Energy complementing equipment fault diagnosis method, intelligent equipment and storage medium

Abstract

The invention relates to the technical field of charging and battery swap, in particular to an energy complementing equipment fault diagnosis method, intelligent equipment and a storage medium, and aims to solve the technical problem of how to realize more efficient and reliable fault diagnosis of energy complementing equipment. In order to achieve the purpose, the ultrahigh-frequency physical signal generated by the logic controller in the battery charging and replacing process is obtained, fault diagnosis is carried out on the energy supplementing equipment based on the ultrahigh-frequency physical signal and by applying the trained time sequence anomaly detection model, safer, more convenient and more efficient diagnosis can be achieved, manual participation is not needed, and the labor cost is effectively saved. And the data does not need to be uploaded to the cloud and diagnosed by the cloud, so that the risk of data privacy leakage is avoided, and the problem of delay in the cloud diagnosis process is solved. As the energy complementing equipment can be diagnosed only by collecting the ultrahigh-frequency physical signal generated by the logic controller of the energy complementing equipment, the charging and replacing process of a user does not need to be interrupted in the diagnosis process, and the service quality can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of charging and battery replacement technology, and specifically to a method for diagnosing faults of energy replenishment equipment, an intelligent device and a storage medium. Background Art

[0002] The efficient operation of battery swap stations and charging piles is an important part of the electric vehicle industry chain. However, equipment failure, unstable operation or service interruption will directly affect the quality of charging and swapping services, resulting in reduced customer satisfaction and even economic losses. Therefore, timely and accurate diagnosis and prevention of charging equipment failures are the key to ensuring their normal operation and improving overall operational efficiency. In addition, with the iteration and upgrade of charging and swapping technology, equipment monitoring and fault diagnosis have become more complex, and the huge amount of real-time data stored in the cloud is more likely to cause remote service jams and increase the risk of delays.

[0003] Currently, conventional fault diagnosis for energy replenishment equipment includes charging module diagnosis and mechanical wear diagnosis, specifically, damage to the charging module resulting in no charging current, and damage to the battery lift resulting in the inability to transport the battery to the battery swap warehouse to complete the battery swap. However, for the above types of faults, if you rely on cloud-based fault diagnosis, you often need to rely on a strong and stable network connection; at the same time, data collection, transmission, processing, and feedback will all produce certain delays, which may affect the rapid response to the fault. In terms of service experience, there are currently specialists on duty at the charging and swapping sites, and they will go there immediately to deal with any faults that occur. However, the fault will interrupt the user's charging and swapping process, causing the user to be stuck at the station, reducing the quality of service.

[0004] Accordingly, the art needs a new energy charging equipment fault diagnosis solution to solve the above problems. Summary of the invention

[0005] In order to overcome the above-mentioned defects, the present application is proposed to solve or at least partially solve the technical problem of how to achieve more efficient and reliable fault diagnosis of energy charging equipment.

[0006] In a first aspect, a method for diagnosing a fault of an energy charging device is provided, wherein the energy charging device includes a logic controller, and the logic controller is used to control a charging and replacing process of the energy charging device; the method includes:

[0007] Acquire the ultra-high frequency physical signal generated by the logic controller during the charging and swapping process;

[0008] Based on the ultra-high frequency physical signal, a trained timing anomaly detection model is applied to perform fault diagnosis on the energy replenishment device.

[0009] In a technical solution of the above-mentioned energy charging equipment fault diagnosis method, the obtaining of the ultra-high frequency physical signal generated by the logic controller in controlling the charging and swapping process includes:

[0010] Obtaining a control signal generated by the logic controller during the charging and battery replacement process;

[0011] Based on a preset frequency, sampling the control signal to obtain the ultra-high frequency physical signal;

[0012] Wherein, the preset frequency is greater than 10 Hz.

[0013] In a technical solution of the above-mentioned energy charging equipment fault diagnosis method, the charging and battery replacement process includes a battery replacement process, the energy charging equipment includes a battery lifter of a battery replacement station; the ultra-high frequency physical signal includes an ultra-high frequency torque physical signal; the ultra-high frequency torque physical signal includes a first ultra-high frequency torque physical signal and a second ultra-high frequency torque physical signal;

[0014] The obtaining of the ultra-high frequency physical signal generated by the logic controller in the process of controlling the charging and swapping includes:

[0015] Acquire a first ultra-high frequency torque physical signal generated by the logic controller when the battery lift is in an unloaded state;

[0016] Acquire a second ultra-high frequency torque physical signal generated by the logic controller when the battery lift is in a loaded state.

[0017] In a technical solution of the above energy charging equipment fault diagnosis method, the energy charging equipment fault diagnosis is performed based on the ultra-high frequency physical signal and using a trained timing anomaly detection model, including:

[0018] Based on the first ultra-high frequency torque physical signal and the second ultra-high frequency torque physical signal, the trained timing anomaly detection model is applied to perform fault diagnosis on the battery lift.

[0019] In a technical solution of the above-mentioned energy charging equipment fault diagnosis method, the method further includes:

[0020] When it is detected that there is no battery replacement order at the battery replacement station, a lift detection instruction is sent to the logic controller, so that the logic controller controls the battery lift to perform self-check to obtain the first ultra-high frequency torque physical signal and the second ultra-high frequency torque physical signal.

[0021] In a technical solution of the above-mentioned energy charging equipment fault diagnosis method, the charging and swapping process includes a charging process, the energy charging equipment includes a charging module of a battery swap station; the ultra-high frequency physical signal includes an ultra-high frequency current physical signal;

[0022] The obtaining of the ultra-high frequency physical signal generated by the logic controller in the process of controlling the charging and swapping includes:

[0023] Acquire the ultra-high frequency current physical signal generated by the logic controller when the charging module is charging the battery.

[0024] In a technical solution of the above-mentioned energy charging equipment fault diagnosis method, the obtaining of the ultra-high frequency current physical signal generated by the logic controller during the process of the charging module charging the battery includes:

[0025] When it is detected that there is no battery replacement order at the battery replacement station or there are fully charged batteries at the battery replacement station, a charging module detection instruction is sent to the logic controller of the charging module in the not fully charged position to obtain the ultra-high frequency current physical signal generated by the logic controller during the charging module charging the battery.

[0026] In a technical solution of the above-mentioned energy charging equipment fault diagnosis method, the charging and replacing process includes a charging process, the energy charging equipment includes a charging module of a charging pile; the ultra-high frequency physical signal includes an ultra-high frequency current physical signal;

[0027] The obtaining of the ultra-high frequency physical signal generated by the logic controller in the process of controlling the charging and swapping includes:

[0028] When the user places a charging order and starts charging the device to be charged, the ultra-high frequency current physical signal generated by the logic controller during the process of the charging module charging the device to be charged is obtained.

[0029] In a technical solution of the above energy charging equipment fault diagnosis method, the energy charging equipment fault diagnosis is performed based on the ultra-high frequency physical signal and using a trained timing anomaly detection model, including:

[0030] Based on the ultra-high frequency current physical signal, a trained timing anomaly detection model is applied to perform fault diagnosis on the charging module.

[0031] In a technical solution of the above energy charging equipment fault diagnosis method, the application of the trained time series anomaly detection model to perform fault diagnosis on the energy charging equipment includes:

[0032] Using the trained time series anomaly detection model, diagnose the fault of the energy replenishment equipment to obtain a diagnosis result;

[0033] When the diagnosis result is abnormal, an alarm is issued to prompt troubleshooting.

[0034] In a technical solution of the above-mentioned energy charging equipment fault diagnosis method, the method further includes:

[0035] Using the ultra-high frequency physical signal samples generated by the logic controller during the charging and swapping process, the timing anomaly detection model is trained to obtain the trained timing anomaly detection model;

[0036] Wherein, the ultra-high frequency physical signal sample includes a diagnosis result label; the diagnosis result label is a normal label or an abnormal label.

[0037] In a second aspect, a smart device is provided, comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in any one of the technical solutions of the above-mentioned method for diagnosing faults of energy charging equipment is implemented.

[0038] In a third aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, wherein the program codes are suitable for being loaded and run by a processor to execute the method described in any one of the technical solutions of the above-mentioned energy charging equipment fault diagnosis method.

[0039] Solution 1. A method for diagnosing faults of an energy charging device, characterized in that the energy charging device includes a logic controller, and the logic controller is used to control the charging and replacing process of the energy charging device; the method includes:

[0040] Acquire the ultra-high frequency physical signal generated by the logic controller during the charging and swapping process;

[0041] Based on the ultra-high frequency physical signal, a trained timing anomaly detection model is applied to perform fault diagnosis on the energy replenishment device.

[0042] Solution 2. The energy replenishment equipment fault diagnosis method according to Solution 1 is characterized in that:

[0043] The obtaining of the ultra-high frequency physical signal generated by the logic controller in the process of controlling the charging and swapping includes:

[0044] Obtaining a control signal generated by the logic controller during the charging and battery replacement process;

[0045] Based on a preset frequency, sampling the control signal to obtain the ultra-high frequency physical signal;

[0046] Wherein, the preset frequency is greater than 10 Hz.

[0047] Solution 3. The energy replenishment equipment fault diagnosis method according to Solution 1 is characterized in that:

[0048] The charging and battery swapping process includes a battery swapping process, the energy replenishment equipment includes a battery lifter of a battery swapping station; the ultra-high frequency physical signal includes an ultra-high frequency torque physical signal; the ultra-high frequency torque physical signal includes a first ultra-high frequency torque physical signal and a second ultra-high frequency torque physical signal;

[0049] The obtaining of the ultra-high frequency physical signal generated by the logic controller in the process of controlling the charging and swapping includes:

[0050] Acquire a first ultra-high frequency torque physical signal generated by the logic controller when the battery lift is in an unloaded state;

[0051] Acquire a second ultra-high frequency torque physical signal generated by the logic controller when the battery lift is in a loaded state.

[0052] Solution 4. The energy replenishment equipment fault diagnosis method according to Solution 3 is characterized in that:

[0053] The method of applying a trained timing anomaly detection model based on the ultra-high frequency physical signal to perform fault diagnosis on the energy replenishment device includes:

[0054] Based on the first ultra-high frequency torque physical signal and the second ultra-high frequency torque physical signal, the trained timing anomaly detection model is applied to perform fault diagnosis on the battery lift.

[0055] Solution 5. The energy charging equipment fault diagnosis method according to Solution 3 is characterized in that the method further comprises:

[0056] When it is detected that there is no battery replacement order at the battery replacement station, a lift detection instruction is sent to the logic controller, so that the logic controller controls the battery lift to perform self-check to obtain the first ultra-high frequency torque physical signal and the second ultra-high frequency torque physical signal.

[0057] Solution 6. The energy replenishment equipment fault diagnosis method according to Solution 1 is characterized in that:

[0058] The charging and battery swapping process includes a charging process, the energy replenishment equipment includes a charging module of a battery swapping station; the ultra-high frequency physical signal includes an ultra-high frequency current physical signal;

[0059] The obtaining of the ultra-high frequency physical signal generated by the logic controller in the process of controlling the charging and swapping includes:

[0060] Acquire the ultra-high frequency current physical signal generated by the logic controller when the charging module is charging the battery.

[0061] Solution 7. The energy replenishment equipment fault diagnosis method according to Solution 6 is characterized in that:

[0062] The obtaining of the ultra-high frequency current physical signal generated by the logic controller during the process of the charging module charging the battery comprises:

[0063] When it is detected that there is no battery replacement order at the battery replacement station or there are fully charged batteries at the battery replacement station, a charging module detection instruction is sent to the logic controller of the charging module in the not fully charged position to obtain the ultra-high frequency current physical signal generated by the logic controller during the charging module charging the battery.

[0064] Solution 8. The energy replenishment equipment fault diagnosis method according to Solution 1 is characterized in that:

[0065] The charging and replacing process includes a charging process, the energy replenishment device includes a charging module of a charging pile; the ultra-high frequency physical signal includes an ultra-high frequency current physical signal;

[0066] The obtaining of the ultra-high frequency physical signal generated by the logic controller in the process of controlling the charging and swapping includes:

[0067] When the user places a charging order and starts charging the device to be charged, the ultra-high frequency current physical signal generated by the logic controller during the process of the charging module charging the device to be charged is obtained.

[0068] Solution 9. The energy replenishment equipment fault diagnosis method according to Solution 6 or 8 is characterized in that:

[0069] The method of applying a trained timing anomaly detection model based on the ultra-high frequency physical signal to perform fault diagnosis on the energy replenishment device includes:

[0070] Based on the ultra-high frequency current physical signal, a trained timing anomaly detection model is applied to perform fault diagnosis on the charging module.

[0071] Solution 10. The energy charging equipment fault diagnosis method according to any one of solutions 1 to 8 is characterized in that:

[0072] The application of the trained time series anomaly detection model to perform fault diagnosis on the energy replenishment device includes:

[0073] Using the trained time series anomaly detection model, diagnose the fault of the energy replenishment equipment to obtain a diagnosis result;

[0074] When the diagnosis result is abnormal, an alarm is issued to prompt troubleshooting.

[0075] Solution 11. The energy charging equipment fault diagnosis method according to any one of solutions 1 to 8, characterized in that the method further comprises:

[0076] Using the ultra-high frequency physical signal samples generated by the logic controller during the charging and swapping process, the timing anomaly detection model is trained to obtain the trained timing anomaly detection model;

[0077] Wherein, the ultra-high frequency physical signal sample includes a diagnosis result label; the diagnosis result label is a normal label or an abnormal label.

[0078] Solution 12. A smart device, comprising:

[0079] at least one processor;

[0080] and, a memory communicatively coupled to the at least one processor;

[0081] Wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the energy charging equipment fault diagnosis method described in any one of Schemes 1 to 11 is implemented.

[0082] Solution 13. A computer-readable storage medium storing a plurality of program codes, wherein the program codes are suitable for being loaded and run by a processor to execute the energy charging equipment fault diagnosis method described in any one of Solutions 1 to 11.

[0083] The above one or more technical solutions of the present application have at least one or more of the following beneficial effects:

[0084] In implementing the technical solution of the energy replenishment equipment fault diagnosis method provided by the present application, the present application obtains the ultra-high frequency physical signal generated by the logic controller during the charging and swapping process, and based on the ultra-high frequency physical signal, applies the trained timing anomaly detection model to perform fault diagnosis on the energy replenishment equipment. Through the above configuration method, the present application can realize safe, convenient and efficient diagnosis of energy replenishment equipment without human participation, effectively saving labor costs. At the same time, there is no need to upload data to the cloud for diagnosis by the cloud, which avoids the risk of data privacy leakage and effectively solves the delay problem of the cloud diagnosis process. At the same time, since the present application only needs to collect the ultra-high frequency physical signal generated by the logic controller of the energy replenishment equipment, it can realize the diagnosis of the energy replenishment equipment, and the diagnosis process does not need to interrupt the user's charging and swapping process, which can effectively improve the service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] The disclosure of the present application will become easier to understand with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present application. Among them:

[0086] Figure 1 It is a flowchart of the main steps of a method for diagnosing a fault of an energy charging device according to an embodiment of the present application;

[0087] Figure 2 This is a schematic diagram of the battery lift at the battery swap station;

[0088] Figure 3 It is a schematic diagram of the charging module;

[0089] Figure 4 This is the engineering schematic diagram of the battery compartment of the battery swap station;

[0090] Figure 5 It is the engineering schematic diagram of the charging pile group;

[0091] Figure 6 This is a schematic diagram of the main system architecture of the battery swap station;

[0092] Figure 7 This is a schematic diagram of the main system architecture of the charging pile;

[0093] Figure 8 It is a flowchart of the main steps of a battery lift fault diagnosis process of a battery swap station according to an implementation of an embodiment of the present application;

[0094] Fig. 9 It is a flowchart of the main steps of a fault diagnosis process of a charging module of a battery swap station according to an implementation of an embodiment of the present application;

[0095] Fig.10 It is a flowchart of the main steps of a fault diagnosis process of a charging pile according to an implementation method of an embodiment of the present application;

[0096] Fig.11 It is a schematic diagram of the main structure of a smart device according to an embodiment of the present application.

[0097] Reference numerals:

[0098] 11: memory; 12: processor. DETAILED DESCRIPTION

[0099] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0100] In the description of the present application, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, memory, and may also include software parts, such as program code, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware or a combination of the two. Computer-readable storage media include any suitable medium that can store program code, such as a disk, a hard disk, an optical disk, a flash memory, a read-only memory, a 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 term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B" and may include only A, only B or A and B. The singular terms "one" and "the" may also include plural forms.

[0101] The relevant user personal information that may be involved in the various embodiments of this application is strictly in accordance with the requirements of laws and regulations, following the principles of legality, legitimacy and necessity, based on the reasonable purposes of business scenarios, to process the personal information that users actively provide during the use of products / services or generated due to the use of products / services, as well as the personal information obtained with the user's authorization.

[0102] The user personal information processed by this application will vary depending on the specific product / service scenario, and shall be based on the specific scenario in which the user uses the product / service, and may involve the user's account information, device information, driving information, vehicle information or other related information. This application will treat the user's personal information and its processing with a high degree of diligence.

[0103] This application attaches great importance to the security of user personal information and has taken reasonable and feasible security protection measures that meet industry standards to protect user information and prevent personal information from being accessed, disclosed, used, modified, damaged or lost without authorization.

[0104] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart of the main steps of a method for diagnosing a fault of an energy-replenishing device according to an embodiment of the present application. Figure 1 As shown, the energy charging device in the embodiment of the present application includes a logic controller. The logic controller is used to control the charging and replacing process of the energy charging device. The energy charging device fault diagnosis method in the embodiment of the present application mainly includes the following steps S101 to S102.

[0105] Step S101: Acquire the ultra-high frequency physical signal generated by the logic controller during the charging and swapping process.

[0106] In this embodiment, the ultra-high frequency physical signal generated by the logic controller during the charging and swapping process can be obtained. The ultra-high frequency physical signal refers to a physical signal obtained by ultra-high frequency sampling of the control signal generated by the logic controller during the charging and swapping process. Ultra-high frequency sampling refers to sampling with a sampling frequency higher than a preset frequency. That is, the ultra-high frequency physical signal obtained by ultra-high frequency sampling can obtain more fine-grained features in the control signal of the logic controller, thereby facilitating the timing anomaly detection model to better diagnose faults of the energy replenishment equipment and identify abnormal diagnostic results in a timely and effective manner.

[0107] In one embodiment, the energy replenishment equipment may include a battery lifter at a battery swap station, a charging module at a battery swap station, a charging module at a charging pile, a charging gun, a battery in a battery swap station, etc. Figure 2 As shown, the charging module is Figure 3 As shown in the figure, the engineering schematic diagram of the battery compartment of the battery swap station is as follows Figure 4 As shown in the figure, the engineering schematic diagram of the charging pile group is as follows Figure 5 In one case, Figure 4 As shown, the battery compartment of the battery swap station may include 21 compartments and 1 battery lifter. The 21 compartments are supplied with current by 10 charging modules.

[0108] During the battery swapping process at the battery swap station, after the user places an order (battery swap order), the battery lift will move horizontally and lift to get a fully charged battery from the battery compartment and transport it to the conveyor belt, which will then transport the battery to the docking station to wait for it to be swapped. After the user has finished swapping batteries, the battery lift will move horizontally and lift to get the low-charged battery from the cache and transport it to the battery compartment for charging.

[0109] During the charging process at a battery swap station or charging pile, when the battery compartment receives a low-power battery, it will send a current request signal to the charging module, and then the charging module will supply the corresponding current for charging.

[0110] In one implementation, the control signal generated by the logic controller during the charging and swapping process can be obtained, and the control signal can be sampled based on a preset frequency to obtain an ultra-high frequency physical signal, where the preset frequency is greater than 10 Hz. The higher the preset frequency, the better the ultra-high frequency physical signal obtained by sampling can reflect the fine-grained characteristics of the control signal, thereby better detecting early abnormalities of the energy replenishment equipment.

[0111] In one implementation, the preset frequency may be 100 Hz.

[0112] In another embodiment, the preset frequency may be 1000 Hz.

[0113] In other implementations, the preset frequency may be a frequency value between 100 Hz and 1000 Hz.

[0114] In one implementation, the control signal may include a current signal, a torque signal, etc. The ultra-high frequency physical signal may include an ultra-high frequency current physical signal, an ultra-high frequency torque physical signal, etc.

[0115] Step S102: Based on the ultra-high frequency physical signal, the trained timing anomaly detection model is applied to perform fault diagnosis on the energy replenishment equipment.

[0116] In this embodiment, the ultra-high frequency physical signal can be input into a trained timing anomaly detection model to perform fault diagnosis on the energy charging equipment.

[0117] In one implementation, the diagnosis result of the time series anomaly detection model may include normal and abnormal. When the diagnosis result is abnormal, an alarm may be issued to remind relevant personnel to perform troubleshooting.

[0118] In one implementation, the time series anomaly detection model may be a model composed of a decision tree, a support vector machine, a random forest, a recurrent neural network RNN, a long short-term memory network LSTM, etc. The time series anomaly detection model can capture complex features in ultra-high frequency physical signals and improve the accuracy of fault diagnosis.

[0119] In one implementation, the trained time series anomaly detection model can be deployed on the local side of the energy charging device to implement fault diagnosis of the energy charging device.

[0120] Based on the method described in steps S101 to S102 above, the embodiment of the present application obtains the ultra-high frequency physical signal generated by the logic controller during the charging and swapping process, and based on the ultra-high frequency physical signal, applies the trained timing anomaly detection model to perform fault diagnosis on the energy replenishment equipment. Through the above configuration method, the embodiment of the present application can realize safe, convenient and efficient diagnosis of the energy replenishment equipment without human participation, which effectively saves labor costs. At the same time, there is no need to upload the data to the cloud for diagnosis by the cloud, which avoids the risk of data privacy leakage and effectively solves the delay problem of the cloud diagnosis process. At the same time, since the present application only needs to collect the ultra-high frequency physical signal generated by the logic controller of the energy replenishment equipment, it can realize the diagnosis of the energy replenishment equipment, and the diagnosis process does not need to interrupt the user's charging and swapping process, which can effectively improve the service quality.

[0121] Step S101 and step S102 are further described below.

[0122] In one implementation of the embodiment of the present application, the charging and battery swapping process may include a battery swapping process, the energy replenishment device may include a battery lifter of a battery swapping station; the ultra-high frequency physical signal may include an ultra-high frequency torque physical signal; the ultra-high frequency torque physical signal may include a first ultra-high frequency torque physical signal and a second ultra-high frequency torque physical signal; step S101 may further include the following steps S1011 and S1012:

[0123] Step S1011: Acquire the first ultra-high frequency torque physical signal generated by the logic controller when the battery lift is in an unloaded state.

[0124] Step S1012: Acquire a second ultra-high frequency torque physical signal generated by the logic controller when the battery lift is in a loaded state.

[0125] Step S102 may further include the following steps S1021:

[0126] Step S1021: Based on the first ultra-high frequency torque physical signal and the second ultra-high frequency torque physical signal, a trained timing anomaly detection model is applied to perform fault diagnosis on the battery lift.

[0127] In this embodiment, the battery lifter of the battery swap station is as follows: Figure 2 As shown. For the fault diagnosis of the battery lift of the battery swap station, the first ultra-high frequency torque physical signal when the battery lift is unloaded and the second ultra-high frequency torque physical signal when the battery lift is loaded can be obtained, and the first ultra-high frequency torque physical signal and the second ultra-high frequency torque physical signal are input into the trained timing anomaly detection model to perform fault diagnosis on the battery lift.

[0128] In one embodiment, when it is detected that there is no battery replacement order at the battery replacement station, a lift detection instruction is sent to the logic controller, so that the logic controller controls the battery lift to perform a self-check to obtain the first ultra-high frequency torque physical signal and the second ultra-high frequency torque physical signal. That is, when there is no battery replacement order at the battery replacement station, the battery lift can be diagnosed for a fault, so as to further reduce the impact of the fault diagnosis process on the user's battery replacement process.

[0129] In one embodiment, if the fault diagnosis result of the battery lift is normal, a lift detection instruction can be issued to the battery lift regularly to perform fault diagnosis on the battery lift regularly. If the diagnosis result is abnormal, an alarm can be issued to remind the user to troubleshoot the fault. For example, the on-duty personnel of the battery swap station can be dispatched to intervene, troubleshoot the battery lift problem, and replace the corresponding problematic parts.

[0130] In one implementation of the embodiment of the present application, the charging and battery swapping process may include a charging process, the energy replenishment device may include a charging module of a battery swap station, and the ultra-high frequency physical signal may include an ultra-high frequency current physical signal. Step S101 may further include the following steps S1013:

[0131] Step S1013: Acquire the ultra-high frequency current physical signal generated by the logic controller when the charging module is charging the battery.

[0132] Step S102 may further include step S1022:

[0133] Step S1022: Based on the ultra-high frequency current physical signal, the trained timing anomaly detection model is applied to perform fault diagnosis on the charging module.

[0134] In this embodiment, the logic controller generates an ultra-high frequency current physical signal during the process of charging the battery by the charging module, and inputs the obtained ultra-high frequency current physical signal into the trained timing anomaly detection model to perform fault diagnosis on the charging module.

[0135] In one embodiment, when it is detected that there is no battery swap order at the battery swap station or there is a fully charged battery at the battery swap station, a charging module detection instruction can be sent to the logic controller of the charging module of the not fully charged position to obtain the ultra-high frequency current physical signal generated by the logic controller during the charging module charging the battery. That is, when the user places an order and there is no fully charged battery in the battery swap station, the diagnostic function of the charging module is not turned on. The charging module detection instruction is only sent when it is detected that there is no order or there is a fully charged battery in the battery swap station, so as to further reduce the impact of the fault diagnosis process on the user's battery swap process.

[0136] In one implementation, if the fault diagnosis result of the charging module of the battery swap station is normal, a charging module detection instruction can be issued regularly to perform fault diagnosis on the charging module. If the fault diagnosis result of the charging module of the battery swap station is abnormal, an alarm can be issued to remind relevant personnel to troubleshoot the fault. For example, the on-duty personnel of the battery swap station can be dispatched to intervene, troubleshoot the problem of the charging module, and replace the corresponding problematic parts.

[0137] In one implementation of the embodiment of the present application, the charging and swapping process may include a charging process, the energy replenishment device may include a charging module of a charging pile, and the ultra-high frequency physical signal may include an ultra-high frequency current physical signal. Step S101 may further include the following steps S1014:

[0138] Step S1014: when the user places a charging order and starts charging the device to be charged, the ultra-high frequency current physical signal generated by the logic controller during the charging module charging the device to be charged is obtained.

[0139] In this embodiment, after the user places an order (charging order), inserts the charging gun into the device to be charged (such as an electric car, etc.), and clicks the "Start Charging" button, the control device of the charging pile can issue a charging command and record the ultra-high frequency current physical signal generated by the logic controller during the charging process. The obtained ultra-high frequency current physical signal is input into the timing anomaly detection model to perform fault diagnosis on the charging module of the charging pile.

[0140] In one implementation, if the fault diagnosis result of the charging module of the charging pile is normal, the charging module self-check instruction can be issued again when waiting for the next charging. If the fault diagnosis result of the charging module of the charging pile is abnormal, an alarm can be issued to remind the user to conduct troubleshooting, such as dispatching the on-duty personnel of the charging pile group to intervene, troubleshoot the problem of the charging module and replace the corresponding problem parts, and recommend the user to go to other charging piles.

[0141] In one implementation, ultra-high frequency physical signal samples may be labeled with normal labels or abnormal labels, and a timing anomaly detection model may be trained based on the labeled ultra-high frequency physical signal samples to obtain a trained timing anomaly detection model.

[0142] Different timing anomaly detection models can be trained for the battery lift, the charging module of the battery swap station, and the charging module of the charging pile, respectively, so as to be used for fault diagnosis of the battery lift, the charging module of the battery swap station, and the charging module of the charging pile, respectively.

[0143] A timing anomaly detection model can also be trained based on the ultra-high frequency physical signal samples of the battery lift, the charging module of the battery swap station, and the charging module of the charging pile for fault diagnosis of the battery lift, the charging module of the battery swap station, and the charging module of the charging pile.

[0144] Please refer to the attached Figure 6 To Attachment Fig.10 , the following is based on the attached Figure 6 To Attachment Fig.10 , based on the system architecture, the fault diagnosis method of the energy charging equipment of the present application is explained.

[0145] like Figure 6 As shown, the battery swap station may include computing equipment and hardware equipment. The computing equipment may include MCS (central control equipment), AEC (AI computing and reasoning equipment), and PLC (logic controller). The hardware equipment may include a battery lift and a charging module. Figure 8 As shown, the fault diagnosis of the battery lifter of the battery swap station may include the following steps S201 to S206:

[0146] Step S201: Check order status.

[0147] In this embodiment, when diagnosing a fault on the battery lift, it is necessary to detect the order status of the battery swap station. If no order has been placed, step S202 can be executed.

[0148] Step S202: the main control sends a lift detection instruction.

[0149] In this embodiment, when there is no user order, the battery swap station master can issue a lift detection instruction.

[0150] Step S203: Acquire ultra-high frequency torque physical signals in no-load and loaded states.

[0151] Step S204: inferring the ultra-high frequency torque physical signal under no-load and loaded states based on the timing anomaly detection model.

[0152] Step S205: Determine whether the inference result is normal; if so, end and continue to execute step S201 after 24 hours; if not, execute step S206.

[0153] Step S206: Alarm prompt, after manual intervention and inspection, execute step S202.

[0154] like Fig. 9 As shown, the fault diagnosis of the charging module of the battery swap station may include the following steps S301 to S307:

[0155] Step S301: Check order status.

[0156] In this embodiment, when diagnosing a fault in the charging module, it is necessary to detect the order status of the battery swap station; if no order has been placed, step S302 can be executed; if an order has been placed, step S306 can be executed.

[0157] Step S302: The main control sends a charging module detection instruction to the storage position that is not fully charged.

[0158] Step S303: Acquire the ultra-high frequency current physical signal of the logic controller of the charging module.

[0159] Step S304: inferring the ultra-high frequency current physical signal based on the timing anomaly detection model.

[0160] Step S305: Determine whether the inference result is normal; if so, end the process and continue to execute step S301 after 24 hours; if not, execute step S307.

[0161] Step S306: Check whether there is a fully charged battery; if so, execute step S302; if not, execute step S301.

[0162] Step S307: Alarm prompt, after manual intervention and inspection, execute step S302.

[0163] See attached Figure 7 The system architecture of the charging pile mainly includes computing devices and hardware devices. The computing devices mainly include MCS (central control device), MCU (microcontroller), and PLC (logic controller). The hardware devices mainly include battery lifters and charging modules.

[0164] like Fig.10 As shown, the fault diagnosis of the charging module of the charging pile mainly includes the following steps S401 to S406:

[0165] Step S401: User clicks “Start Charging”

[0166] Step S402: the main control sends a charging module detection instruction.

[0167] Step S403: Acquire the ultra-high frequency current physical signal of the logic controller of the charging module.

[0168] Step S404: inferring the ultra-high frequency current physical signal based on the timing anomaly detection model.

[0169] Step S405: Determine whether the inference result is normal; if so, end and the user continues charging; if not, execute step S406.

[0170] Step S406: an alarm is given, and after manual intervention and inspection, the user goes to another charging station for charging.

[0171] In one example, please refer to Appendix 1, which shows the number of accidents that can be prevented in advance by diagnosing the fault of the battery lift according to the embodiment of the present application. According to statistics, the loss of battery lift damage is about 200,000 yuan. After the fault diagnosis is performed in advance according to the embodiment of the present application, the cost of replacing parts is 2,000-3,000 yuan, which can effectively reduce the fault maintenance cost of the battery lift in the battery swap station.

[0172] Table 1 Comparison of costs before and after fault diagnosis of battery lift using the embodiment of the present application

[0173] In another example, you can refer to Appendix 2, which shows that performing fault diagnosis on the charging module according to the embodiment of the present application can effectively improve the fault detection rate of the charging module. According to statistics, the fault detection rate of performing fault diagnosis on the charging module according to the embodiment of the present application is 85.71%, that is, the embodiment of the present application can cover 85.71% of the fault scenarios of the charging module, which can effectively reduce the probability of users encountering charging piles or battery swap stations that cannot be charged.

[0174] Table 2 Comparison table of fault diagnosis of charging module before and after using the embodiment of the present application

[0175] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effect of the present application, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted schemes are equivalent to the technical schemes described in this application, and therefore will also fall within the scope of protection of this application.

[0176] It is understood by those skilled in the art that all or part of the processes in the method for implementing the above-mentioned embodiment of the present application can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.

[0177] Another aspect of the present application also provides a computer-readable storage medium.

[0178] In an embodiment of a computer-readable storage medium according to the present application, the computer-readable storage medium may be configured to store a program for executing the energy-compensating device fault diagnosis method of the above method embodiment, and the program may be loaded and run by a processor to implement the above energy-compensating device fault diagnosis method. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The computer-readable storage medium may be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present application is a non-temporary computer-readable storage medium.

[0179] Another aspect of the present application also provides a smart device.

[0180] In an embodiment of an intelligent device according to the present application, the intelligent device may include at least one processor; and a memory connected to the at least one processor in communication; wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in any of the above embodiments is implemented. Figure 2 , Figure 2 FIG. 4 exemplarily shows that the memory 11 and the processor 12 are communicatively connected via a bus.

[0181] In some embodiments of the present application, the smart device may be, but is not limited to, a mobile phone, a tablet computer, a desktop, a laptop, a handheld computer, a notebook computer, a vehicle-mounted device, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), an augmented reality (AR) or a virtual reality (VR) device, etc., and the embodiments of the present application are not limited to this.

[0182] So far, the technical solution of the present application has been described in conjunction with an embodiment shown in the accompanying drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present 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 fall within the protection scope of the present application.

Claims

1. A method for diagnosing faults of energy replenishment equipment, characterized in that: The energy replenishment device includes a logic controller, and the logic controller is used to control the charging and replacement process of the energy replenishment device; the method includes: Acquire the ultra-high frequency physical signal generated by the logic controller during the charging and swapping process; Based on the ultra-high frequency physical signal, a trained timing anomaly detection model is applied to perform fault diagnosis on the energy replenishment device.

2. The energy charging equipment fault diagnosis method according to claim 1, characterized in that: The obtaining of the ultra-high frequency physical signal generated by the logic controller in the process of controlling the charging and swapping includes: Obtaining a control signal generated by the logic controller during the charging and battery replacement process; Based on a preset frequency, sampling the control signal to obtain the ultra-high frequency physical signal; Wherein, the preset frequency is greater than 10 Hz.

3. The energy charging equipment fault diagnosis method according to claim 1, characterized in that: The charging and battery swapping process includes a battery swapping process, the energy replenishment equipment includes a battery lifter of a battery swapping station; the ultra-high frequency physical signal includes an ultra-high frequency torque physical signal; the ultra-high frequency torque physical signal includes a first ultra-high frequency torque physical signal and a second ultra-high frequency torque physical signal; The obtaining of the ultra-high frequency physical signal generated by the logic controller in the process of controlling the charging and swapping includes: Acquire a first ultra-high frequency torque physical signal generated by the logic controller when the battery lift is in an unloaded state; Acquire a second ultra-high frequency torque physical signal generated by the logic controller when the battery lift is in a loaded state.

4. The energy charging equipment fault diagnosis method according to claim 3 is characterized in that: The method of applying a trained timing anomaly detection model based on the ultra-high frequency physical signal to perform fault diagnosis on the energy replenishment device includes: Based on the first ultra-high frequency torque physical signal and the second ultra-high frequency torque physical signal, the trained timing anomaly detection model is applied to perform fault diagnosis on the battery lift.

5. The energy charging equipment fault diagnosis method according to claim 3, characterized in that: The method further comprises: When it is detected that there is no battery replacement order at the battery replacement station, a lift detection instruction is sent to the logic controller, so that the logic controller controls the battery lift to perform self-check to obtain the first ultra-high frequency torque physical signal and the second ultra-high frequency torque physical signal.

6. The energy charging equipment fault diagnosis method according to claim 1, characterized in that: The charging and battery swapping process includes a charging process, the energy replenishment equipment includes a charging module of a battery swapping station; the ultra-high frequency physical signal includes an ultra-high frequency current physical signal; The obtaining of the ultra-high frequency physical signal generated by the logic controller in the process of controlling the charging and swapping includes: Acquire the ultra-high frequency current physical signal generated by the logic controller during the process of the charging module charging the battery.

7. The energy charging equipment fault diagnosis method according to claim 6, characterized in that: The obtaining of the ultra-high frequency current physical signal generated by the logic controller during the process of the charging module charging the battery comprises: When it is detected that there is no battery replacement order at the battery replacement station or there are fully charged batteries at the battery replacement station, a charging module detection instruction is sent to the logic controller of the charging module in the not fully charged position to obtain the ultra-high frequency current physical signal generated by the logic controller during the charging module charging the battery.

8. The energy charging equipment fault diagnosis method according to claim 1, characterized in that: The charging and replacing process includes a charging process, the energy replenishment device includes a charging module of a charging pile; the ultra-high frequency physical signal includes an ultra-high frequency current physical signal; The obtaining of the ultra-high frequency physical signal generated by the logic controller in the process of controlling the charging and swapping includes: When the user places a charging order and starts charging the device to be charged, the ultra-high frequency current physical signal generated by the logic controller during the process of the charging module charging the device to be charged is obtained.

9. The energy charging equipment fault diagnosis method according to claim 6 or 8, characterized in that: The method of applying a trained timing anomaly detection model based on the ultra-high frequency physical signal to perform fault diagnosis on the energy replenishment device includes: Based on the ultra-high frequency current physical signal, a trained timing anomaly detection model is applied to perform fault diagnosis on the charging module.

10. The energy charging equipment fault diagnosis method according to any one of claims 1 to 8, characterized in that: The application of the trained time series anomaly detection model to perform fault diagnosis on the energy replenishment device includes: Using the trained time series anomaly detection model, diagnose the fault of the energy replenishment equipment to obtain a diagnosis result; When the diagnosis result is abnormal, an alarm is issued to prompt troubleshooting.

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