Mobile intelligent charging pile, control system and operation method thereof

Abstract

The invention relates to the technical field of new energy automobile charging equipment, in particular to a mobile intelligent charging pile, a control system and an operation method of the mobile intelligent charging pile. A preset fault diagnosis model is adopted to diagnose and analyze the collected data to obtain a diagnosis result; analyzing and processing the diagnosis result according to a preset decision generation model to obtain a coping decision; generating early warning information and a corresponding control instruction according to the coping decision, and adjusting the state of related equipment arranged on the mobile intelligent charging pile according to the control instruction; and early warning information and a coping decision are sent to monitoring personnel. According to the method, the problem that the mobile intelligent charging pile lacks fault diagnosis measures, for example, the mobile intelligent charging pile is paralyzed for a long time when the charging bag stops due to faults in the moving process on the guide rail can be solved, and the paralysis time of the mobile intelligent charging pile is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicle charging equipment, and in particular to a mobile intelligent charging pile, a control system and an operating method thereof. Background Art

[0002] With the development of new energy vehicles, the proportion of new energy vehicles is increasing, and the demand for charging piles is becoming more and more vigorous. At present, the charging setting method of new energy vehicles is to equip one charging pile for one charging parking space. Since the proportion of fuel vehicles is still relatively high, in the absence of railings, the phenomenon of new energy vehicle charging parking spaces being occupied by fuel vehicles often occurs, resulting in a low utilization rate of charging piles.

[0003] In order to solve this problem, the prior art provides a technical solution for a shared charging pile, which is also called a mobile smart charging pile. A rail is arranged above the parking space, and the charging pack can move on the rail according to the command. When the user parks the car and scans the QR code corresponding to the parking space, the charging pack will automatically move to the parking space to charge the new energy vehicle. However, this technical solution lacks fault diagnosis measures. For example, when the charging pack stops due to a fault during the movement on the rail, the mobile smart charging pile will be paralyzed for a long time.

[0004] Therefore, the present invention provides a mobile intelligent charging pile, a control system and an operation method thereof to solve the above-mentioned problems. Summary of the invention

[0005] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a mobile intelligent charging pile, a control system and an operation method thereof, so as to solve the problem that the above mobile intelligent charging pile lacks fault diagnosis measures, such as when the charging pack fails and stops during the movement on the guide rail, causing the mobile intelligent charging pile to be paralyzed for a long time.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] In a first aspect, the present invention provides an operation method of a mobile intelligent charging pile.

[0008] Including: obtaining collected data from multiple sensors set on the mobile intelligent charging pile;

[0009] Use the preset fault diagnosis model to diagnose and analyze the collected data and the current status of the smart charging pile to obtain the diagnosis results including the abnormal location;

[0010] Analyze and process the diagnosis results according to the preset decision generation model, determine the cause of the abnormality, and match the corresponding response decision based on the cause of the abnormality;

[0011] Generate warning information and corresponding control instructions according to the response decision, adjust the status of relevant equipment set on the mobile intelligent charging pile with the control instructions; and send the warning information and response decision to the monitoring personnel.

[0012] Preferably, the diagnostic process of the preset fault diagnosis model includes: using the first analysis layer to analyze the collected data to determine whether there is abnormal data; if so, obtaining the abnormal data and the collected data of the sensor corresponding to the abnormal data within a preset time period to form an abnormal data set; using the second analysis layer to analyze and process the abnormal data set to determine the abnormal factors, and determine the abnormal location according to the source or positioning information of the abnormal data set; based on the preset correlation relationship between the abnormal factors established by the third analysis layer, each abnormal factor is analyzed to determine at least one abnormal cause; and a diagnostic result is formed based on the abnormal factors, abnormal causes and abnormal locations.

[0013] Preferably, the preset fault diagnosis model also includes: after obtaining the diagnosis result, when the diagnosis result includes that the charging pack stops on the guide rail, acquiring a monitoring image according to the abnormal position in the diagnosis result; determining the position of the charging pack in the monitoring image to determine whether the charging pack stops at the preset position of the guide rail, and if not, sending the diagnosis result to the preset decision generation model.

[0014] Preferably, the processing process of the preset decision generation model includes: determining the abnormal level according to a correspondence table of abnormal factors, abnormal causes and preset abnormal levels in the diagnosis results; matching the abnormal level, abnormal cause, abnormal location and preset response decision based on a correspondence library of preset abnormal levels, preset abnormal causes, preset abnormal locations and preset response decisions to obtain a response decision.

[0015] Preferably, the construction and processing process of the preset decision generation model includes: based on a correspondence table established among preset abnormal factors, preset abnormal causes, and preset abnormal levels, and a correspondence library established among preset abnormal levels, preset abnormal causes, preset abnormal locations and preset response decisions, a convolutional neural network is used to construct a preset decision generation model; when the diagnosis result is received, the preset decision generation model obtains the abnormal level according to the abnormal factors and abnormal causes in the diagnosis result; and generates a response decision according to the abnormal level, abnormal cause, and abnormal location.

[0016] Preferably, the corresponding control instructions are generated according to the response decision, and the status of related equipment set on the mobile intelligent charging pile is adjusted by the control instructions, including: according to the preset correspondence table between response decisions and control instructions, the currently generated response decision is converted into a control instruction, and sent to the corresponding device to adjust the status of the device.

[0017] Preferably, a method for operating a mobile intelligent charging pile further includes:

[0018] Obtain scan data generated after the user scans a preset QR code, wherein the scan data includes a parking space location and user information; analyze and process the scan data to match a charging pack in a standby state; and call the charging pack to the parking space location.

[0019] Preferably, a method for operating a mobile intelligent charging pile further includes:

[0020] When the charging pack is charging the car, the latest version number of the charging pricing model is obtained; according to the version number, it is determined whether the built-in charging pricing model set in the charging pack is the same; if so, when charging the car, the charging is calculated according to the built-in charging pricing model; if not, the latest charging pricing model is downloaded, and the charging is calculated according to the latest charging pricing model when charging the car.

[0021] In a second aspect, the present invention provides a mobile intelligent charging pile, comprising:

[0022] At least one processor; a memory; at least one application, wherein the at least one application is stored in the memory and is configured to be executed by the at least one processor, and the at least one application is configured to: execute the operating method described in any one of the above schemes.

[0023] In a third aspect, the present invention provides a control system for a mobile intelligent charging pile, comprising:

[0024] A data acquisition module, used to acquire data collected by multiple sensors provided on the mobile intelligent charging pile;

[0025] The fault diagnosis module uses a preset fault diagnosis model to perform diagnostic analysis on the collected data to analyze the current status of the smart charging pile to obtain a diagnostic result including the abnormal location;

[0026] The decision generation module analyzes and processes the diagnosis results according to the preset decision generation model, determines the cause of the abnormality, and matches the corresponding response decision based on the cause of the abnormality;

[0027] The central processing module generates warning information and corresponding control instructions according to the response decision, adjusts the status of relevant equipment set on the mobile intelligent charging pile with the control instructions, and sends the warning information and response decision to the monitoring personnel.

[0028] The beneficial effects of the present invention are:

[0029] 1. The present invention uses a preset fault diagnosis model to diagnose and analyze the collected data obtained by multiple sensors on the mobile intelligent charging pile to obtain a diagnosis result containing the abnormal position; then a preset decision generation model is used to analyze and process the diagnosis result, determine the cause of the abnormality, and match the corresponding response decision based on the abnormal cause; finally, according to the response decision, an early warning message and a corresponding control instruction are generated, and while sending an early warning message to the monitoring personnel, the status of the relevant equipment is adjusted according to the control instruction; the monitoring personnel analyzes the response decision and the monitoring image and promptly goes to the scene to deal with the fault. The present invention adopts the above-mentioned method to solve the problem of lack of fault diagnosis measures for mobile intelligent charging piles, such as when the charging pack stops due to a fault during the movement on the guide rail, causing the mobile intelligent charging pile to be paralyzed for a long time, reducing the paralysis time of the mobile intelligent charging pile and improving the service quality of the mobile intelligent charging pile.

[0030] 2. The preset fault diagnosis model of the present invention includes three analysis layers. The first analysis layer is used to analyze the collected data to determine whether there is abnormal data; if so, the abnormal data and the collected data of the sensor corresponding to the abnormal data within a preset time period are obtained to form an abnormal data set; the second analysis layer is used to analyze and process the abnormal data set to determine the abnormal factors, and determine the abnormal location according to the source or positioning information of the abnormal data set; based on the preset correlation relationship between the abnormal factors established by the third analysis layer, each abnormal factor is analyzed to determine at least one abnormal cause; the diagnosis result is formed based on the abnormal factors, abnormal causes and abnormal locations. The preset fault diagnosis model used in the present invention can accurately diagnose the fault problem of the mobile intelligent charging pile according to the abnormal data set, so that the preset decision generation model can match the response decision in a targeted manner.

[0031] 3. The preset decision generation model of the present invention proposes two ways to generate response decisions. The first one is based on the pre-established correspondence table and correspondence library to process the information in the diagnosis results and find the response decision, which is suitable for scenarios with small data volumes; the second one is based on the correspondence table and correspondence library, and uses a preset decision generation model constructed by a convolutional neural network to process the diagnosis results to generate a response decision suitable for scenarios with large data volumes. The present invention selects preset decision generation models in different ways according to different data volumes, which can improve the processing efficiency of generating response decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A schematic flow chart of an operating method of a mobile intelligent charging pile according to the present invention;

[0033] Figure 2 The module diagram of a control system of a mobile intelligent charging pile of the present invention. DETAILED DESCRIPTION

[0034] The following will refer to the attached Figure 1 To Attachment Figure 2 The embodiments of the present invention are described in detail. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0035] The overall equipment of the mobile intelligent charging pile includes a guide rail, a moving device, a transposition device, a charging pack, and a charging room. The moving device can carry the charging pack and move on the guide rail; the transposition device can transpose the charging pack on the guide rail to a designated position to charge new energy vehicles; the charging room is equipped with multiple charging packs in standby state, and can charge the charging packs.

[0036] The present invention provides an operation method of a mobile intelligent charging pile, as shown in the attached Figure 1 As shown, the following steps are included:

[0037] Step S11: Acquire data collected by multiple sensors installed on the mobile intelligent charging pile.

[0038] In step S11, different numbers and types of sensors are set at multiple locations on the mobile smart charging pile, and the types of sensors include but are not limited to positioning sensors, current sensors, voltage sensors, etc., to collect various detection data of the mobile smart charging pile.

[0039] Step S12: Use a preset fault diagnosis model to perform diagnostic analysis on the collected data, analyze the current state of the smart charging pile, and obtain a diagnostic result including the abnormal location.

[0040] In step S12, the current state of the smart charging pile includes normal and abnormal states. When there is no abnormal data in the collected data, the state of the smart charging pile is normal, and the diagnosis result is normal, with no abnormal position; when there is abnormal data in the collected data, the smart charging pile is in an abnormal state, and the diagnosis result of the preset fault diagnosis model includes the abnormal position, abnormal factors, abnormal causes, etc., so that the preset decision generation model analyzes the diagnosis results and obtains a response decision.

[0041] Step S13: Analyze and process the diagnosis results according to the preset decision generation model, determine the cause of the abnormality, and match the corresponding response decision based on the cause of the abnormality.

[0042] In step S13, the abnormality cause includes but is not limited to the fault type, equipment model, and component model.

[0043] Step S14: Generate warning information and corresponding control instructions according to the response decision, adjust the status of relevant equipment set on the mobile intelligent charging pile with the control instructions; and send the warning information and response decision to the monitoring personnel.

[0044] In step S14, after the monitoring personnel obtain the early warning information and the response decision, they can analyze the response decision and the monitoring image of the abnormal location and go to the scene to deal with the fault in time, so as to restore the mobile smart charging pile from the paralyzed state to normal working state as soon as possible.

[0045] In this embodiment, the present invention uses a preset fault diagnosis model to perform diagnostic analysis on the collected data obtained by multiple sensors on the mobile intelligent charging pile to obtain a diagnostic result containing the abnormal position; then a preset decision generation model is used to analyze and process the diagnostic result, determine the cause of the abnormality, and match the corresponding response decision based on the abnormal cause; finally, according to the response decision, an early warning message and a corresponding control instruction are generated, and while issuing an early warning message to the monitoring personnel, the status of the relevant equipment is adjusted according to the control instruction; the monitoring personnel analyzes the response decision and the monitoring image and promptly goes to the scene to handle the fault. The present invention adopts the above-mentioned method to solve the problem of lack of fault diagnosis measures for mobile intelligent charging piles, such as when the charging pack stops due to a fault during the movement on the guide rail, causing the mobile intelligent charging pile to be paralyzed for a long time, reducing the paralysis time of the mobile intelligent charging pile and improving the service quality of the mobile intelligent charging pile.

[0046] In one embodiment of the present invention, the diagnostic process of the preset fault diagnosis model includes: using the first analysis layer to analyze the collected data to determine whether there is abnormal data; if so, obtaining the abnormal data and the collected data of the sensor corresponding to the abnormal data within a preset time period to form an abnormal data set; using the second analysis layer to analyze and process the abnormal data set to determine the abnormal factors, and determine the abnormal location based on the source or positioning information of the abnormal data set; based on the preset correlation relationship between the abnormal factors established by the third analysis layer, each abnormal factor is analyzed to determine at least one abnormal cause; and a diagnostic result is formed based on the abnormal factors, abnormal causes and abnormal locations.

[0047] Preferably, abnormal factors include but are not limited to voltage, current, temperature, position, and timeout corresponding to each device on the mobile intelligent charging pile.

[0048] Preferably, the abnormal cause includes but is not limited to the fault type, equipment model, and component model.

[0049] Specifically, the preset fault diagnosis model includes three analysis layers. The first analysis layer is used to determine whether the collected data contains data that does not meet the preset standards. If not, the collected data is normal; if so, it contains abnormal data, and then the abnormal data and the collected data of the sensor corresponding to the abnormal data within a preset time period are obtained to form an abnormal data set sorted by time. Analyzing the abnormal data set can avoid accidental acquisition failures of the sensor. The second analysis layer is used to process the abnormal data set, filter out the actual abnormal data, and determine the corresponding abnormal factors based on the abnormal data; and then use the third analysis layer to process each abnormal factor to obtain the cause of the abnormality, and form a diagnosis result based on the abnormal factor, abnormal cause and abnormal location.

[0050] Preferably, the preset standard is a standard corresponding to each collected data, including but not limited to the upper and lower limits corresponding to the collected data, and the preset upper limit of the time that the charging pack stays in a non-set position when moving on the guide rail.

[0051] Through the configuration of this embodiment, the preset fault diagnosis model adopted by the present invention can accurately diagnose the fault problem of the mobile intelligent charging pile according to the abnormal data set, so that the preset decision generation model can match the response decision in a targeted manner.

[0052] In one embodiment of the present invention, the preset fault diagnosis model also includes: after obtaining the diagnosis result, when the diagnosis result includes that the charging pack stops on the guide rail, acquiring a monitoring image according to the abnormal position in the diagnosis result; determining the position of the charging pack in the monitoring image to determine whether the charging pack stops at the preset position of the guide rail, and if not, sending the diagnosis result to the preset decision generation model.

[0053] In this embodiment, when it is detected that the diagnosis result includes the charging pack stopping on the guide rail, the monitoring image of the corresponding guide rail will be called, and the monitoring image will be analyzed to determine whether the current stopping position of the charging pack is normal. If not, it is determined that the position of the charging pack is abnormal, further ensuring the correctness of the diagnosis result and avoiding incorrect diagnosis results.

[0054] Preferably, the method of identifying the location of the charging pack includes but is not limited to ① an image recognition model constructed based on preset image data and a convolutional neural network; ② monitoring whether the corresponding positions of the charging pack and the parking space in the image are correct; ③ monitoring the coding number of the section of the guide rail where the charging pack is located in the image.

[0055] In an optional embodiment of the present invention, the processing process of the preset decision generation model includes: determining the abnormal level according to a correspondence table of abnormal factors, abnormal causes and preset abnormal levels in the diagnosis results; matching the abnormal level, abnormal cause, abnormal location and preset response decision based on a correspondence library of preset abnormal levels, preset abnormal causes, preset abnormal locations and preset response decisions to obtain a response decision.

[0056] Specifically, according to the status of each device of the mobile intelligent charging pile and the abnormal situations that the charging pack may encounter during the movement, the abnormal level was evaluated and targeted response strategies were designed; and a correspondence table between the abnormal factors, abnormal causes, and preset abnormal levels was established based on the three, so that the corresponding abnormal level can be found through the abnormal factors and abnormal causes; similarly, based on the preset abnormal level, preset abnormal cause, preset abnormal position and the corresponding preset response decision, a correspondence library was established between the four, so that the corresponding response decision can be matched through the abnormal level, abnormal cause and abnormal position, so as to solve the problems encountered by the mobile intelligent charging pile.

[0057] In another optional embodiment of the present invention, the construction and processing process of the preset decision generation model includes: based on a correspondence table established among preset abnormal factors, preset abnormal causes, and preset abnormal levels, and a correspondence library established among preset abnormal levels, preset abnormal causes, preset abnormal locations and preset response decisions, a convolutional neural network is used to construct a preset decision generation model; when the diagnosis result is received, the preset decision generation model obtains the abnormal level according to the abnormal factors and abnormal causes in the diagnosis result; and generates a response decision according to the abnormal level, abnormal cause, and abnormal location.

[0058] In this embodiment, the preset decision generation model constructed using a convolutional neural network includes an input layer, multiple convolutional layers, and an output layer. The final preset decision generation model is obtained based on the training data and test data established based on the correspondence table and the correspondence library to automatically analyze the diagnosis results and match targeted response decisions.

[0059] In the above two optional embodiments, the preset decision generation model of the present invention proposes two ways to generate response decisions. The first one is based on the pre-established correspondence table and correspondence library, processes the information in the diagnosis result, finds the response decision, and is suitable for scenes with small data volume; the second one is based on the correspondence table and correspondence library, and uses a preset decision generation model constructed by a convolutional neural network to process the diagnosis result to generate a response decision suitable for scenes with large data volume. The present invention selects preset decision generation models in different ways according to different data volumes, which can improve the processing efficiency of generating response decisions.

[0060] For example, when the mobile device cannot move forward in the preset direction with the charging pack, and has been at the current position for more than the preset time, the response decision is: return the charging pack to the original standby position in the charging room, call another charging pack to move to the preset position; if the same fault occurs again, the response decision is to adjust the charging state of the parking space after the fault position to an abnormal state, return the charging pack at the current position to the charging room, cancel the current charging service, and remind the user of the charging fault at the corresponding position. The reminder can be in the form of indicator lights, sensors, etc.

[0061] In one embodiment of the present invention, the corresponding control instructions are generated according to the response decision, and the status of related equipment set on the mobile intelligent charging pile is adjusted by the control instructions, including: according to the preset correspondence table between response decisions and control instructions, the currently generated response decision is converted into a control instruction, and sent to the corresponding device to adjust the status of the device.

[0062] Specifically, the control instructions converted from the response decisions are sent to the corresponding devices to adjust the status of the devices, including but not limited to changes in the status indicator lights, the rotation direction of the mobile device, the charging or discharging status of the charging pack, etc., so as to avoid the mobile smart charging pile from falling into a long-term paralyzed state.

[0063] In one embodiment of the present invention, a method for operating a mobile intelligent charging pile further includes:

[0064] Obtain scan data generated after the user scans a preset QR code, wherein the scan data includes a parking space location and user information; analyze and process the scan data to match a charging pack in a standby state; and call the charging pack to the parking space location.

[0065] Specifically, when the user scans the preset QR code corresponding to the current parking space and completes the relevant operations, the scanning data combining the user and the current parking space will be generated; then the central processing module will process the scanned data, match the appropriate charging pack, and move the charging pack to the current position through the mobile device; at the same time, each sensor collects data, and the preset diagnostic model performs diagnostic analysis on the collected data in real time, and diagnoses the movement of the charging pack in real time, so as to call the preset decision generation module in time to generate a response decision when a problem is diagnosed; if the problem cannot be solved automatically, the monitoring personnel and the user will be informed so that the monitoring personnel can perform maintenance in time, and at the same time avoid the user being in a long waiting state, and remind the user to change the charging location.

[0066] In one embodiment of the present invention, a method for operating a mobile intelligent charging pile further includes:

[0067] When the charging pack is charging the car, the latest version number of the charging pricing model is obtained; according to the version number, it is determined whether the built-in charging pricing model set in the charging pack is the same; if so, when charging the car, the charging is calculated according to the built-in charging pricing model; if not, the latest charging pricing model is downloaded, and the charging is calculated according to the latest charging pricing model when charging the car.

[0068] In this embodiment, the charging pricing model will change with the change of time. The present invention adopts the method of this embodiment to ensure that the charging pack applies the latest charging pricing model, performs pricing when charging new energy vehicles, and adjusts the charging price in time to avoid losses to the company.

[0069] The present invention also provides a mobile intelligent charging pile, comprising: at least one processor; a memory; and at least one application, wherein the at least one application is stored in the memory and configured to be executed by the at least one processor, and the at least one application is configured to: execute the operating method described in any one of the above embodiments.

[0070] Specifically, the mobile intelligent charging pile overall equipment includes a guide rail, a moving device, a transposition device, a charging pack, and a charging room, wherein the moving device can carry the charging pack and move on the guide rail; the transposition device can transpose the charging pack on the guide rail to a designated charging position to charge the new energy vehicle, and keep the guide rail in a connected state without affecting the passage of the charging pack; the charging room is provided with multiple charging packs in a standby state, and contains multiple devices for charging the charging packs. The mobile intelligent charging pile of the present invention can satisfy multiple charging packs to charge new energy vehicles at the same time.

[0071] The present invention also provides a control system for a mobile intelligent charging pile. Figure 2 ,include:

[0072] The data acquisition module is used to obtain the collected data of multiple sensors installed on the mobile intelligent charging pile.

[0073] The fault diagnosis module uses a preset fault diagnosis model to perform diagnostic analysis on the collected data to analyze the current status of the smart charging pile to obtain a diagnostic result that includes the abnormal location.

[0074] The decision generation module analyzes and processes the diagnosis results according to the preset decision generation model, determines the cause of the abnormality, and matches the corresponding response decision based on the cause of the abnormality.

[0075] The central processing module generates warning information and corresponding control instructions according to the response decision, adjusts the status of relevant equipment set on the mobile intelligent charging pile with the control instructions, and sends the warning information and response decision to the monitoring personnel.

[0076] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0077] It should be noted that in the description of the present invention, the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings, which are only for the convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0078] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0079] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0080] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0081] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0082] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0083] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0084] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, 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 invention.

Claims

1. A method for operating a mobile intelligent charging pile, characterized in that: include: Acquire data collected by multiple sensors installed on the mobile intelligent charging pile; Use the preset fault diagnosis model to diagnose and analyze the collected data and the current status of the smart charging pile to obtain the diagnosis results including the abnormal location; Analyze and process the diagnosis results according to the preset decision generation model, determine the cause of the abnormality, and match the corresponding response decision based on the cause of the abnormality; Generate warning information and corresponding control instructions according to the response decision, adjust the status of relevant equipment set on the mobile intelligent charging pile with the control instructions; and send the warning information and response decision to the monitoring personnel.

2. The operating method according to claim 1, characterized in that: The diagnostic process of the preset fault diagnosis model includes: using the first analysis layer to analyze the collected data to determine whether there is abnormal data; if so, obtaining the abnormal data and the collected data of the sensor corresponding to the abnormal data within a preset time period to form an abnormal data set; using the second analysis layer to analyze and process the abnormal data set to determine the abnormal factor, and determine the abnormal location according to the source or positioning information of the abnormal data set; based on the preset correlation relationship between the abnormal factors established by the third analysis layer, analyzing each abnormal factor to determine at least one abnormal cause; forming a diagnostic result based on the abnormal factor, abnormal cause and abnormal location.

3. The operating method according to claim 2, characterized in that: The preset fault diagnosis model also includes: after obtaining the diagnosis result, when the diagnosis result includes that the charging pack stops on the guide rail, obtaining a monitoring image according to the abnormal position in the diagnosis result; determining the position of the charging pack in the monitoring image to determine whether the charging pack stops at the preset position of the guide rail, and if not, sending the diagnosis result to the preset decision generation model.

4. The operating method according to claim 3, characterized in that: The processing process of the preset decision generation model includes: determining the abnormal level according to a correspondence table of abnormal factors, abnormal causes and preset abnormal levels in the diagnosis results; matching the abnormal level, abnormal cause, abnormal location and preset response decision based on a correspondence library of preset abnormal levels, preset abnormal causes, preset abnormal locations and preset response decisions to obtain a response decision.

5. The operating method according to claim 3, characterized in that: The construction and processing process of the preset decision generation model includes: based on a correspondence table established among preset abnormal factors, preset abnormal causes, and preset abnormal levels, and a correspondence library established among preset abnormal levels, preset abnormal causes, preset abnormal locations, and preset response decisions, a preset decision generation model is constructed using a convolutional neural network; when a diagnosis result is received, the preset decision generation model obtains an abnormal level according to the abnormal factors and abnormal causes in the diagnosis result; and generates a response decision according to the abnormal level, abnormal cause, and abnormal location.

6. The operating method according to claim 1, characterized in that: The corresponding control instructions are generated according to the response decision, and the status of related devices set on the mobile intelligent charging pile are adjusted by the control instructions, including: according to the preset correspondence table between the response decision and the control instruction, the currently generated response decision is converted into a control instruction, and sent to the corresponding device to adjust the status of the device.

7. The operating method according to claim 6, characterized in that: Also includes: Obtaining scan data generated after a user scans a preset QR code, wherein the scan data includes a parking space location and user information; The scanned data is analyzed and processed to match the charging pack in the standby state; and the charging pack is called to the parking space location.

8. The operating method according to claim 7, characterized in that: Also includes: When the charging pack is charging the car, the latest version number of the charging pricing model is obtained; according to the version number, it is determined whether the built-in charging pricing model set in the charging pack is the same; if so, when charging the car, the charging is calculated according to the built-in charging pricing model; if not, the latest charging pricing model is downloaded, and the charging is calculated according to the latest charging pricing model when charging the car.

9. A mobile intelligent charging pile, characterized in that: include: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the operating method according to any one of claims 1 to 8.

10. A control system for a mobile intelligent charging pile, characterized in that: include: A data acquisition module, used to acquire data collected by multiple sensors provided on the mobile intelligent charging pile; The fault diagnosis module uses a preset fault diagnosis model to perform diagnostic analysis on the collected data to analyze the current status of the smart charging pile to obtain a diagnostic result including the abnormal location; The decision generation module analyzes and processes the diagnosis results according to the preset decision generation model, determines the cause of the abnormality, and matches the corresponding response decision based on the cause of the abnormality; The central processing module generates warning information and corresponding control instructions according to the response decision, and adjusts the status of related equipment set on the mobile intelligent charging pile with the control instructions; And send early warning information and response decisions to monitoring personnel.

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