0032] By obtaining the charging monitoring data, environmental monitoring data and fault intelligence data of the target charging pile, the impact coefficient on the basic status index is calculated. The data monitored during the charging process are comprehensively considered to evaluate the working status of the target charging pile, the impact of the use environment on the service life of the target charging pile, and the failure risk implications of other related charging piles. The health status of the target charging pile can be evaluated more comprehensively and accurately. Before the target charging pile becomes abnormal, the evaluated status indicators can be used to perceive the decline in the health status of the target charging pile in advance, which helps to formulate an operation and maintenance plan that is more in line with the current health status of the target charging pile, thereby preventing and reducing the occurrence of charging pile failures, which is beneficial to ensuring the efficiency and safety of electric vehicle charging.

[0033] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments according to the present application and should not be regarded as limiting the scope of the present application.

[0035] Figure 1 A flow chart of a charging pile status assessment method provided in an embodiment of the present application is shown;

[0036] Figure 2 A schematic diagram of a curve showing changes in basic status indicators with working years in a charging pile status assessment method provided in an embodiment of the present application is shown;

[0037] Figure 3 A block diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0038] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the concept or scope of the present application. Therefore, the drawings and descriptions are considered to be exemplary in nature and not restrictive.

[0039] To facilitate understanding of the technical solutions of the embodiments of the present application, the following describes the related technologies of the embodiments of the present application. The following related technologies can be combined with the technical solutions of the embodiments of the present application as optional solutions, and they all belong to the protection scope of the embodiments of the present application.

[0040] It should be noted that the application scenarios or application examples provided in this application are for ease of understanding, and the application of the technical solution is not specifically limited in the embodiments of this application. In addition, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0041] In the related art, there is a common technical solution for monitoring charging piles. By monitoring the basic electrical parameters of the charging piles, such as output voltage, current, and power, and setting fixed thresholds for various electrical parameters, the charging piles are judged to be working properly according to whether they exceed the thresholds. When the monitoring value exceeds the threshold, an alarm is issued and maintenance is carried out. This method can guarantee the basic operation of the charging pile to a certain extent, but it has obvious limitations. For example, only focusing on electrical parameters can only detect some obvious electrical faults, but cannot fully reflect the overall health of the charging pile. Potential problems inside the charging pile, such as aging of electronic components, communication failures, whether the mechanical structure of the charging pile is stable, whether the charging interface is worn, whether the communication module is normal, etc., cannot be detected and predicted. In addition, the setting of fixed thresholds cannot adapt to charging piles of different models and different usage environments, which is prone to misjudgment and missed judgment. In addition, the method based on manual regular inspections is prone to missed inspections and misjudgments due to reliance on manual experience. Maintenance personnel inspect the charging piles at fixed time intervals, mainly checking whether the appearance is damaged, whether the charging interface is loose, whether the display is normal, etc. This method is inefficient and cannot detect potential problems in time. When a charging pile fails, a corresponding fault code will be generated. The fault type and location can be determined by reading the fault code. However, this method can only diagnose after the fault has occurred, and cannot predict potential faults in advance, nor can it warn of possible abnormalities in the charging pile before the fault occurs.

[0042] The embodiments of the present application provide a more comprehensive, thus more accurate and efficient technical solution for evaluating the health of an electric vehicle charging pile. By comprehensively considering multiple factors, such as environmental conditions, usage frequency, charging load, etc., an objective and comprehensive evaluation of the health of the charging pile is performed to more accurately identify potential failures and signs of aging of various components inside the charging pile, including but not limited to power modules, charging interfaces, control units and communication modules, etc., and the operating status of the charging pile can be monitored in real time, and abnormal conditions that may affect charging efficiency and safety can be discovered in a timely manner, thereby reducing operating costs and maintenance difficulties and improving operating efficiency.

[0043] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The several specific embodiments listed can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0044] See also Figure 1 The flowchart of the charging pile status assessment method shown in FIG. 1 specifically includes steps 101 to 103 .

[0045] Step 101, obtaining basic status indicators and operation monitoring data of the target charging pile.

[0046] Step 102, based on the operation monitoring data belonging to the same category, calculating the influence coefficient of the operation monitoring data of the category on the basic status index;

[0047] Step 103, calculate the current state index of the target charging pile according to the basic state index and the influence coefficient of each category of operation monitoring data, and the current state index is used to represent the current health state of the target charging pile.

[0048] The basic status index is a quantitative evaluation value that comprehensively reflects the target charging pile based on the factory performance, which is used as a benchmark for evaluating the health status of the target charging pile. The basic status index is affected by the initial capacity of the target charging pile, including working capacity, ability to adapt to the working environment, etc. In some embodiments, the basic status index can also take into account the influence of the aging degree of the target charging pile, that is, the basic status index is also determined according to the working years of the target charging pile. The basic status index can be obtained by manual assignment or calculation based on quantitative parameters of different factory performance.

[0049] Operation monitoring data refers to data obtained after the target charging pile is put into use, and can be divided into charging monitoring data, environmental monitoring data, and fault intelligence data of associated charging piles according to categories. It is understandable that in each category of operation monitoring data, more than one specific operation monitoring data may be included. Charging monitoring data includes data monitored during the charging process, specifically, it may include electrical parameters monitored during the charging process, such as voltage, current, power, charging time, etc., and / or data for monitoring the physical characteristics of the target charging pile during charging, such as physical vibration, charging sound, appearance image, temperature, etc. of the target charging pile. Environmental monitoring data is used to represent the environmental conditions of the target charging pile, and specifically may include monitoring data on environmental dimensions such as climate, installation conditions, and access to the power grid stability. An associated charging pile is a charging pile that is pre-bound with a target charging pile. Charging piles of the same series, model, and batch can be bound with the target charging pile according to quantity or demand, and fault intelligence data of the associated charging pile can be obtained through real-time communication over the Internet or offline updates.

[0050] After obtaining the operation monitoring data, based on the above-mentioned category classification method, the operation monitoring data of different categories are calculated separately, and the operation monitoring data belonging to the same category are used to calculate the influence coefficient of the operation monitoring data of this category on the basic status indicators. The influence coefficient of each category of operation monitoring data is used to represent the total influence of the operation monitoring data of the corresponding category on the basic status indicators.

[0051] In this way, after calculating the influence coefficient of each category of operation monitoring data, the status indicator used to represent the current overall health status of the target charging pile can be calculated based on the basic status indicator and the influence coefficient of each category of operation monitoring data. The calculated status indicator comprehensively considers various influencing factors of the target charging pile, so that it can more accurately indicate the health status of the target charging pile. When executing subsequent operation and maintenance strategies based on the status indicator, better execution results can also be obtained.

[0052] The embodiment of the present application obtains the charging monitoring data, environmental monitoring data and fault intelligence data of the target charging pile, calculates the influence coefficient on the basic status index, and comprehensively considers the data monitored during the charging process to evaluate the working status of the target charging pile, the impact of the use environment on the service life of the target charging pile, and the failure risk implications of other related charging piles. The health status of the target charging pile can be evaluated more comprehensively and accurately, and the health status of the target charging pile can be perceived in advance by using the evaluated status indicators before the target charging pile becomes abnormal, which helps to formulate an operation and maintenance plan that is more in line with the current health status of the target charging pile, thereby preventing and reducing the occurrence of charging pile failures, which is beneficial to ensuring the efficiency and safety of electric vehicle charging.

[0053] In an example implementation, step 101 obtains the basic status index of the target charging pile, which may specifically include executing the following steps: obtaining the factory capacity data and service life of the target charging pile for different performances; searching for the basic status sub-indicator corresponding to the factory capacity data of the target charging pile according to the preset mapping relationship corresponding to different performances; and calculating the basic status index according to each basic status sub-indicator and service life.

[0054] The factory capability data of different performances may include at least one of the following performance dimensions: shell protection performance, authoritative certification, heat dissipation performance, power factor, mean trouble-free working time, temperature range adaptability, humidity resistance and salt spray corrosion resistance, electromagnetic interference resistance and anti-static resistance. Various performances may correspond to different preset mapping relationships. In some embodiments, the factory capability data is the level of various performances, and the basic status sub-indicator is the score. Accordingly, the mapping table preset for a certain performance includes the corresponding relationship between the different levels of the performance and the score.

[0055] Refer to Tables 1 to 8 below, which are examples of preset mapping relationship tables between levels and scores of different performances.

[0056] Table 1 Preset mapping relationship between enclosure protection level and rating

[0057]

[0058] Table 2 Preset mapping relationship between authoritative certification levels and scores

[0059]

[0060] Table 3 Preset mapping relationship between heat dissipation performance level and score

[0061]

[0062] Table 4 Preset mapping relationship between power factor level and score

[0063] grade score Power factor range Features High Level 5 ≥0.98 The power utilization efficiency is extremely high, the impact on the power grid is small, and the energy saving effect is significant Intermediate 4 0.95-0.98 The power utilization efficiency is high and can better adapt to the requirements of the power grid Low level 3 ＜0.95 The efficiency of electric energy utilization is low, which may have an adverse impact on the power grid and needs to be improved

[0064] Table 5 Preset mapping relationship between mean trouble-free working time level and score

[0065]

[0066] Table 6 Preset mapping relationship between temperature range adaptability level and score

[0067]

[0068] Table 7 Preset mapping relationship between humidity resistance and salt spray corrosion resistance level and score

[0069]

[0070] Table 8 Preset mapping relationship between anti-electromagnetic interference capability and anti-static capability level and score

[0071]

[0072] According to the performance text description or performance level provided in the factory capacity data of the target charging pile, the corresponding specific score is found in the corresponding preset mapping relationship as the basic status sub-indicator. In some embodiments, the factory capacity data can be manually input. In other embodiments, the executor of the method provided in the embodiment of the present application can obtain the factory specification file (electronic file) of the target charging pile, so as to extract the factory capacity data of the target charging pile for different performances in the factory specification file. For example, the factory capacity data corresponding to each performance can be determined in combination with keyword recognition, semantic analysis and other methods. By extracting the factory capacity data from the factory specification file, manual input operations can be reduced, manpower can be saved, possible errors in manual operations can be reduced, and the efficiency of obtaining factory capacity data can be improved.

[0073] After obtaining each basic state sub-indicator, the preset algorithm can be used to calculate the initial state indicator of the target charging pile. The initial state indicator represents the state of the target charging pile when it is not in use and after leaving the factory. In one embodiment, after finding the scores corresponding to the factory capacity data, the performance corresponding to Tables 1 to 8 above can be recorded as C1 to C8 respectively, and the working life of the target charging pile is recorded as C9, where C9 takes the negative value of the working life. The calculation formula of the initial state indicator B0 is as follows:

[0074]

[0075] Calculate the aging coefficient b of the target charging pile based on its working years:

[0076]

[0077] T is the expected life of the target charging pile, and B1 is the preset scoring benchmark when the target charging pile is retired.

[0078] The calculation formula of a basic status indicator B is as follows:

[0079] B=(1-(1-B0)e bt )*100

[0080] t is the variable parameter of working years. Assuming T is 10 years, B0 = 0.95, B1 = 0.35, the aging coefficient b = 0.26 is calculated. Correspondingly, the curve of the basic status index B changing with the actual working years t is as follows: Figure 2 shown.

[0081] This implementation can fully consider the factory capacity and aging of the actual service life of the target charging pile, and calculate a basic status index that is more in line with the current service life. In some implementations, the influence coefficient of each category of operation monitoring data can be a normalized coefficient, and each influence coefficient is directly multiplied by the basic status index to obtain the actual status index (current status index) that comprehensively considers various monitoring conditions of the target charging pile.

[0082] In some embodiments, the above-mentioned step 102 calculates the influence coefficient of the operation monitoring data of the category on the basic status indicator based on the operation monitoring data belonging to the same category, and specifically includes executing the following steps: searching for the influence sub-coefficient corresponding to the operation monitoring data in the preset mapping relationship corresponding to each operation monitoring data; for each category of operation monitoring data, using the calculation formula corresponding to the category, and calculating the influence coefficient of the operation monitoring data of the category on the basic status indicator according to the influence sub-coefficient corresponding to each operation monitoring data of the category.

[0083] For each specific operation monitoring data, its corresponding preset mapping relationship can be set respectively. The preset mapping relationship is used to describe the relationship between different data contents and influencing sub-coefficients. Its specific implementation can be a mapping formula or a table. By finding the influencing sub-coefficient corresponding to each operation monitoring data through the preset mapping relationship, the quantitative accuracy of each operation monitoring data on the health status can be improved. Different categories of operation monitoring data have different impacts on the basic status indicators. Calculating the total impact coefficient of each category of operation monitoring data on the basic status indicators by category can improve the interpretability of the impact coefficient and provide a clearer intermediate result for the operation and maintenance decision makers of the target charging pile.

[0084] The charging monitoring data in the operation monitoring data may specifically include: vibration parameters of the target charging pile, charging sound parameters of the target charging pile, appearance monitoring images of the target charging pile, hardware module temperature of the target charging pile, and charging electrical parameters of the target charging pile.

[0085] As the charging pile ages with use, the mechanical structure will also wear out, and the connectors will become loose, causing the vibration of the charging pile to intensify. Therefore, monitoring the vibration of the target charging pile is conducive to monitoring the degree of hardware aging of the target charging pile. The charging pile includes multiple hardware. In some embodiments, the vibration sensor can be set on one or more key components, including but not limited to the power module, charging interface, control unit and communication module, or on the outer shell of the target charging pile. The embodiments of the present application are not limited and are only used for example. The vibration parameters provide a representative monitoring interface for the health status of the charging pile. By monitoring the vibration / displacement of the mechanical components inside the charging pile, the vibration parameters can be used to detect the early wear and looseness of the mechanical components, thereby improving the fault prevention capability of the charging pile. In some embodiments, the vibration parameters monitored at different times can be converted into a vibration parameter time series. Using time series analysis technology, the changes in vibration parameters can be mined through deep learning, and the vibration parameters generated by abnormal mechanical components can be extracted to provide support for evaluating the health of the charging pile.

[0086] The aging of electrical components in a charging pile may cause changes in the sound of the current generated during charging. Therefore, by monitoring the charging sound parameters of the target charging pile, early signs of aging or other failures of the electrical components of the charging pile can be discovered in time, so that repairs or replacements can be performed in advance to avoid failures, helping operation and maintenance personnel to find problems in time and extend the service life of the equipment. In some embodiments, voiceprint recognition technology can be used to capture subtle sound changes such as abnormal discharge of electrical components. Specifically, the frequency domain features of abnormal discharge of electrical components can be identified in the frequency domain. For example, sound samples of different levels of abnormal discharge can be obtained to train a machine learning model to identify the level of abnormal sound through the machine learning model.

[0087] The appearance monitoring image of the target charging pile can be collected by an image / video acquisition device, which can be specifically set in the environment where the target charging pile is installed. The installation location of the image / video acquisition device can be selected according to the actual installation situation. It can be understood that an image / video acquisition device can monitor the appearance of one or more charging piles, and image detection can be performed on one or more parts of the charging pile in the collected image. For example, a computer vision (CV) algorithm can be used to detect whether the charging gun is damaged.

[0088] The hardware modules of the target charging pile include a charging module, a gun line, a charging gun, a heat dissipation module, etc. In one application scenario of an embodiment of the present application, a monitoring module for monitoring temperature and / or humidity can be set on at least one hardware module of the target charging pile to monitor the temperature and humidity of the hardware module during the charging process. Different coefficient values ​​may correspond to different temperature and humidity ranges. In addition, early warnings can also be issued separately through monitored data. For example, when the cooling fan of the charging pile fails and causes the internal temperature to rise, the temperature sensor will immediately sound an alarm to avoid damage to key components due to overheating.

[0089] The charging electrical parameters of the target charging pile may include charging efficiency, output voltage stability / output current stability, etc. Charging efficiency is used to measure the ratio of input power to the power actually used for battery charging. High efficiency means less energy loss. For example, for a 100kW charging pile, if the actual input power is 110kW and the power used for charging is 95kW, the charging efficiency is about 86%. Higher efficiency can reflect the good performance of the charging pile. In one embodiment, the monitoring frequency of charging efficiency can be calculated once a day, and the average charging efficiency of the previous day is calculated based on the average charging efficiency of all usage records of the previous day. The deviation of the output voltage and current from the set value should be stable within a small range to ensure accurate control of the battery charging process. When the voltage is constant, the output voltage stability is considered, and when the current is constant, the output current stability is considered. Therefore, the stability of voltage or current can represent the operating state of the target charging pile. In one embodiment, the voltage / current stability can be calculated once a day, and the average value is calculated based on the accuracy range of all voltages and currents of the previous day.

[0090] In addition, charging monitoring parameters such as inspection frequency, electrical fault frequency, and equipment alarm level during the use of the target charging pile can also be monitored.

[0091] According to the description of the above specific implementation methods, it can be understood that in addition to being used to calculate the impact coefficient on the basic status indicator, the operation monitoring data can also be used as a monitoring indicator alone to facilitate operation and maintenance personnel to observe the operating status of the target charging pile in various monitoring dimensions.

[0092] The charging pile status assessment method provided in some embodiments of the present application combines multi-sensor fusion technology. By integrating various types of sensors, in addition to traditional electrical parameters, it also covers multiple dimensions such as mechanical structure, environmental factors, voiceprint characteristics, and visual images. By monitoring the vibration and displacement of the mechanical parts inside the charging pile, the wear and looseness of the mechanical parts can be detected early; environmental sensors can sense the impact of factors such as temperature, humidity, and dust on the charging pile in real time, and take protective measures in advance; voiceprint recognition technology can capture subtle sound changes such as abnormal discharge of electrical components; and the use of cameras can monitor the appearance of the charging pile and the use status of the charging gun. The synchronous collection and monitoring of multiple key parameters of the charging pile can be achieved through an all-round, multi-dimensional comprehensive monitoring strategy, which is the basis for the comprehensive evaluation of the present invention.

[0093] Refer to Tables 9 to 13 below, which are examples of preset mapping relationships between charging monitoring data of several different dimensions and influencing sub-coefficients.

[0094] Table 9 Preset mapping relationship between voltage / current stability and influencing factor coefficient

[0095] Electrical parameters\coefficients 1.0 0.95 0.90 0.8 Voltage accuracy range Within ±1% ±1％-±2％ ±2％-±3％ Exceed ±3% Current accuracy range Within ±3% ±3％-±5％ ±5％-±7％ Exceed ±7%

[0096] Table 10 Preset mapping relationship between inspection frequency and influencing factor coefficient

[0097]

[0098] The specific contents of the above manual inspection include checking electrical connections, cleaning filters, testing protection devices, etc.

[0099] Table 11 Preset mapping relationship between heat dissipation outlet temperature and influencing factor coefficient

[0100]

[0101] Table 12 Preset mapping relationship table of fault frequency and influencing factor coefficient

[0102]

[0103] Table 13 Preset mapping relationship between equipment alarm fault level and impact sub-coefficient

[0104] Failure level coefficient Description of equipment alarm fault levels fatal 0 Faults that require the charging pile to be deactivated serious 0.5 It is recommended to stop using the charging pile due to fault and it must be repaired. generally 0.7 The function of the charging pile is affected by the fault, but it can still be used slight 0.9 The fault is a prompt message. The charging pile function does not affect the use and can be used normally. No trouble 1 No faults reported currently

[0105] According to OCPP (Open Charge Point Protocol) or China Electricity Council protocol, charging piles should detect and report relevant characteristic fault information. According to the real-time alarm code of the equipment, the corresponding fault level can be determined, and then the corresponding influence sub-coefficient can be found according to the fault level in Table 13.

[0106] According to the at least one charging monitoring parameter, the influence coefficient R of the charging monitoring parameter on the basic status index is calculated:

[0107]

[0108] In the above formula, k is the fault factor, pi is the influencing coefficient of the i-th charging monitoring parameter, pri is the reference value of the influencing coefficient of the i-th charging monitoring parameter, a is a constant, and n is the number of charging monitoring parameters.

[0109] In one example, the charging monitoring parameters include four performance parameters: charging efficiency (actual value 95%, reference value 98%), current and voltage accuracy (actual value 0.95, reference value 1), regular inspection rate (actual value 0.95, reference value 0.98), fault frequency (actual value 0.96, reference value 0.98), a = 10, then the calculated health R is:

[0110]

[0111] The environmental monitoring data in the operation monitoring data may specifically include: climate condition parameters, installation condition parameters affected by the artificially provided installation environment, electrical stability parameters of the access grid, and electromagnetic interference parameters affected by electromagnetic interference sources within a preset range of the installation location.

[0112] Refer to Tables 14 to 17 below, which are examples of preset mapping relationships between environmental monitoring data and influencing sub-coefficients for several examples.

[0113] Table 14 Preset mapping relationship between climate condition level and influencing sub-coefficient

[0114]

[0115] Table 15 Preset mapping relationship between installation environment level and influencing factor

[0116]

[0117] Table 16 Preset mapping relationship between grid stability level and influencing factor coefficient

[0118] grade coefficient Voltage Stability Parameter Description high 1 The voltage deviation average value is within ±2%, the maximum and minimum values ​​are within ±5%, and the standard deviation is small middle 0.98 The average voltage deviation is within ±2%-±5%, the maximum and minimum values ​​are within ±8%, and the standard deviation is small Low 0.95 The voltage deviation average value exceeds ±5%, the maximum and minimum values ​​exceed ±8%, and the standard deviation is large

[0119] Table 17 Preset mapping relationship between electromagnetic interference level and impact factor

[0120]

[0121] After the corresponding influencing sub-coefficients are determined according to different environmental monitoring parameters, the environmental monitoring parameters can be directly multiplied to obtain the total influencing coefficient E of the operating monitoring parameters of this type of environmental monitoring parameters on the basic status indicators.

[0122] For the fault intelligence data in the operation monitoring data, the operation monitoring data of the target charging pile is obtained in the above step 101, and specifically the number of times different types of faults occur in each associated charging pile can be obtained. Then, step 102 calculates the influence coefficient of the category operation monitoring data on the basic state index based on the operation monitoring data belonging to the same category, which can specifically include the following steps: according to the impact level of each fault type on the health state, the weight corresponding to each fault type is determined, and then based on the number of different types of faults that occur in each associated charging pile and the weight corresponding to each fault type, the influence coefficient used to represent the potential fault risk of the target charging pile is calculated. The faults that occur in the associated charging piles may be family defects brought to the "charging pile family" due to factors such as the design, material, and process of the equipment manufacturer. Therefore, using the real faults that have occurred in the associated charging piles, a more accurate prediction and evaluation of the potential fault risk of the target charging pile can be made, thereby improving the accuracy of the calculated state index.

[0123] By using the fault intelligence data of the associated charging piles, assigning weights to each fault type, and calculating the potential fault risk impact coefficient of the target charging pile in combination with the number of fault occurrences, it not only improves the accuracy of fault prediction, but also helps to identify and prevent possible family defects. In this way, it helps to take measures in advance for monitoring and maintenance, reduce the occurrence of faults, extend the life of equipment, and improve the reliability and safety of charging piles, thereby optimizing the overall performance of the charging network.

[0124] In one example, the impact level corresponding to the fault type of the associated charging pile can be determined according to the fault code of the associated charging pile, and then the corresponding weight is determined. Refer to Table 18 below, which is an example of a preset mapping relationship table of fault levels and weights.

[0125] Table 18 Preset mapping relationship between fault level and weight

[0126]

[0127] An example formula for calculating the influence coefficient F of the associated charging pile is:

[0128]

[0129] In the above formula, A ijrepresents the number of times the jth fault occurs in the i-th device, B j represents the weight of the jth fault, M represents the total number of fault types, and N represents the total number of devices.

[0130] For example, suppose that the total number of devices associated with the charging pile is 10, and 4 types of faults have occurred. For the first device, the first fault occurred once, the second fault occurred once, the third fault did not occur, and the fourth fault occurred twice; for the second device, the first fault did not occur, the second fault occurred twice, the third fault occurred once, and the fourth fault occurred twice. The weights of the four faults are 0.9, 0.8, 0.7, and 0.6, respectively. Then: numerator = (1×0.85+1×0.9+2x0.98)+(2×0.9+1×0.95+2×0.98)+....(calculated to the 10th device)≈8.45, denominator = (1+1+2)+(2+1+2)+....(calculated to the 10th device)≈9, and finally the influence coefficient of family defects F = 8.45 / 9≈0.94 is obtained.

[0131] By multiplying the above-mentioned basic status indicator B, the influence coefficient R of the charging monitoring data, the influence coefficient E of the environmental monitoring data, and the influence coefficient F of the potential failure risk determined according to the associated charging pile failure, we can get the status indicator L=B×R×E×F representing the current health status of the target charging pile.

[0132] In some embodiments, after executing step 103 to calculate the current state index of the target charging pile according to the basic state index and the influence coefficient of each category of operation monitoring data, a preset operation and maintenance strategy corresponding to the state index can also be selected, and a strategy identifier corresponding to the preset operation and maintenance strategy can be prompted. The prompting method can be by setting an alarm light on site (charging pile installation site or operation and maintenance personnel work site) or displaying the operation and maintenance strategy representation on the on-site display device. An example of the correspondence table between state indicators, indicator light colors and operation and maintenance strategies is shown in Table 19 below. By assigning corresponding operation and maintenance strategies to different state indicators in advance and giving corresponding prompts, it is convenient for operation and maintenance personnel to perform maintenance in accordance with the indicated operation and maintenance strategies, solve the decision-making problems of operation and maintenance personnel, simplify the decision-making steps of operation and maintenance personnel, and adopt a more suitable operation and maintenance strategy to maintain the target charging pile, which can extend the service life of the target charging pile.

[0133] Table 19 Operation and maintenance strategy correspondence table

[0134] score Rating color Description of health status and operation and maintenance strategy 71-100 points normal green No major impact on the safe operation of the equipment, the risk of sudden deterioration is very small, and no manual maintenance is required 51~70 points Notice blue It does not endanger the safe operation of the equipment for the time being, the risk of sudden deterioration is not high, and no manual maintenance is required 31-50 minutes abnormal yellow There is a certain threat to the safe operation of the equipment, and personnel need to be arranged to check in time 0-30 minutes serious red There is a serious threat to the safe operation of the equipment. Charging needs to be stopped and personnel should be arranged for repairs.

[0135] By establishing the above mathematical model, the embodiment of the present application can conduct in-depth analysis of the operation monitoring data of the target charging pile, predict potential failure risks, and evaluate health development trends, so that operation and maintenance personnel can formulate maintenance plans corresponding to the current health status. Operation and maintenance personnel can prepare the required parts and tools in advance, effectively reducing the cost of maintenance by operation and maintenance personnel and the possibility of service interruption. For example, by analyzing the historical operation data of the charging pile, it is predicted that a certain charging module may fail in the future, so that replacement can be arranged in advance, avoiding the inconvenience caused to users by sudden failures.

[0136] The charging pile status assessment method provided in some embodiments of the present application can construct an efficient and stable real-time monitoring data transmission network and an efficient data processing center to obtain multi-dimensional operation monitoring parameters, ensure the rapid transmission and timely analysis of monitoring data, and provide guarantees for real-time monitoring and early warning. With the help of high-speed communication networks and real-time data processing technology, uninterrupted real-time monitoring of charging piles can be achieved. Once any abnormality occurs, the monitoring system using the method provided in the embodiment of the present application can immediately send detailed early warning information to the operation and maintenance personnel, including the type, location and severity of the fault, to ensure that the problem can be handled in a timely manner. For example, if the output current of the charging pile suddenly fluctuates abnormally, the system will issue an alarm to notify the maintenance personnel to take prompt action to prevent the fault from expanding.

[0137] In some embodiments, a model can be constructed that can dynamically adjust the rating / score / parameter thresholds in the above-mentioned various preset mapping tables according to the individual differences and usage environments of different charging piles. Through the constructed adjustment model, the mapping relationships in various preset mapping tables are adjusted to make the evaluation results more accurate and in line with the actual situation, and potential health problems can be more keenly discovered, thereby improving the accuracy and adaptability of the status evaluation. For example, for charging piles installed in cold areas, the system will automatically adjust the temperature-related evaluation parameters to adapt to the impact of low temperature environments on the equipment.

[0138] Figure 3 FIG. 1 is a block diagram of an electronic device used to implement an embodiment of the present application. Figure 3 As shown, the electronic device includes: a memory 301 and a processor 302, and the memory 301 stores a computer program that can be run on the processor 302. When the processor 302 executes the computer program, the method in the above embodiment is implemented. The number of the memory 301 and the processor 302 can be one or more. In a specific implementation, the electronic device may also include a communication interface 303 for communicating with external devices and performing data exchange transmission.

[0139] In specific implementation, if the memory 301, the processor 302 and the communication interface 303 are implemented independently, the memory 301, the processor 302 and the communication interface 303 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0140] Optionally, in a specific implementation, if the memory 301, the processor 302 and the communication interface 303 are integrated on a chip, the memory 301, the processor 302 and the communication interface 303 can communicate with each other through an internal interface.

[0141] An embodiment of the present application provides a computer-readable storage medium storing a computer program, which implements the method provided in the embodiment of the present application when the program is executed by a processor.

[0142] An embodiment of the present application provides a computer program product, including a computer program, which implements the method provided in the embodiment of the present application when executed by a processor.

[0143] An embodiment of the present application also provides a chip, which includes a processor for calling and executing instructions stored in the memory from the memory, so that a communication device equipped with the chip executes the method provided by the embodiment of the present application.

[0144] An embodiment of the present application also provides a chip, including: an input interface, an output interface, a processor and a memory, wherein the input interface, the output interface, the processor and the memory are connected via an internal connection path, and the processor is used to execute the code in the memory. When the code is executed, the processor is used to execute the method provided in the embodiment of the application.

[0145] It should be understood that the processor may be a CPU (Central Processing Unit), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. It is worth noting that the processor may be a processor supporting the Advanced RISC Machines (ARM) architecture.

[0146] Further, optionally, the above-mentioned memory may include a read-only memory and a random access memory. The memory may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory. Among them, the non-volatile memory may include a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may include a random access memory (RAM), which is used as an external cache. By way of exemplary but not limiting description, many forms of RAM are available. For example, static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct memory bus random access memory (DR RAM).

[0147] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0148] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0149] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0150] Any process or method described in the flow chart or otherwise described herein can be understood as a module, fragment or portion of a code representing one or more executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiment of the present application includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0151] The logic and / or steps described in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or used in combination with these instruction execution systems, devices or apparatuses.

[0152] It should be understood that the various parts of the present application can be implemented with hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium, and when the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0153] In addition, each functional unit in each embodiment of the present application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a disk or an optical disk, etc.

[0154] The above is only an exemplary embodiment of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope recorded in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A charging pile status assessment method, characterized in that: The method comprises: Obtaining basic status indicators and operation monitoring data of the target charging pile; the operation monitoring data is divided into: charging monitoring data, environmental monitoring data, and fault intelligence data of the associated charging pile; Based on the operation monitoring data belonging to the same category, calculating the influence coefficient of the operation monitoring data of the category on the basic status indicator; According to the basic status index and the influence coefficient of each category of operation monitoring data, the current status index of the target charging pile is calculated, and the current status index is used to represent the current health status of the target charging pile.

2. The method according to claim 1, characterized in that The calculating, based on the operation monitoring data belonging to the same category, the influence coefficient of the operation monitoring data of the category on the basic status indicator comprises: In the preset mapping relationship corresponding to each of the operation monitoring data, searching for the influence sub-coefficient corresponding to the operation monitoring data; For each category of operation monitoring data, the calculation formula corresponding to the category is used to calculate the influence coefficient of the operation monitoring data of the category on the basic status indicator according to the influence sub-coefficient corresponding to each operation monitoring data of the category.

3. The method according to claim 1 or 2, characterized in that: With respect to the charging monitoring data in the operation monitoring data, the step of obtaining the operation monitoring data of the target charging pile includes: Acquire at least one of the following charging monitoring data collected by the target charging pile during the charging process: vibration parameters of the target charging pile, charging sound parameters of the target charging pile, appearance monitoring image of the target charging pile, hardware module temperature of the target charging pile, and charging electrical parameters of the target charging pile.

4. The method according to claim 1 or 2, characterized in that: With respect to the environmental monitoring data in the operation monitoring data, the step of obtaining the operation monitoring data of the target charging pile includes: Obtain at least one of the following environmental monitoring parameters of the target charging pile deployment environment: climate condition parameters, installation condition parameters affected by the artificially provided installation environment, electrical stability parameters of the access power grid, and electromagnetic interference parameters affected by electromagnetic interference sources within a preset range of the installation location.

5. The method according to claim 1 or 2, characterized in that: With respect to the fault intelligence data in the operation monitoring data, the step of obtaining the operation monitoring data of the target charging pile includes: Obtaining the number of different types of faults that occur in each of the associated charging piles; The calculating, based on the operation monitoring data belonging to the same category, the influence coefficient of the operation monitoring data of the category on the basic status indicator comprises: Determine the weight corresponding to each fault type according to the impact level of each fault type on the health status; Based on the number of times different types of faults occur in each of the associated charging piles and the weight corresponding to each of the fault types, an influence coefficient representing the potential fault risk of the target charging pile is calculated.

6. The method according to claim 1, characterized in that The step of obtaining the basic status indicator of the target charging pile includes: Obtaining factory capacity data and service life of the target charging pile for different performances; According to the preset mapping relationship corresponding to different performances, searching for the basic status sub-indicator corresponding to the factory capacity data of the target charging pile; The basic status index is calculated according to each of the basic status sub-indicators and the years of use.

7. The method according to claim 6, characterized in that The obtaining of the factory capability data of the target charging pile for different performances includes: Obtaining the factory specification document of the target charging pile; From the factory specification file, the factory capability data of the target charging pile for different performances is extracted.

8. The method according to claim 1, characterized in that After calculating the current state index of the target charging pile according to the basic state index and the influence coefficient of each category of operation monitoring data, the method further includes: Selecting a preset operation and maintenance strategy corresponding to the status indicator; A policy identifier corresponding to the preset operation and maintenance policy is prompted.

9. An electronic device comprising a memory, a processor and a computer program stored in the memory, wherein the processor implements the method according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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