Fault early warning method of charging pile, electronic equipment and storage medium

Abstract

The invention relates to the technical field of charging pile operation and maintenance, particularly provides a fault early warning method of a charging pile, electronic equipment and a storage medium, and aims to solve the problems of lagging fault discovery, low diagnosis efficiency, poor precision and lack of preventive maintenance capability in a charging pile fault detection technology. In order to achieve the purpose, the fault early warning method for the charging pile comprises the steps that charging order data of the charging pile and real-time environment data corresponding to the charging order data are acquired; based on the charging order data and the real-time environment data, obtaining a fault early warning analysis result of the charging pile; and performing fault early warning on the charging pile according to the fault early warning analysis result. The charging order data and the real-time environment data are fused, active prevention of environment induced faults, real-time monitoring of the operation state and early warning of the multi-parameter collaborative deterioration risk are achieved, operation and maintenance are promoted to be converted into active early warning from passive first-aid repair, the efficiency is remarkably improved, and the cost is reduced.

Description

Fault warning methods, electronic devices and storage media for charging piles Technical Field

[0001] This application relates to the field of charging pile operation and maintenance technology, specifically providing a fault early warning method, electronic device and storage medium for charging piles. Background Technology

[0002] With the rapid development of new energy sources, the scale of charging infrastructure has expanded rapidly, placing higher demands on the efficiency and reliability of charging pile operation and maintenance. However, the current charging pile operation and maintenance system faces three core challenges that severely restrict the reliability of the charging network and the service experience.

[0003] First, fault detection is severely delayed. The current model relies heavily on users actively reporting faults and periodic manual inspections. However, charging piles are widely distributed in complex environments such as outdoors and underground. On average, troubleshooting a single charging pile fault takes 30 to 40 minutes, and several hours in multi-pile scenarios. Faults often go undetected for a long time at night or in remote areas.

[0004] Second, fault diagnosis is inefficient and inaccurate. Traditional methods rely on maintenance personnel to use handheld instruments to check point by point. Manual diagnosis is difficult to correlate with multi-dimensional operational data. For example, it is impossible to establish a causal relationship between power grid fluctuations and charging interruptions. It can only achieve "fault existence determination" rather than "root cause analysis", resulting in a high rate of repeated repairs and increased handling costs.

[0005] Third, preventative maintenance capabilities are weak. The existing system lacks an early warning mechanism for progressive and latent faults. In fact, most electrical faults are caused by progressive factors, and some faults are preventable. However, due to the inability to identify fault precursors, similar problems recur. At the same time, in extreme environments such as high temperatures (e.g., above 50°C) or low temperatures (below -20°C), the system also lacks the ability to predict risks, leading to a high incidence of seasonal faults.

[0006] Accordingly, there is a need in this field for a new charging pile fault early warning scheme to solve the above problems. Summary of the Invention

[0007] In order to overcome the above-mentioned deficiencies, this application is made to provide solutions or at least partially solve the technical problems of existing charging pile fault detection technologies, such as delayed fault detection, low diagnostic efficiency, poor accuracy, and lack of preventive maintenance capabilities.

[0008] In a first aspect, this application provides a method for fault early warning of a charging pile, the method comprising: acquiring charging order data of the charging pile and real-time environmental data corresponding to the charging order data; acquiring fault early warning analysis results of the charging pile based on the charging order data and the real-time environmental data; and issuing a fault early warning for the charging pile based on the fault early warning analysis results.

[0009] In one technical solution of the above-mentioned charging pile fault early warning method, the step of obtaining the charging pile fault early warning analysis result based on the charging order data and the real-time environmental data includes: inputting the charging order data, the real-time environmental data, and the charging pile operation data corresponding to the charging order data into a preset fault judgment engine; the fault judgment engine matches the combination of input data with a preset risk combination database; wherein, the preset risk combination database stores multiple high-risk parameter combinations, each high-risk parameter combination including at least two high-risk combinations of parameters from the real-time environmental data, charging order data, and charging pile operation data; if the match is successful, the fault judgment engine outputs the charging pile fault early warning analysis result, the fault early warning analysis result including at least the risk level and fault type of the charging pile.

[0010] In one technical solution of the above-mentioned charging pile fault early warning method, the method further includes: determining preset characteristic indicators based on the charging order data and the charging pile operation data corresponding to the charging order data; wherein, the preset characteristic indicators include at least the charging volume growth rate, current fluctuation coefficient, voltage stability and charging pile temperature; inputting the preset characteristic indicators into a preset fault judgment engine, the fault judgment engine comparing the preset characteristic indicators with preset fault thresholds; if at least one preset characteristic indicator exceeds the corresponding preset fault threshold, the fault judgment engine outputs the fault early warning analysis result of the charging pile, the fault early warning analysis result including at least the risk level and fault type of the charging pile.

[0011] In one technical solution of the above-mentioned fault warning method for charging piles, the method further includes: acquiring real-time environmental data of the location of the charging pile; determining whether the real-time environmental data of the location of the charging pile exceeds a preset extreme environmental threshold; if it exceeds the threshold, generating a fault warning analysis result for the charging pile, wherein the fault warning analysis result includes at least the risk level and fault type of the charging pile.

[0012] In one technical solution of the above-mentioned fault early warning method for charging piles, the step of issuing a fault early warning for the charging pile based on the fault early warning analysis results includes: determining the early warning level based on the fault type and risk level of the fault early warning analysis results; generating graded early warning information for the charging pile based on the fault early warning analysis results, wherein the graded early warning information includes at least the fault type, risk level, and recommended handling solution; and selecting a corresponding early warning push method to push the graded early warning information based on the early warning level.

[0013] In one technical solution of the above-mentioned fault early warning method for charging piles, the method further includes: collecting fault handling result data, wherein the fault handling result data is associated with corresponding graded early warning information; and dynamically adjusting at least one of the preset fault threshold, the preset extreme environment threshold, and the preset risk combination database based on the fault handling result data.

[0014] In one technical solution of the above-mentioned charging pile fault early warning method, the step of dynamically adjusting at least one of the preset fault threshold, the preset extreme environment threshold, and the preset risk combination database based on the fault processing result data includes: calculating the early warning accuracy rate corresponding to each fault type based on the fault processing result data; and adjusting the corresponding preset fault threshold, preset extreme environment threshold, or high-risk parameter combination in the preset risk combination database based on the early warning accuracy rate corresponding to each fault type.

[0015] In one technical solution of the above-mentioned charging pile fault early warning method, the step of obtaining the charging order data of the charging pile and the real-time environmental data corresponding to the charging order data includes: in response to the update of the charging order cache data of the charging pile, obtaining the charging order data of the charging pile, wherein the charging order data includes at least charging pile information, charging data, and historically reported detailed data; the charging data includes charging amount and charging duration; the historically reported detailed data includes current data, voltage data, and power data reported according to a preset period; and based on the charging pile information, obtaining the real-time environmental data of the charging pile; wherein the real-time environmental data includes at least ambient temperature data, ambient humidity data, and precipitation rate data.

[0016] In a second aspect, an electronic device is provided, comprising a processor and a memory, the memory being adapted to store a plurality of program codes, the program codes being adapted to be loaded and executed by the processor to perform the fault warning method for charging piles as described in any of the above-described technical solutions.

[0017] In a third aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, the program codes being adapted to be loaded and run by a processor to perform the fault warning method for charging piles as described in any of the above-described technical solutions.

[0018] The above-described technical solutions of this application have at least one or more of the following beneficial effects:

[0019] The charging pile fault early warning method of this application includes: acquiring charging order data of the charging pile and real-time environmental data corresponding to the charging order data; acquiring fault early warning analysis results of the charging pile based on the charging order data and the real-time environmental data; and issuing fault early warnings for the charging pile based on the fault early warning analysis results. This application, by fusing and analyzing charging order data and real-time environmental data, can not only proactively trigger preventative maintenance based on real-time environmental data, effectively reducing the incidence of environment-induced faults, but also monitor the charging pile's operating status in real time, and provide early warnings for the combined risks of charging order data and real-time environmental data that have not yet reached a single threshold but show a trend of coordinated deterioration. This achieves a transformation from "passive emergency repair" to "proactive early warning" in intelligent maintenance, thereby significantly improving maintenance efficiency and reducing operating costs. Attached Figure Description

[0020] The preferred embodiments of this application are described below with reference to the accompanying drawings, in which:

[0021] Figure 1 is a schematic flowchart of the main steps of a fault warning method for a charging pile according to an embodiment of this application;

[0022] Figure 2 is a detailed flowchart illustrating the steps of a fault warning method for a charging pile according to an embodiment of this application;

[0023] Figure 3 is a schematic block diagram of the main structure of an electronic device according to an embodiment of the present application.

[0024] List of reference numerals in the attached diagram:

[0025] 11: Memory; 12: Processor. Detailed Implementation

[0026] Some embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application.

[0027] In the description of this application, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and can also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" or "this" can also include plural forms.

[0028] The existing charging pile operation and maintenance technology mainly has the following three problems:

[0029] Delayed fault detection: Relying on user reports and manual inspections results in slow response and incomplete coverage. Faults are particularly difficult to detect in a timely manner at night, in remote or complex environments, leading to delays in fault handling and a decline in user experience.

[0030] Insufficient fault diagnosis capabilities: The diagnostic methods are limited, relying on manual experience and on-site instruments to troubleshoot point by point. There is a lack of integrated analysis of multi-dimensional operating data such as voltage, current, communication, and power grid fluctuations. It can only determine whether there is a fault, but cannot accurately locate the root cause, resulting in repeated maintenance and waste of resources.

[0031] Lack of preventive early warning mechanisms: It is unable to identify the precursors of progressive failures caused by heat dissipation failure, contact degradation, etc., and has no ability to intervene in preventable failures in advance; at the same time, it lacks risk prediction under extreme high and low temperature environmental conditions, resulting in frequent and recurring seasonal and environmentally related failures.

[0032] To address this, this application provides a method for early warning of charging pile faults, comprising: acquiring charging order data of the charging pile and real-time environmental data corresponding to the charging order data; acquiring fault warning analysis results of the charging pile based on the charging order data and the real-time environmental data; and issuing a fault warning for the charging pile based on the fault warning analysis results. This application, through the fusion analysis of charging order data and real-time environmental data, can not only proactively trigger preventative maintenance based on real-time environmental data, effectively reducing the incidence of environment-induced faults, but also monitor the operating status of the charging pile in real time, and provide early warnings for the combined risks of charging order data and real-time environmental data that have not yet reached a single threshold but show a trend of coordinated deterioration. This achieves an intelligent maintenance transformation from "passive emergency repair" to "proactive early warning," thereby significantly improving maintenance efficiency and reducing operating costs.

[0033] Referring to Figure 1, Figure 1 is a schematic flowchart of the main steps of a charging pile fault early warning method according to an embodiment of this application. As shown in Figure 1, the charging pile fault early warning method in this embodiment mainly includes the following steps S101-S103.

[0034] Step S101: Obtain the charging order data of the charging pile and the real-time environmental data corresponding to the charging order data.

[0035] In this embodiment, the charging order data of the charging pile is a comprehensive dataset that may include charging pile information, charging data, and historically reported detailed data. After obtaining the charging order data, the real-time environmental data of the charging pile is obtained by calling a preset environmental data interface based on the charging pile information contained in the charging order data.

[0036] Step S102: Based on the charging order data and the real-time environmental data, obtain the fault early warning analysis results of the charging pile.

[0037] In this embodiment, based on charging order data and real-time environmental data, the fault warning analysis results of charging piles are obtained through multi-dimensional data fusion analysis.

[0038] Step S103: Based on the fault warning analysis results, issue a fault warning for the charging pile.

[0039] Based on the above steps S101-S103, this application, through the fusion analysis of charging order data and real-time environmental data, can not only proactively trigger preventive maintenance based on real-time environmental data, effectively reducing the incidence of environment-induced failures, but also monitor the operating status of charging piles in real time, and provide early warnings for the combined risks of charging order data and real-time environmental data that have not yet reached a single threshold but show a trend of coordinated deterioration. This realizes the transformation of intelligent maintenance from "passive emergency repair" to "proactive early warning," thereby significantly improving maintenance efficiency and reducing operating costs.

[0040] The following sections will provide further explanation of steps S101-S103.

[0041] Referring to Figure 2, Figure 2 is a detailed flowchart of a fault warning method for a charging pile according to an embodiment of this application.

[0042] Regarding step S101, in one embodiment, obtaining the charging order data of the charging pile and the real-time environmental data corresponding to the charging order data includes: in response to the update of the charging order cache data of the charging pile, obtaining the charging order data of the charging pile, wherein the charging order data includes at least charging pile information, charging data, and historically reported detailed data; the charging data includes charging amount and charging duration; the historically reported detailed data includes current data, voltage data, and power data reported according to a preset period; and based on the charging pile information, obtaining the real-time environmental data of the charging pile; wherein the real-time environmental data includes at least ambient temperature data, ambient humidity data, and precipitation rate data.

[0043] Specifically, when a charging pile completes a charging transaction or undergoes a status change during charging, such as an update to the charging amount, charging duration, or historically reported detailed data (e.g., current, voltage, and power data reported every 100ms), the charging order cache data is updated. At this time, in response to the update event of the charging pile's charging order cache data, the charging order data of the charging pile is retrieved. The charging order data can include charging pile information, charging data, and historically reported detailed data. The charging pile information can include the charging pile's unique device identifier (SN code), installation location code, device model, and hardware configuration parameters, etc., and is used to locate and identify specific charging piles. The charging data includes the charging amount and charging duration for this charge, and may also include the charging start and end times. Historically reported detailed data represents the charging pile's process status, including operating parameters collected and reported at preset intervals (e.g., every 5 seconds, 10 seconds, or 30 seconds) during the charging process, specifically current data, voltage data, and power data.

[0044] After obtaining the charging order data, the system further obtains real-time environmental data of the charging pile's location based on the charging pile information contained therein, such as the installation location code, through a preset environmental data interface. The real-time environmental data may include ambient temperature data, ambient humidity data, and precipitation rate data. In some scenarios, it may also include environmental parameters that may affect the operation of the charging pile, such as wind speed, light intensity, and atmospheric pressure.

[0045] Regarding step S102, in one embodiment, obtaining the fault warning analysis result of the charging pile based on the charging order data and the real-time environmental data includes: inputting the charging order data, the real-time environmental data, and the charging pile operation data corresponding to the charging order data into a preset fault judgment engine; the fault judgment engine matches the combination of input data with a preset risk combination database; wherein, the preset risk combination database stores multiple high-risk parameter combinations, each high-risk parameter combination including at least two high-risk combinations of parameters from the real-time environmental data, charging order data, and charging pile operation data; if the match is successful, the fault judgment engine outputs the fault warning analysis result of the charging pile, the fault warning analysis result including at least the risk level and fault type of the charging pile.

[0046] Specifically, the charging pile operation data corresponding to the charging order data is obtained based on the charging pile information in the charging order data. The charging pile operation data refers to the charging pile temperature data. Then, the charging order data, real-time environmental data, and the charging pile operation data corresponding to the charging order data are input into the preset fault judgment engine. The charging order data includes current data, voltage data, and power data from historically reported detailed data. The real-time environmental data includes ambient temperature data, ambient humidity data, and precipitation rate data.

[0047] The pre-set risk combination database stores multiple high-risk parameter combinations determined through analysis of historical faults. Each high-risk parameter combination covers high-risk combinations of at least two parameters from real-time environmental data, charging order data, and charging pile operation data. For example, in historical fault analysis, when the high-risk parameter combination of "ambient temperature ≥ 35℃ + charging current ≥ 80% of rated current + charging pile temperature ≥ 60℃" occurs, the probability of "charging pile fault" within the following 10 minutes is ≥ 90%. Therefore, the high-risk parameter combination is configured as "ambient temperature data higher than 35℃, current data greater than or equal to 80% of rated current, and charging pile temperature greater than or equal to 60℃". This combination corresponds to the fault type "charging pile fault" and the risk level "high".

[0048] The fault diagnosis engine matches the input data combination with each high-risk parameter combination in the preset risk combination database one by one. The matching logic can use fuzzy matching or exact matching. For numerical parameters, it determines whether they fall within the numerical range of the corresponding high-risk parameter combination. If the input data combination successfully matches a high-risk parameter combination, the fault diagnosis engine outputs a fault warning analysis result that includes the charging pile's risk level (e.g., low, medium, high, emergency) and fault type (e.g., charging pile fault, communication module fault, insulation fault, payment system fault, etc.) based on the fault information corresponding to the high-risk parameter combination. If no high-risk parameter combination is matched, the current charging pile is determined to be operating normally, and no fault warning analysis result is output.

[0049] This application combines multi-dimensional charging order data, real-time environmental data, and charging pile operation data for judgment, effectively overcoming the problems of false alarms, missed alarms, and difficulty in fault location caused by traditional fault judgment relying on a single data source. It not only improves the accuracy and interpretability of fault identification, but also combines environmental factors such as weather to predict the combined risks formed by the synergistic deterioration of multiple parameters in advance, and achieves accurate hierarchical early warning, significantly enhancing the initiative and intelligence level of operation and maintenance.

[0050] In one embodiment, the method further includes: determining preset characteristic indicators based on the charging order data and the charging pile operation data corresponding to the charging order data; wherein the preset characteristic indicators include at least the charging volume growth rate, current fluctuation coefficient, voltage stability, and charging pile temperature; inputting the preset characteristic indicators into a preset fault judgment engine, wherein the fault judgment engine compares the preset characteristic indicators with preset fault thresholds; if at least one preset characteristic indicator exceeds the corresponding preset fault threshold, the fault judgment engine outputs a fault warning analysis result for the charging pile, wherein the fault warning analysis result includes at least the risk level and fault type of the charging pile.

[0051] Specifically, based on the charging data in the charging order data and the detailed data reported in history, the charging growth rate, current fluctuation coefficient and voltage stability are calculated by a preset algorithm as preset characteristic indicators, while the charging pile temperature is determined according to the charging pile operation data. After the preset characteristic indicators are input into the fault judgment engine, the fault judgment engine will compare each indicator with the corresponding preset fault threshold.

[0052] The preset fault threshold is a critical value set based on historical fault data and equipment safety operation parameters. For example, when the charging growth rate is ≤0 and maintained for a preset time (not in user pause state), and the current fluctuation coefficient is >0.8, it is judged as "charging pile fault"; or, when the charging pile temperature is ≥65℃ and maintained for a preset time, and the fan operation status is "abnormal", it is judged as "heat dissipation system fault".

[0053] If at least one preset characteristic indicator exceeds the corresponding preset fault threshold, the fault judgment engine determines that the charging pile has a fault risk, and outputs a fault warning analysis result containing the risk level and fault type according to the type and degree of exceeding the preset fault threshold.

[0054] By extracting preset feature indicators from charging order data and charging pile operation data, and comparing each preset feature indicator with the corresponding preset fault threshold in real time, once a fault exceeding the preset fault threshold is identified, a fault warning analysis result containing the risk level and specific fault type is output, enabling timely response and accurate location of faults.

[0055] In one embodiment, the method further includes: acquiring real-time environmental data of the location of the charging pile; determining whether the real-time environmental data of the location of the charging pile exceeds a preset extreme environmental threshold; if it exceeds the threshold, generating a fault warning analysis result for the charging pile, wherein the fault warning analysis result includes at least the risk level and fault type of the charging pile.

[0056] Specifically, real-time environmental data refers to the environmental parameters at the charging pile installation location, including at least ambient temperature, ambient humidity, and precipitation rate data. Preset extreme environmental thresholds are environmental safety thresholds set based on the charging pile equipment's tolerance capabilities and historical environmentally induced fault data. For example, extreme ambient temperature thresholds can be set to -20℃ (lower limit) and 50℃ (upper limit), extreme ambient humidity thresholds can be set to 90%RH (upper limit), and extreme precipitation rate thresholds can be set to 80% (indicating heavy rain or above).

[0057] When any parameter in the acquired real-time environmental data exceeds the corresponding preset extreme environmental threshold, for example, when the ambient temperature reaches 55℃ (exceeding the upper limit of 50℃), or the ambient humidity reaches 95%RH (exceeding the upper limit of 95%RH), or the precipitation rate reaches 90% (exceeding the upper limit of 80%), a fault warning analysis result containing risk level and fault type will be generated.

[0058] The risk level of the fault warning analysis results can be set according to the degree of exceedance, such as "low risk" for slight exceedance (50℃ < temperature ≤ 55℃) and "high risk" for severe exceedance (temperature > 55℃); the fault type can be set as "high temperature charging risk", "insulation degradation risk in high humidity environment" or "short circuit risk of equipment in heavy rainfall", etc.

[0059] By comparing real-time environmental data with extreme environmental thresholds, the fault early warning analysis results are determined. This not only effectively solves the problem of false alarms and missed alarms caused by the traditional early warning mechanism ignoring environmental factors such as temperature and humidity, but also proactively triggers preventive operation and maintenance measures before extreme weather arrives. This significantly reduces the incidence of faults caused by extreme weather, such as insulation failure caused by low temperature and overheating of charging piles caused by high temperature, thereby greatly improving the operational stability and proactive operation and maintenance of charging piles.

[0060] In one embodiment, the step of issuing a fault warning for the charging pile based on the fault warning analysis results includes: determining a warning level based on the fault type and risk level of the fault warning analysis results; generating graded warning information for the charging pile based on the fault warning analysis results, wherein the graded warning information includes at least the fault type, risk level, and recommended handling solution; and selecting a corresponding warning push method to push the graded warning information based on the warning level.

[0061] Specifically, based on the fault type and risk level determined in the fault warning analysis results, the warning level is determined according to the preset warning level classification rules. For example, when the fault type is "charging pile fault" and the risk level is "urgent", the warning level corresponds to Level 1 warning (urgent); when the fault type is "heat dissipation system fault" and the risk level is "high", the warning level corresponds to Level 2 warning (important); when the fault type is "risk of insulation degradation in high humidity environment" and the risk level is "low", the warning level corresponds to Level 3 warning (advance warning).

[0062] After determining the warning level, a graded warning information is generated based on the fault warning analysis results. This information includes the specific fault type, risk level, and recommended handling solutions for the fault. Recommended handling solutions include "immediately stop the operation of the charging pile and arrange for technicians to rush to the site with a spare module for replacement" or "increase the frequency of equipment inspections and check the module temperature every 2 hours".

[0063] Then, select the corresponding warning push method according to the warning level. For example, a level 1 warning can be triggered by sound and light alarms on the operation and maintenance management platform, and by sending tiered warning information to the operation and maintenance personnel's mobile phones via SMS, operation and maintenance APP, etc., to ensure that relevant personnel receive the fault information as soon as possible; a level 2 warning can be sent by message notification on the operation and maintenance management platform and by the operation and maintenance personnel's operation and maintenance APP; a level 3 warning can be sent by message notification only through the operation and maintenance management platform.

[0064] By generating tiered early warning information and using multi-channel, differentiated push methods, it is possible to ensure that tiered early warning information of different severity levels is accurately conveyed. This allows maintenance personnel to quickly take corresponding countermeasures based on the priority of the early warning information and the recommended handling plan, further improving the timeliness and effectiveness of fault handling.

[0065] In one embodiment, after the graded warning information is pushed, an operation and maintenance work order is generated based on the graded warning information and the operation and maintenance management platform. The operation and maintenance work order may include the graded warning information and charging pile information, such as the unique device identifier (SN code) and installation location code of the charging pile, as well as the installation time and historical fault records of the charging pile. The operation and maintenance work order is then sent to the relevant operation and maintenance personnel through the operation and maintenance APP. The operation and maintenance personnel receive the operation and maintenance work order and update the processing status (such as "order accepted", "processing" or "completed").

[0066] In one embodiment, the method further includes: collecting fault handling result data, wherein the fault handling result data is associated with corresponding graded early warning information; and dynamically adjusting at least one of the preset fault threshold, the preset extreme environment threshold, and the preset risk combination database based on the fault handling result data.

[0067] Specifically, after the charging pile completes the fault warning and handling process, fault handling result data is collected, and this data is correlated with the corresponding graded warning information. The fault handling result data may include: the actual occurrence of the fault (e.g., whether a fault actually occurred after the warning, whether the fault escalated or was mitigated), the actual fault causes investigated on-site by maintenance personnel (which may be consistent with or different from the fault type analyzed in the warning), and the specific handling measures taken (e.g., replacing modules, cleaning heat dissipation channels, adjusting operating parameters, etc.).

[0068] Based on fault handling results data, at least one of the preset fault thresholds, preset extreme environment thresholds, and preset risk combination databases is dynamically adjusted and optimized. For example, if historical data shows that a certain preset fault threshold (e.g., charging pile temperature ≥65℃) triggers warnings multiple times in actual operation, but on-site investigation finds that the charging pile can still operate stably at this temperature without any faults, or the fault handling results show that the actual fault incidence rate corresponding to this threshold is much lower than expected, then it can be determined that the original preset fault threshold setting is unreasonable and there is an over-warning situation. In this case, the threshold needs to be appropriately increased (e.g., adjusted to ≥70℃) based on the actual temperature in the fault handling results.

[0069] Through a dynamic adjustment mechanism based on actual fault handling results, the preset thresholds and risk combination database can continuously align with the actual operating status and fault occurrence patterns of charging piles, thereby continuously optimizing the accuracy and adaptability of faults and avoiding false alarms and missed alarms caused by fixed thresholds or outdated risk combinations. This forms a closed-loop management system of "early warning-processing-feedback-optimization," continuously improving the intelligence level and practicality of fault early warning.

[0070] In one implementation, dynamically adjusting at least one of the preset fault threshold, the preset extreme environment threshold, and the preset risk combination database based on the fault handling result data includes: calculating the early warning accuracy rate corresponding to each fault type based on the fault handling result data; and adjusting the corresponding preset fault threshold, preset extreme environment threshold, or high-risk parameter combination in the preset risk combination database based on the early warning accuracy rate corresponding to each fault type.

[0071] Specifically, based on fault handling result data, the early warning accuracy rate corresponding to each fault type is calculated. The calculation method for early warning accuracy rate can be as follows: for a specific fault type, the ratio of the number of early warnings for that fault type to the actual number of faults occurring within a preset time period is calculated. For example, if there were 50 early warnings for "charging pile faults" in the past 30 days, and 40 of those "charging pile faults" actually occurred, then the early warning accuracy rate for that fault type is 80%. If the early warning accuracy rate is lower than a preset accuracy threshold (e.g., 85%), it indicates that the relevant parameters currently used to determine that fault type may be unreasonable and need to be adjusted. Through a dynamic adjustment strategy based on early warning accuracy rate, the judgment conditions affecting the early warning effect can be accurately located and optimized, making fault judgment more accurate and reliable in identifying various fault types.

[0072] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of this application, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these variations are all within the scope of protection of this application.

[0073] Those skilled in the art will understand that all or part of the processes in the method of the above-described embodiment can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0074] Furthermore, this application also provides an electronic device. In one embodiment of the electronic device according to this application, the electronic device includes a processor and a memory. The memory can be configured to store a program for executing the fault warning method for a charging pile according to the above-described method embodiments. The processor can be configured to execute the program in the memory, which includes, but is not limited to, the program for executing the fault warning method for a charging pile according to the above-described method embodiments. For ease of explanation, only the parts related to the embodiments of this application are shown. For specific technical details not disclosed, please refer to the method section of the embodiments of this application. The electronic device can be an electronic device formed by various electronic devices. Referring to Figure 3, Figure 3 exemplarily shows that the memory 11 and the processor 12 are connected via a bus communication connection.

[0075] Furthermore, this application also provides a computer-readable storage medium. In one embodiment of the computer-readable storage medium according to this application, the computer-readable storage medium can be configured to store a program that executes the fault warning method for a charging pile according to the above-described method embodiments. This program can be loaded and run by a processor to implement the fault warning method for the charging pile. For ease of explanation, only the parts related to the embodiments of this application are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of this application. The computer-readable storage medium can be a memory device formed by various electronic devices. Optionally, in the embodiments of this application, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0076] Furthermore, it should be understood that since the various modules are only provided to illustrate the functional units of the device described in this application, the physical devices corresponding to these modules may be the processor itself, or a part of the processor's software, hardware, or a combination of both. Therefore, the number of modules shown in the figures is merely illustrative.

[0077] Those skilled in the art will understand that the various modules in the device can be adaptively split or combined. Such splitting or combining of specific modules will not cause the technical solution to deviate from the principles of this application; therefore, the technical solutions after splitting or combining will fall within the protection scope of this application.

[0078] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. A fault early warning method for charging piles, characterized in that, The method includes: acquiring charging order data of the charging pile and real-time environmental data corresponding to the charging order data; acquiring fault warning analysis results of the charging pile based on the charging order data and the real-time environmental data; and issuing fault warnings for the charging pile based on the fault warning analysis results.

2. The fault early warning method for charging piles according to claim 1, characterized in that, The step of obtaining the fault warning analysis result of the charging pile based on the charging order data and the real-time environmental data includes: inputting the charging order data, the real-time environmental data, and the charging pile operation data corresponding to the charging order data into a preset fault judgment engine; the fault judgment engine matches the combination of input data with a preset risk combination database; wherein, the preset risk combination database stores multiple high-risk parameter combinations, each high-risk parameter combination including at least two high-risk combinations of parameters from the real-time environmental data, charging order data, and charging pile operation data; if the match is successful, the fault judgment engine outputs the fault warning analysis result of the charging pile, the fault warning analysis result including at least the risk level and fault type of the charging pile.

3. The fault early warning method for charging piles according to claim 1, characterized in that, The method further includes: determining preset characteristic indicators based on the charging order data and the charging pile operation data corresponding to the charging order data; wherein the preset characteristic indicators include at least the charging volume growth rate, current fluctuation coefficient, voltage stability, and charging pile temperature; inputting the preset characteristic indicators into a preset fault judgment engine, wherein the fault judgment engine compares the preset characteristic indicators with preset fault thresholds; if at least one preset characteristic indicator exceeds the corresponding preset fault threshold, the fault judgment engine outputs the fault warning analysis result of the charging pile, wherein the fault warning analysis result includes at least the risk level and fault type of the charging pile.

4. The fault early warning method for charging piles according to claim 1, characterized in that, The method further includes: acquiring real-time environmental data of the location of the charging pile; determining whether the real-time environmental data of the location of the charging pile exceeds a preset extreme environmental threshold; if it exceeds the threshold, generating a fault warning analysis result for the charging pile, wherein the fault warning analysis result includes at least the risk level and fault type of the charging pile.

5. The fault early warning method for charging piles according to any one of claims 1 to 4, characterized in that, The step of issuing a fault warning for the charging pile based on the fault warning analysis results includes: determining the warning level based on the fault type and risk level of the fault warning analysis results; generating graded warning information for the charging pile based on the fault warning analysis results, wherein the graded warning information includes at least the fault type, risk level, and recommended handling solution; and selecting a corresponding warning push method to push the graded warning information based on the warning level.

6. The fault early warning method for charging piles according to claim 5, characterized in that, The method further includes: collecting fault handling result data, wherein the fault handling result data is associated with corresponding graded early warning information; and dynamically adjusting at least one of the preset fault threshold, the preset extreme environment threshold, and the preset risk combination database based on the fault handling result data.

7. The fault early warning method for charging piles according to claim 6, characterized in that, The step of dynamically adjusting at least one of the preset fault threshold, the preset extreme environment threshold, and the preset risk combination database based on the fault handling result data includes: calculating the early warning accuracy rate corresponding to each fault type based on the fault handling result data; and adjusting the corresponding preset fault threshold, preset extreme environment threshold, or high-risk parameter combination in the preset risk combination database based on the early warning accuracy rate corresponding to each fault type.

8. The fault early warning method for charging piles according to claim 1, characterized in that, The step of acquiring charging order data of the charging pile and the corresponding real-time environmental data includes: acquiring charging order data of the charging pile in response to an update of the charging order cache data of the charging pile, wherein the charging order data includes at least charging pile information, charging data, and historically reported detailed data; the charging data includes charging amount and charging duration; the historically reported detailed data includes current data, voltage data, and power data reported according to a preset period; and acquiring real-time environmental data of the charging pile based on the charging pile information; wherein the real-time environmental data includes at least ambient temperature data, ambient humidity data, and precipitation rate data.

9. An electronic device comprising a processor and a memory, the memory being adapted to store a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by the processor to perform the fault warning method for the charging pile according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by a processor to perform the fault warning method for the charging pile as described in any one of claims 1 to 8.

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