Abnormality detection method and device of charging gun, electronic equipment, medium and product

Abstract

The invention provides an anomaly detection method and device for a charging gun, electronic equipment, a medium and a product. The anomaly detection method comprises the steps of obtaining charging state message information, a measurement data record and a station feature environment of the charging gun; determining plugging information of the charging gun based on the charging state message information; performing data cleaning on the target information of the charging gun based on the measurement data record of the charging gun, the station characteristic environment and the plugging information; and performing anomaly detection on the charging gun based on the target information after data cleaning and a preset anomaly judgment mode to obtain an anomaly detection result. In the mode, comprehensive abnormity judgment is performed on the charging gun, so that the operation and maintenance working efficiency, the availability rate of a station and the satisfaction degree of users are improved.

Description

Technical Field

[0001] The present invention relates to the field of charging detection technology, and in particular to a charging gun abnormality detection method, device, electronic equipment, medium and product. Background Art

[0002] With the cumulative sales of new energy vehicles and the increasing demand for charging among new energy vehicle users, the infrastructure of charging piles has been vigorously developed. Therefore, abnormal detection of charging guns of charging piles is also particularly important.

[0003] Charging guns can be divided into smart charging guns and non-smart charging guns. Smart charging guns have richer functions. Compared with non-smart charging guns, they can realize real-time detection of their own conditions, and connect with the cloud, online platforms, etc., to report their own conditions in real time, and can directly realize real-time detection and determination of their own abnormalities. However, due to environmental factors, technical support factors, cost factors, charging demand and other factors in some areas, these areas will not choose to install smart charging guns to solve the charging problems of new energy vehicles. Therefore, non-smart charging guns cannot be banned.

[0004] Currently, non-intelligent charging guns collect and report the gun tip temperature, and also provide functions such as high temperature warning of the gun tip temperature and generation of work orders. However, they cannot cover all abnormal scenarios of non-intelligent charging guns. For abnormal problems of non-intelligent charging guns that are not caused by abnormal high temperature of the gun tip temperature, it is impossible to make an abnormal judgment. Therefore, it is impossible to make a comprehensive abnormal judgment on the non-intelligent charging gun, which not only affects the efficiency of operation and maintenance, but also reduces the availability of the station and user satisfaction. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a charging gun abnormality detection method, device, electronic device, medium and product, which perform comprehensive abnormality judgment on the charging gun, thereby improving operation and maintenance efficiency, site availability and user satisfaction.

[0006] In the first aspect, an embodiment of the present invention provides a method for detecting an abnormality of a charging gun, including: obtaining charging status message information, measurement data records, and site characteristic environment of the charging gun; determining the plugging and unplugging information of the charging gun based on the charging status message information; performing data cleaning on target information of the charging gun based on the measurement data records, site characteristic environment, and plugging and unplugging information of the charging gun; performing an abnormality detection on the charging gun based on the target information after data cleaning and a pre-set abnormality judgment method to obtain an abnormality detection result.

[0007] In a preferred embodiment of the present invention, the method also includes: if the abnormality detection result indicates that the charging gun is normal, then a first abnormality prediction is performed on the charging gun based on the target information and plug-in information after data cleaning to obtain a first abnormality prediction result; if the abnormality detection result indicates that the charging gun is a first type of abnormality, then an abnormality elimination is performed on the charging pile corresponding to the charging gun to obtain an elimination result; if the elimination result is that there is an abnormality in the charging pile, then the life characteristics of the charging gun are analyzed based on the maintenance and scrapping data of the charging gun acquired in advance; and a second abnormality prediction is performed on the abnormality of the charging gun based on the life characteristics corresponding to the charging gun to obtain a second abnormality prediction result.

[0008] In a preferred embodiment of the present invention, the above-mentioned determination of the plugging and unplugging information of the charging gun based on the charging status message information includes: determining the current state and the previous state of the charging gun based on the charging status message information; recording the current state and the previous state to form a plugging and unplugging detailed data record; determining the plugging and unplugging times of the charging gun based on each current state in the plugging and unplugging detailed data record and the previous state corresponding to each current state; using the plugging and unplugging detailed data record and the plugging and unplugging times as the plugging and unplugging information.

[0009] In a preferred embodiment of the present invention, the above-mentioned data cleaning of the target information of the charging gun based on the measurement data record of the charging gun, the station characteristic environment and the plug-in information includes: determining whether the terminal in the station characteristic environment corresponding to the charging gun is a fast charging terminal based on the measurement data record; if the terminal in the station characteristic environment corresponding to the charging gun is a fast charging terminal, determining the plug-in times and the plug-in detail data record corresponding to the charging gun based on the plug-in information; and data cleaning of the target information corresponding to the plug-in times in the measurement data record based on the plug-in detail data record.

[0010] In a preferred embodiment of the present invention, the above-mentioned abnormality determination of the charging gun based on the target information after data cleaning and the pre-set abnormality determination method to obtain the abnormality detection result includes: if the number of times that the charging standard voltage in the target information after data cleaning within the preset time is greater than or equal to the voltage threshold meets the first number threshold in the abnormality determination method, then the abnormality detection result is that the first abnormality occurs; if the number of times that the gun tip temperature in the target information after data cleaning within the preset time is within the preset temperature range meets the second number threshold in the abnormality determination method, then the abnormality detection result is that no abnormality occurs, otherwise the abnormality detection result is that the second abnormality occurs; if the target information after data cleaning within the preset time is If the duration of the emergency stop feedback meets the duration threshold in the abnormal judgment method, the abnormal detection result is the occurrence of the third abnormality; if the number of orders in the target information after data cleaning within the preset time does not meet the order number threshold in the abnormal judgment method, and the order numbers of the remaining charging guns in the station characteristic environment corresponding to the charging gun all meet the order number threshold, then the abnormal detection result is the occurrence of the fourth abnormality; if the charging voltage in the target information after data cleaning within the preset time does not meet the preset voltage range threshold, then the abnormal detection result is the occurrence of the fifth abnormality; if the charging current in the target information after data cleaning within the preset time does not meet the preset current range threshold, then the abnormal detection result is the occurrence of the sixth abnormality.

[0011] In a preferred embodiment of the present invention, if the abnormal detection result does not include the first abnormality, the second abnormality, the third abnormality, the fourth abnormality, the fifth abnormality and the sixth abnormality, the abnormal detection result characterizes that the charging gun is normal; if the abnormal detection result does not include the first abnormality and the second abnormality, and includes the third abnormality and the fourth abnormality, the abnormal detection result characterizes that the charging gun is a first type of abnormality; if the abnormal detection result does not include the first abnormality, the second abnormality, the third abnormality and the fourth abnormality, the abnormal detection result characterizes that the charging gun is a second type of abnormality.

[0012] In a preferred embodiment of the present invention, a first abnormality prediction is performed on the charging gun based on the target information and plug-in information after data cleaning to obtain a first abnormality prediction result, including: determining the charging standard voltage level of the charging gun based on the charging standard voltage in the target information after data cleaning, a preset first prediction threshold interval and the number of orders; determining the gun tip temperature level based on the gun tip temperature in the target information after data cleaning, a preset second prediction threshold interval and the number of orders; determining the plug-in number level based on the plug-in number in the plug-in information and a preset third prediction threshold interval; performing a first abnormality prediction on the charging gun based on the charging standard voltage level, the gun tip temperature level, the plug-in number level and a preset level coefficient to obtain a first abnormality prediction result.

[0013] In a preferred embodiment of the present invention, a first abnormality prediction is performed on the charging gun based on the charging standard voltage level, the gun head temperature level, the plug-in number level and a preset coefficient threshold interval to obtain a first abnormality prediction result, including: determining the charging standard voltage level coefficient corresponding to the charging gun based on the charging standard voltage level and the preset level coefficient; determining the gun head temperature level coefficient corresponding to the charging gun based on the gun head temperature level and the preset level coefficient; determining the plug-in number level coefficient corresponding to the charging gun based on the plug-in number level and the preset level coefficient; determining the comprehensive level coefficient of the charging gun based on the charging standard voltage level coefficient, the gun head temperature level coefficient, the plug-in number level coefficient and a preset weight ratio; determining the first abnormality prediction result based on the comprehensive level coefficient and the coefficient threshold interval; the first abnormality prediction result includes: the charging gun is currently damaged, the charging gun is currently in normal use, and the charging gun is damaged within the predicted time.

[0014] In a preferred embodiment of the present invention, the charging pile corresponding to the charging gun is subjected to abnormality elimination to obtain an elimination result, including: eliminating abnormalities of the charging pile corresponding to the charging gun based on the charging voltage and the charging current; after starting the self-repair function of the charging pile, if the charging voltage does not meet the voltage range threshold, or the charging current does not meet the current range threshold, then it is determined that the elimination result is that there is an abnormality in the charging pile.

[0015] In a preferred embodiment of the present invention, a second prediction is made on the abnormality of the charging gun based on the life characteristics corresponding to the charging gun to obtain a second abnormality prediction result, including: determining the number of repairs and the damage rate of the charging gun based on the life characteristics; and making a second prediction on the abnormality of the charging gun based on the number of repairs and the damage rate to obtain a second abnormality prediction result.

[0016] In a preferred embodiment of the present invention, after performing abnormality detection on the charging gun based on the target information after data cleaning and a pre-set abnormality determination method to obtain the abnormality detection result, the method also includes: storing the abnormality detection result in correspondence with the measurement data record of the charging gun; the measurement data record includes location association information of the charging gun; the location association information includes: data date, power station name, centralized control address, terminal number, equipment type and equipment unique identification number; verifying the correctness of the abnormality detection result; if the abnormality detection result is correct, then issuing an early warning reminder to the operation and maintenance group of the charging gun based on the location association information corresponding to the abnormality detection result.

[0017] In the second aspect, an embodiment of the present invention also provides an abnormality detection device for a charging gun, including: an information acquisition module, used to obtain charging status message information, measurement data records and site characteristic environment of the charging gun; a plug-in information determination module, used to determine the plug-in information of the charging gun based on the charging status message information; a data cleaning module, used to perform data cleaning on the target information of the charging gun based on the measurement data records of the charging gun, the site characteristic environment and the plug-in information; an abnormality judgment module, used to perform abnormality detection on the charging gun based on the target information after data cleaning and a pre-set abnormality judgment method, to obtain an abnormality detection result.

[0018] In a third aspect, an embodiment of the present invention further provides an electronic device, including a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the abnormality detection method for the charging gun of the first aspect.

[0019] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the abnormality detection method for the charging gun according to the first aspect.

[0020] In a fifth aspect, an embodiment of the present invention further provides a computer program product, comprising a computer-readable storage medium storing a non-volatile program code executable by a processor, wherein the program code comprises instructions for executing the abnormality detection method for a charging gun according to the first aspect.

[0021] The embodiments of the present invention bring the following beneficial effects:

[0022] The embodiment of the present invention provides a method, device, electronic device, medium and product for detecting abnormalities of a charging gun, which obtains the charging status message information, measurement data records and station characteristic environment of the charging gun; determines the plugging and unplugging information of the charging gun based on the charging status message information; performs data cleaning on the target information of the charging gun based on the measurement data records, station characteristic environment and plugging and unplugging information of the charging gun; performs abnormality detection on the charging gun based on the target information after data cleaning and a pre-set abnormality determination method to obtain an abnormality detection result. In this method, a comprehensive abnormality determination is performed on the charging gun, thereby improving the operation and maintenance efficiency, the availability of the station and the user satisfaction.

[0023] Other features and advantages of the present disclosure will be set forth in the following description, or some features and advantages may be inferred or unambiguously determined from the description, or may be learned by implementing the above-mentioned technology of the present disclosure.

[0024] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A flowchart of a method for detecting abnormality of a charging gun provided by an embodiment of the present invention;

[0027] Figure 2 A flowchart of another abnormality detection method for a charging gun provided by an embodiment of the present invention;

[0028] Figure 3 A flowchart of another abnormality detection method for a charging gun provided in an embodiment of the present invention;

[0029] Figure 4 A life cycle function diagram provided by an embodiment of the present invention;

[0030] Figure 5 A schematic diagram of the structure of an abnormality detection device for a charging gun provided in an embodiment of the present invention;

[0031] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] With the cumulative sales of new energy vehicles and the increasing demand for charging among new energy vehicle users, the infrastructure of charging piles has been vigorously developed. Therefore, abnormal detection of charging guns of charging piles is also particularly important.

[0034] Charging guns can be divided into smart charging guns and non-smart charging guns. Smart charging guns have richer functions. Compared with non-smart charging guns, they can realize real-time detection of their own conditions, and connect with the cloud, online platforms, etc., to report their own conditions in real time, and can directly realize real-time detection and determination of their own abnormalities. However, due to environmental factors, technical support factors, cost factors, charging demand and other factors in some areas, these areas will not choose to install smart charging guns to solve the charging problems of new energy vehicles. Therefore, non-smart charging guns cannot be banned.

[0035] Currently, non-intelligent charging guns collect and report the gun tip temperature, and also provide functions such as high temperature warning of the gun tip temperature and generation of work orders. However, they cannot cover all abnormal scenarios of non-intelligent charging guns. For abnormal problems of non-intelligent charging guns that are not caused by abnormal high temperature of the gun tip temperature, it is impossible to make an abnormal judgment. Therefore, it is impossible to make a comprehensive abnormal judgment on the non-intelligent charging gun, which not only affects the efficiency of operation and maintenance, but also reduces the availability of the station and user satisfaction.

[0036] Based on this, the embodiments of the present invention provide a charging gun abnormality detection method, device, electronic device, medium and product, which can obtain the charging status message information, measurement data record and station characteristic environment of the charging gun; determine the plugging and unplugging information of the charging gun based on the charging status message information; perform data cleaning on the target information of the charging gun based on the measurement data record, station characteristic environment and plugging and unplugging information of the charging gun; perform abnormality detection on the charging gun based on the target information after data cleaning and the pre-set abnormality judgment method to obtain the abnormality detection result. In this method, the charging gun is comprehensively judged for abnormalities, thereby improving the efficiency of operation and maintenance work, the availability of the station and user satisfaction.

[0037] To facilitate understanding of this embodiment, a charging gun abnormality detection method disclosed in an embodiment of the present invention is first introduced in detail.

[0038] Example 1

[0039] The embodiment of the present invention provides a method for detecting abnormality of a charging gun. Figure 1 The following is a flow chart of a method for detecting abnormality of a charging gun provided by an embodiment of the present invention. Figure 1 As shown, the abnormality detection method of the charging gun may include the following steps:

[0040] Step S101, obtaining the charging status message information, measurement data records and station characteristic environment of the charging gun.

[0041] The charging status message information may include the charging gun being charged, the charging gun being idle, the charging gun being faulty, the charging gun being off the grid, and the charging gun being on standby. The acquired charging status message information may be the status change of the charging gun within a period of time.

[0042] The measurement data record may be obtained through a PDU (Protocol Data Unit) and a PCU (Power Control Unit), and the measurement data record may include a PDU measurement data record and a PCU measurement data record.

[0043] Among them, the PDU measurement data record may specifically include: data generation time and the message time corresponding to the data generation time, power station name, centralized control address, equipment index, equipment SN, equipment production date, manufacturer product number, operating status, insulation resistance positive to ground resistance, insulation resistance negative to ground resistance, gun head DC power supply positive pole temperature and gun head DC power supply negative pole temperature.

[0044] Among them, the PCU measurement data record may specifically include: the data generation time and the power station name, centralized control address, equipment index, equipment SN, equipment production date, manufacturer product number, operating status, ambient temperature, gun head DC power supply positive pole temperature, gun head DC power supply negative pole temperature, insulation resistance positive to ground resistance, insulation resistance negative to ground resistance and gun head status.

[0045] The operating state may be, for example, standby, and the gun head state may be, for example, returned.

[0046] Furthermore, on this basis, fields such as software code, charging standard voltage (CC1 voltage), terminal number, product series to which the terminal belongs, and product version to which the terminal belongs can be added. The software code can be parsed from the software version, the CC1 voltage can be 12V, and the terminal number can be obtained from the terminal facilities of the power station corresponding to the power station name through the centralized control address and equipment index. The product series and product version of the terminal can be associated based on the terminal number.

[0047] Among them, the measurement data record can also include the total number of messages, the number of messages with CC1 voltage>=11, the number of messages with normal gun temperature, the number of emergency stop feedbacks, and the duration of emergency stop feedback. There can be multiple charging guns distributed in multiple power stations. The charging guns in multiple power stations can form a charging gun network. The measurement data records of each charging gun can be summarized to analyze the operation of the entire charging gun network. Therefore, the measurement data record can also include: the number of charging orders of the terminal on the same day, the number of fast charging orders on the same power station on the same day, the number of fast charging terminals in the power station, the management company to which the power station belongs, the city to which the power station belongs, the operation type, whether the charging gun is abnormal, the cause of the abnormality, etc.

[0048] Among them, the station characteristic environment can be a heavy truck station, a bus station, etc.

[0049] Step S102: Determine the plugging and unplugging information of the charging gun based on the charging status message information.

[0050] Among them, the plugging and unplugging information of the charging gun can be determined according to the charging gun state change represented by the charging status message information. For example, the state of the charging gun changes from the charging gun standby to the charging gun charging, which can be considered as a plugging of the gun, and the state of the charging gun changes from the charging gun charging to the charging gun idle, which can be considered as a pulling out of the gun. The plugging and unplugging status of the charging gun, that is, the plugging and unplugging information, can be determined through the charging gun state change in the charging status message information.

[0051] Step S103 , performing data cleaning on the target information of the charging gun based on the measurement data record of the charging gun, the characteristic environment of the station, and the plugging and unplugging information.

[0052] Among them, the target information of the charging gun can be CC1 voltage, gun tip temperature, emergency stop feedback duration and number of orders.

[0053] The target information may be cleaned according to a preset time period, for example, the target information may be cleaned on a daily basis, and the cleaned target information may be stored, for example, in a data day table.

[0054] Among them, for the cleaning of target information, only the target information of the charging gun corresponding to the fast charging terminal is cleaned.

[0055] Step S104 , performing abnormality detection on the charging gun based on the target information after data cleaning and a preset abnormality determination method to obtain an abnormality detection result.

[0056] Among them, a threshold value corresponding to the target information can be set in advance, and an abnormality determination is performed based on the threshold value and the target information, thereby obtaining an abnormality detection result.

[0057] For example, the first number threshold, the second number threshold, the duration threshold, the order number threshold, the voltage range threshold and the current range threshold can be set, and the above thresholds can be compared with the number of times the charging standard voltage is greater than or equal to the voltage threshold, the number of times the gun tip temperature is in the preset temperature range, the duration of the emergency stop feedback and the number of orders, the charging voltage and the voltage range threshold, and the charging current and the current range threshold, so as to make an abnormal judgment and obtain an abnormal detection result.

[0058] Specifically, the pre-set judgment method is used to: determine the relationship between the charging standard voltage and the first number threshold, determine the relationship between the gun tip temperature and the second number threshold, determine the relationship between the emergency stop feedback duration and the duration threshold, determine the relationship between the number of orders and the order number threshold, determine the relationship between the charging voltage and the voltage range threshold, and determine the relationship between the charging current and the current range threshold; through the relationship between the charging standard voltage and the first number threshold, the relationship between the gun tip temperature and the second number threshold, the relationship between the emergency stop feedback duration and the duration threshold, the relationship between the number of orders and the order number threshold, the relationship between the charging voltage and the voltage range threshold, and the relationship between the charging current and the current range threshold, an abnormal judgment is jointly performed.

[0059] Among them, the charging voltage and charging current are mainly used to reflect the abnormal situation of the charging pile corresponding to the charging gun.

[0060] Among them, the historical abnormality detection results of each charging gun can be summarized and analyzed in advance to determine the distribution of the charging standard voltage, gun head temperature, emergency stop feedback duration and number of orders when the charging gun is abnormal, and determine the first number threshold, second number threshold, duration threshold and order number threshold based on each distribution. The distribution can be displayed in the form of a dot graph, and the value corresponding to the most densely populated position of the dot graph is the corresponding first number threshold, second number threshold, duration threshold and order number threshold.

[0061] Furthermore, after performing an abnormality detection on the charging gun, an abnormality prediction of the charging gun can be performed based on the abnormality detection result, and the steps may include: if the abnormality detection result indicates that the charging gun is normal, then a first abnormality prediction is performed on the charging gun based on the target information and plug-in information after data cleaning to obtain a first abnormality prediction result; if the abnormality detection result indicates that the charging gun is a first type of abnormality, then an abnormality elimination is performed on the charging pile corresponding to the charging gun to obtain an elimination result; if the elimination result is that there is an abnormality in the charging pile, then the life characteristics of the charging gun are analyzed based on the maintenance and scrapping data of the charging gun acquired in advance; and a second abnormality prediction is performed on the charging gun based on the life characteristics corresponding to the charging gun to obtain a second abnormality prediction result.

[0062] Among them, when there is no abnormality in the charging gun, a first abnormality prediction can be performed on the charging gun, and the first abnormality prediction is a prediction of the abnormality of the charging gun based on the charging standard voltage, the gun head temperature of the charging gun, and the number of times the charging gun is plugged in and out.

[0063] Among them, the first abnormality type is that there are no abnormalities in the charging standard voltage and gun head temperature of the charging gun, but there are abnormalities in the duration of the emergency stop feedback and the number of orders. Under this condition, you can first rule out whether the charging pile corresponding to the charging gun has a fault. Specifically, you can make an abnormal judgment on the charging pile based on the charging voltage and charging current. After starting the self-repair function of the charging pile, if the charging voltage does not meet the voltage range threshold, or the charging current does not meet the current range threshold, it is determined that the exclusion result is that there is an abnormality in the charging pile.

[0064] Among them, the maintenance and scrapping data may include the repair time, scrapping time, reset time, the number of times and location of the charging gun corresponding to the repair time, the number of times and location of the gun pulling out corresponding to the scrapping time, the time and place of starting use, etc.

[0065] Among them, life characteristics may include the time and place when the charging gun starts to be used, as well as the number of times the gun is pulled out, the accumulated time, the maintenance time, the scrapping time, the time when the number of times is reset, etc.

[0066] Specifically, performing a second prediction on the abnormality of the charging gun based on the life characteristics corresponding to the charging gun to obtain a second abnormality prediction result may include: determining the number of repairs and the damage rate of the charging gun based on the life characteristics; performing a second prediction on the abnormality of the charging gun based on the number of repairs and the damage rate to obtain a second abnormality prediction result.

[0067] The abnormality detection method of the charging gun provided in the embodiment of the present invention can obtain the charging status message information, measurement data record and station characteristic environment of the charging gun; determine the plugging and unplugging information of the charging gun based on the charging status message information; perform data cleaning on the target information of the charging gun based on the measurement data record, station characteristic environment and plugging and unplugging information of the charging gun; perform abnormality detection on the charging gun based on the target information after data cleaning and a pre-set abnormality determination method to obtain an abnormality detection result. In this method, a comprehensive abnormality determination is performed on the charging gun, thereby improving the operation and maintenance efficiency, the availability of the station and the user satisfaction.

[0068] Example 2

[0069] The embodiment of the present invention also provides another abnormality detection method for a charging gun. The method is implemented on the basis of the method in the above embodiment. The method focuses on describing the specific implementation method of data cleaning of the target information of the charging gun based on the measurement data record of the charging gun, the characteristic environment of the station and the plug-in information.

[0070] Figure 2 A flowchart of another abnormality detection method for a charging gun provided in an embodiment of the present invention, such as Figure 2 As shown, the data cleaning of the target information of the charging gun based on the measurement data record of the charging gun, the station characteristic environment and the plugging and unplugging information may include the following steps:

[0071] Step S201: Determine whether a terminal in a station characteristic environment corresponding to a charging gun is a fast charging terminal based on the measurement data record.

[0072] Among them, the type of terminal in the station characteristic environment can be determined by measuring the power station name, centralized control address, equipment index, equipment SN and other information in the data record, thereby determining whether the type is a fast charging terminal.

[0073] Step S202: If the terminal in the station characteristic environment corresponding to the charging gun is a fast charging terminal, data cleaning is performed on the target information of the charging gun based on the measurement data record and the plug-in information.

[0074] Among them, since the data of the fast charging terminal is relatively complete, only the target information of the charging gun corresponding to the fast charging terminal is cleaned.

[0075] Specifically, data cleaning of the target information of the charging gun based on the measurement data record and the plug-in information may include the following steps:

[0076] Step A1, determining the number of plugging and unplugging times and plugging and unplugging detailed data records corresponding to the charging gun based on the plugging and unplugging information.

[0077] Among them, the plug-in and plug-out detailed records can be determined according to the plug-in and plug-out information, such as the charging gun idle-the charging gun charging-the charging gun idle. The plug-in and plug-out times can be determined according to the plug-in and plug-out detailed records. For example, the status of the charging gun changes from the charging gun standby to the charging gun charging, which can be considered as one plug-in, and the status of the charging gun changes from the charging gun charging to the charging gun idle, which can be considered as one unplugging.

[0078] Step A2: Clean the target information corresponding to the number of plugging and unplugging times in the measurement data record based on the plugging and unplugging detailed data record.

[0079] Among them, the target information corresponding to the number of plugging and unplugging times can be understood as the duration of the emergency stop feedback and the number of orders up to the current number of plugging and unplugging times, as well as the CC1 voltage and gun tip temperature when plugging and unplugging the charging gun.

[0080] The abnormality detection method for a charging gun provided in an embodiment of the present invention can clean the target information to filter out the data required for abnormality determination of the charging gun, thereby avoiding the tediousness of data comparison and improving the efficiency of abnormality detection.

[0081] Example 3

[0082] The embodiment of the present invention also provides another abnormality detection method for a charging gun, which is implemented on the basis of the method in the above embodiment.

[0083] Figure 3A flowchart of another abnormality detection method for a charging gun provided in an embodiment of the present invention is shown in FIG. Figure 3 As shown, the abnormality detection method of the charging gun may include the following steps:

[0084] Step S301, obtaining the charging status message information, measurement data records and station characteristic environment of the charging gun.

[0085] Step S302: Determine the plugging and unplugging information of the charging gun based on the charging status message information.

[0086] Specifically, determining the plugging and unplugging information of the charging gun based on the charging status message information may include the following steps:

[0087] Step B1, determining the current state and the previous state of the charging gun based on the charging state message information.

[0088] Among them, the current state and the previous state can both include the charging gun being charged, the charging gun being idle, the charging gun being faulty, the charging gun being off the grid, and the charging gun being on standby.

[0089] Step B2, recording the current state and the previous state to form a plug-in and plug-out detailed data record.

[0090] Among them, the plug-in and pull-out detailed data record can be understood as a record of charging gun status changes within a period of time, for example, it can be charging gun standby-charging gun charging-charging gun idle.

[0091] Step B3: determining the number of plugging and unplugging times of the charging gun based on each current state and the previous state corresponding to each current state in the plugging and unplugging detailed data record.

[0092] Among them, for example, the state of the charging gun changes from standby to charging, which can be considered as one plugging in, and the state of the charging gun changes from charging to idle, which can be considered as one unplugging. The state of the charging gun in the plugging and unplugging detail data record can be analyzed to determine the number of plugging and unplugging times of the charging gun.

[0093] Step B4, record the plugging and unplugging details and the plugging and unplugging times as plugging and unplugging information.

[0094] Step S303 , performing data cleaning on the target information of the charging gun based on the measurement data record of the charging gun, the characteristic environment of the station, and the plugging and unplugging information.

[0095] Step S304, if the number of times that the charging standard voltage in the target information after data cleaning is greater than or equal to the voltage threshold within the preset time satisfies the first number threshold in the abnormality determination method, the abnormality detection result is that the first abnormality occurs.

[0096] The preset time may be within the past three days.

[0097] Among them, the voltage threshold can be 11V. If the charging standard voltages of the charging status message information within the preset time are all greater than or equal to the voltage threshold, it is considered that an abnormality has occurred. If there is at least one charging standard voltage among the charging standard voltages of the charging status message information within the preset time that is lower than the voltage threshold, then the charging standard voltage is considered to be normal.

[0098] Step S305, if the number of times the temperature of the gun tip in the target information after data cleaning is in the preset temperature range within the preset time meets the second number threshold in the abnormality determination method, the abnormality detection result is that no abnormality occurs, otherwise the abnormality detection result is that the second abnormality occurs.

[0099] Among them, the temperature range is (-40°C, 130°C). If at least one gun tip temperature in the gun tip temperature of the charging status message information within the preset time is within the temperature range, it can be considered that the gun tip temperature is normal, and the rest are abnormal.

[0100] Step S306, if the duration of the emergency stop feedback in the target information after data cleaning within the preset time meets the duration threshold in the abnormality determination method, the abnormality detection result is that the third abnormality occurs.

[0101] Among them, the number of emergency stop fault codes of the terminal and the duration of the fault can be captured. If the terminal has been a charger fault and the emergency stop fault code has been an emergency stop fault within a preset time, it is considered that an abnormality has occurred.

[0102] Step S307, if the number of orders in the target information after data cleaning within the preset time does not meet the order number threshold in the abnormal judgment method, and the order number of the remaining charging guns in the station characteristic environment corresponding to the charging gun all meet the order number threshold, then the abnormal detection result is the occurrence of the fourth abnormality.

[0103] Among them, the number of charging orders for the terminal on that day is captured, specifically by starting charging to capture the orders, and the number of orders for the same day for other terminals at the same power station is obtained. If the number of orders for the terminal on that day is equal to 0 within the preset time, and the number of orders for the same day for other terminals at the same power station are greater than or equal to 1, it is considered that an abnormality has occurred.

[0104] Step S308, if the charging voltage in the target information after data cleaning within the preset time does not meet the preset voltage range threshold, the abnormality detection result is that the fifth abnormality occurs.

[0105] Among them, for passenger cars, the voltage range threshold is [200V, 500V]; for commercial vehicles, the voltage range threshold is [400V, 750V].

[0106] Step S309, if the charging current in the target information after data cleaning within the preset time does not meet the preset current range threshold, the abnormality detection result is that the sixth abnormality occurs.

[0107] Among them, for passenger cars, the current range threshold is [1A, 400A]; for commercial vehicles, the current range threshold is [1A, 500A].

[0108] It should be noted that if the abnormal detection result does not include the first abnormality, the second abnormality, the third abnormality, the fourth abnormality, the fifth abnormality and the sixth abnormality, the abnormal detection result characterizes that the charging gun is normal; if the abnormal detection result does not include the first abnormality and the second abnormality, and includes the third abnormality and the fourth abnormality, the abnormal detection result characterizes that the charging gun is a first type of abnormality; if the abnormal detection result does not include the first abnormality, the second abnormality, the third abnormality and the fourth abnormality, the abnormal detection result characterizes that the charging gun is a second type of abnormality.

[0109] Furthermore, after obtaining the abnormal detection result, the abnormal detection result and the measurement data record of the charging gun are stored in correspondence, and then the correctness of the abnormal detection result is verified. If the abnormal detection result is correct, the operation and maintenance team of the charging gun is warned based on the location association information corresponding to the abnormal detection result.

[0110] The measurement data record includes the location-related information of the charging gun, which includes: data date, power station name, centralized control address, terminal number, device type and device unique identification number.

[0111] Among them, the abnormal data corresponding to the abnormal detection result can be extracted and reviewed. If the abnormal data is still abnormal after review, the abnormal detection result is correct. If the abnormal data is normal after review, the abnormal detection result corresponding to the abnormal data is adjusted.

[0112] Among them, you can first aggregate according to the management company or operation and maintenance company corresponding to the charging gun, and notify the abnormal situation of the charging gun. Specifically, you can accurately notify the terminal corresponding to the charging gun where the abnormality occurs, and notify the management personnel of the management company or operation and maintenance company. It also supports joint query to the abnormal charging gun query report, and then provide aggregation by power station or operation and maintenance personnel, and notify the operation and maintenance person in charge of the power station.

[0113] Furthermore, the number of power stations and terminals corresponding to the management company or operation and maintenance company can be counted, and the number of terminals and power stations with abnormal charging guns can be added to the early warning reminder.

[0114] Furthermore, the life characteristics of the charging gun can be analyzed based on the maintenance and scrapping data of the charging gun acquired in advance; based on the corresponding life characteristics of the charging gun, the corresponding abnormal detection results and the corresponding measurement data records, an operation and maintenance work order corresponding to the charging gun is generated.

[0115] Among them, life characteristics may include the time and place when the charging gun starts to be used, as well as the number of times the gun is pulled out, the accumulated time, the maintenance time, the scrapping time, the time when the number of times is reset, etc.

[0116] Among them, when generating an operation and maintenance work order, the description of the operation and maintenance work order can refer to specific information such as the fault site. For example, there is an abnormality in the charging gun of charging station A, the centralized control address is B, the CAN address is C, the terminal name is D, the equipment identification is E, the cause of the abnormality is F, and the number of warnings is G. A, B, C, D, E, F, and G are exemplary substitutes, and the specific judgment is made based on the actual abnormal location and abnormal situation.

[0117] Among them, you can first determine whether there is an unfinished operation and maintenance work order for the power station corresponding to the charging gun. If so, you can update the operation and maintenance work order corresponding to the charging gun into the operation and maintenance work order corresponding to the power station, and update the operation and maintenance work order according to the maintenance time and maintenance situation each time maintenance is performed.

[0118] Furthermore, the life cycle function diagram of the charging gun can be determined based on the number of times the charging gun is plugged in and out, the damage rate of the charging gun, the number of repairs of the charging gun, the number of warnings of the charging gun, etc., so that the operation and maintenance personnel have a prior understanding of the situation of abnormal charging guns.

[0119] Specifically, the cleaned data can be input into a pre-trained machine learning model to output a trend chart of the number of plugging and unplugging times of the charging gun. The trend chart of the number of plugging and unplugging times can include a daily trend chart, a monthly trend chart, and an annual trend chart. The vertical axis of the trend chart of the number of plugging and unplugging times is the number of plugging and unplugging times, and the horizontal axis can be the management company, city, operation type, station, charging pile, etc. to which the power station belongs.

[0120] By counting the correct data of the abnormal detection results, the number of abnormalities of the charging gun can be obtained, and then an abnormal trend graph with the horizontal axis being the number of plug-in and unplugging times and the vertical axis being the number of abnormalities can be output.

[0121] Similarly, the cleaned data can be input into a pre-trained machine learning model to output a trend chart of the number of orders for the charging gun, and then an abnormal trend chart of the number of orders and the number of abnormalities can be output; the cleaned data can be input into a pre-trained machine learning model to output a trend chart of the charging standard voltage of the charging gun, and then an abnormal trend chart of the charging standard voltage and the number of abnormalities can be output.

[0122] Among them, by comparing the above-mentioned abnormal trend charts individually or in combination with the actual situation, the most accurate abnormal factors are determined, and the most accurate abnormal factors are used as comprehensive factors to output the trend chart of abnormal identification of the charging gun, wherein the horizontal axis is the indicator corresponding to the comprehensive factor, and the vertical axis is the number of abnormalities.

[0123] Among them, combined with the information such as the number of maintenance of the charging gun, the life cycle function diagram is obtained. For easy understanding, Figure 4 A life cycle function diagram provided by an embodiment of the present invention, such as Figure 4 As shown, by clicking the corresponding position, the corresponding information for each maintenance can be displayed.

[0124] Furthermore, the first number threshold, the second number threshold, the duration threshold and the order number threshold may also be determined based on the nodes where abnormalities occur in the above trend graphs.

[0125] Step S310: If the abnormality detection result indicates that the charging gun is normal, a first abnormality prediction is performed on the charging gun based on the target information and the plug-in information after data cleaning to obtain a first abnormality prediction result.

[0126] Specifically, performing a first abnormality prediction on the charging gun based on the target information and the plug-in information after data cleaning to obtain a first abnormality prediction result may include:

[0127] Step C1, determining the charging standard voltage level of the charging gun based on the charging standard voltage in the target information after data cleaning, a preset first prediction threshold interval and the number of orders.

[0128] Among them, the first prediction threshold interval is 11V, which can determine the number of orders with a charging standard voltage greater than 11V and the number of orders with a charging standard voltage less than 11V. When the charging standard voltage of all orders is greater than 11V, it is considered that the charging standard voltage level is the first level. When the percentage of the number of orders with a charging standard voltage less than 11V to the total number of orders meets the preset threshold percentage, for example, greater than or equal to 50% and less than 100%, the charging standard voltage level is determined to be the second level. When the percentage of the number of orders with a charging standard voltage less than 11V to the total number of orders does not meet the preset threshold percentage, for example, less than 50% and greater than 0, the charging standard voltage level is determined to be the third level. When the charging standard voltage of all orders is less than 11V, it is considered that the charging standard voltage level is the fourth level.

[0129] It should be noted that the higher the level number, the lower the health of the charging gun.

[0130] Step C2, determining the gun tip temperature level based on the gun tip temperature in the target information after data cleaning, a preset second prediction threshold interval and the number of orders.

[0131] Among them, the second prediction threshold interval is (-30℃, 120℃). When it can be determined that the number of orders with gun tip temperatures that meet (-30℃, 120℃), the number of orders that meet (-40℃, -30℃), and the number of orders that meet (120℃, 130℃), and the gun tip temperatures of all orders meet (-30℃, 120℃), the gun tip temperature level is considered to be the first level. When the number of orders with gun tip temperatures that meet (-40℃, -30℃) and (120℃, 130℃) and the total number of orders are When the percentage meets the preset threshold percentage, for example, greater than or equal to 50% and less than 100%, the gun tip temperature level is determined to be the second level; when the percentage of the number of orders whose gun tip temperatures meet (-40°C, -30°C) and (120°C, 130°C) to the total number of orders does not meet the preset threshold percentage, for example, less than 50% and greater than 0, the gun tip temperature level is determined to be the third level; when there are orders with gun tip temperatures less than -40°C or greater than 130°C, the gun tip temperature level is determined to be the fourth level.

[0132] Step C3, determining the plug-in number level based on the plug-in number in the plug-in information and a preset third prediction threshold interval.

[0133] Among them, the third prediction threshold interval is (1 time, 5000 times). When the plug-in and unplugging times meet (1 time, 4000 times), the plug-in and unplugging times level can be considered to be the first level; when the plug-in and unplugging times meet (4000 times, 5000 times), the plug-in and unplugging times level can be considered to be the second level; when the plug-in and unplugging times meet (5000 times, 10000 times), the plug-in and unplugging times level can be considered to be the third level; when the plug-in and unplugging times are greater than 10000 times, the plug-in and unplugging times level can be considered to be the fourth level.

[0134] Step C4, performing a first abnormality prediction on the charging gun based on the charging standard voltage level, the gun head temperature level, the plugging and unplugging times level and a preset level coefficient, to obtain a first abnormality prediction result.

[0135] Specifically, performing a first abnormality prediction on the charging gun based on the charging standard voltage level, the gun head temperature level, the plugging and unplugging times level, and a preset coefficient threshold interval to obtain a first abnormality prediction result may include:

[0136] Step C41, determining the charging standard voltage level coefficient corresponding to the charging gun based on the charging standard voltage level and a preset level coefficient.

[0137] Among them, the charging standard voltage level coefficient can be determined according to a preset level coefficient and a charging standard voltage level comparison table.

[0138] Step C42, determining the gun tip temperature grade coefficient corresponding to the charging gun based on the gun tip temperature grade and a preset grade coefficient.

[0139] The gun tip temperature grade coefficient may be determined according to a preset grade coefficient and a gun tip temperature grade comparison table.

[0140] Step C43, determining the plug-in times level coefficient corresponding to the charging gun based on the plug-in times level and the preset level coefficient.

[0141] The plugging and unplugging times grade coefficient may be determined according to a preset grade coefficient and the plugging and unplugging times grade.

[0142] Step C44, determining the comprehensive level coefficient of the charging gun based on the charging standard voltage level coefficient, the gun head temperature level coefficient, the plugging and unplugging times level coefficient and a preset weight ratio.

[0143] Among them, the number of plugging and unplugging times has the greatest impact on the charging standard voltage, the gun tip temperature, and the number of plugging and unplugging times. The impacts of the charging standard voltage and the gun tip temperature are similar. The weight ratio can be set to 50% for the number of plugging and unplugging times, and 25% for the charging standard voltage and the gun tip temperature.

[0144] Step C45, determining the first abnormality prediction result based on the comprehensive grade coefficient and the coefficient threshold interval.

[0145] The coefficient threshold intervals may be set to three, and each coefficient threshold interval corresponds to a first abnormal prediction result.

[0146] Among them, the first abnormal prediction result includes: the charging gun is currently damaged, the charging gun is currently in normal use, and the charging gun is damaged within the predicted time.

[0147] Among them, the prediction time can be predicted and determined based on the life cycle diagram of the charging gun.

[0148] Step S311: if the abnormality detection result indicates that the charging gun is of the first type of abnormality, then the charging pile corresponding to the charging gun is subjected to abnormality elimination to obtain an elimination result.

[0149] Specifically, the charging pile corresponding to the charging gun is subjected to abnormality elimination to obtain an elimination result, which may include: eliminating abnormalities of the charging pile corresponding to the charging gun based on the charging voltage and the charging current; after starting the self-repair function of the charging pile, if the charging voltage does not meet the voltage range threshold, or the charging current does not meet the current range threshold, then it is determined that the elimination result is that there is an abnormality in the charging pile.

[0150] Step S312: If the exclusion result is that the charging pile is abnormal, the life characteristics of the charging gun are analyzed based on the maintenance and scrapping data of the charging gun obtained in advance.

[0151] Step S313: Perform a second prediction of the abnormality of the charging gun based on the life characteristics corresponding to the charging gun to obtain a second abnormality prediction result.

[0152] Specifically, performing a second prediction on the abnormality of the charging gun based on the life characteristics corresponding to the charging gun to obtain a second abnormality prediction result may include: determining the number of repairs and the damage rate of the charging gun based on the life characteristics; performing a second prediction on the abnormality of the charging gun based on the number of repairs and the damage rate to obtain a second abnormality prediction result.

[0153] Among them, the approximate damage time of the charging gun can be determined based on the number of maintenance times and damage rates in the life cycle diagram of multiple charging guns.

[0154] The abnormality detection method for a charging gun provided in an embodiment of the present invention can make an abnormality judgment on the charging gun by combining the duration of the emergency stop feedback and the number of orders under the current number of plugging and unplugging times related to the charging gun, as well as the CC1 voltage, gun head temperature and other information when the charging gun is plugged and unplugged, with an abnormal judgment method. This makes the abnormality judgment of the charging gun not limited to the abnormal temperature of the gun head, provides a basis for the abnormality detection of the charging gun, and then performs abnormality prediction, thereby realizing abnormal maintenance before the charging gun becomes abnormal, and warning of the abnormal situation of the charging gun through an early warning, and notifying and updating the repair progress in the form of an operation and maintenance work order, thereby improving the operation and maintenance work efficiency, the availability of the site and user satisfaction.

[0155] Example 4

[0156] Corresponding to the above method embodiment, an embodiment of the present invention provides an abnormality detection device for a charging gun, Figure 5 A schematic diagram of the structure of an abnormality detection device for a charging gun provided by an embodiment of the present invention is shown in FIG. Figure 5 As shown, the abnormality detection device of the charging gun may include:

[0157] The information acquisition module 401 is used to obtain the charging status message information, measurement data records and station characteristic environment of the charging gun.

[0158] The plug-in information determination module 402 is used to determine the plug-in information of the charging gun based on the charging status message information.

[0159] The data cleaning module 403 is used to clean the target information of the charging gun based on the measurement data record of the charging gun, the characteristic environment of the site and the plug-in information.

[0160] The abnormality determination module 404 is used to perform abnormality detection on the charging gun based on the target information after data cleaning and a preset abnormality determination method to obtain an abnormality detection result.

[0161] The abnormality detection device for a charging gun provided in an embodiment of the present invention can obtain the charging status message information, measurement data records and station characteristic environment of the charging gun; determine the plugging and unplugging information of the charging gun based on the charging status message information; perform data cleaning on the target information of the charging gun based on the measurement data records, station characteristic environment and plugging and unplugging information of the charging gun; perform abnormality detection on the charging gun based on the target information after data cleaning and a pre-set abnormality determination method to obtain an abnormality detection result. In this method, a comprehensive abnormality determination is performed on the charging gun, thereby improving the operation and maintenance efficiency, the availability of the station and the user satisfaction.

[0162] In some embodiments, the abnormality determination module 404 is also used to, if the abnormality detection result indicates that the charging gun is normal, perform a first abnormality prediction on the charging gun based on the target information and plug-in information after data cleaning to obtain a first abnormality prediction result; if the abnormality detection result indicates that the charging gun is a first type of abnormality, perform abnormality elimination on the charging pile corresponding to the charging gun to obtain an elimination result; if the elimination result is that there is an abnormality in the charging pile, analyze the life characteristics of the charging gun based on the maintenance and scrapping data of the charging gun acquired in advance; perform a second prediction on the abnormality of the charging gun based on the life characteristics corresponding to the charging gun to obtain a second abnormality prediction result.

[0163] In some embodiments, the plug-in information determination module is also used to determine the current state and the previous state of the charging gun based on the charging state message information; record the current state and the previous state to form a plug-in detail data record; determine the number of plug-ins and unplugs of the charging gun based on each current state in the plug-in detail data record and the previous state corresponding to each current state; use the plug-in detail data record and the number of plug-ins and unplugs as plug-in information.

[0164] In some embodiments, the data cleaning module is also used to determine whether the terminal in the station characteristic environment corresponding to the charging gun is a fast charging terminal based on the measurement data record; if the terminal in the station characteristic environment corresponding to the charging gun is a fast charging terminal, the plug-in and unplugging times and the plug-in and unplugging detail data record corresponding to the charging gun are determined based on the plug-in and unplugging information; and the target information corresponding to the plug-in and unplugging times in the measurement data record is cleaned based on the plug-in and unplugging detail data record.

[0165] In some embodiments, the abnormality determination module is also used to: if the number of times that the charging standard voltage in the target information after data cleaning within the preset time is greater than or equal to the voltage threshold satisfies the first number threshold in the abnormality determination method, then the abnormality detection result is the occurrence of the first abnormality; if the number of times that the gun tip temperature in the target information after data cleaning within the preset time is in the preset temperature range satisfies the second number threshold in the abnormality determination method, then the abnormality detection result is that no abnormality has occurred, otherwise the abnormality detection result is that the second abnormality has occurred; if the target information after data cleaning within the preset time satisfies the duration threshold in the abnormality determination method, then the abnormality detection result is the occurrence of the third abnormality; if the number of orders in the target information after data cleaning within the preset time does not meet the order number threshold in the abnormality determination method, and the number of orders of the remaining charging guns in the station characteristic environment corresponding to the charging gun all meet the order number threshold, then the abnormality detection result is the occurrence of the fourth abnormality; if the charging voltage in the target information after data cleaning within the preset time does not meet the preset voltage range threshold, then the abnormality detection result is the occurrence of the fifth abnormality; if the charging current in the target information after data cleaning within the preset time does not meet the preset current range threshold, then the abnormality detection result is the occurrence of the sixth abnormality.

[0166] In some embodiments, if the abnormal detection result does not include the first abnormality, the second abnormality, the third abnormality, the fourth abnormality, the fifth abnormality and the sixth abnormality, the abnormal detection result characterizes that the charging gun is normal; if the abnormal detection result does not include the first abnormality and the second abnormality, and includes the third abnormality and the fourth abnormality, the abnormal detection result characterizes that the charging gun is a first type of abnormality; if the abnormal detection result does not include the first abnormality, the second abnormality, the third abnormality and the fourth abnormality, the abnormal detection result characterizes that the charging gun is a second type of abnormality.

[0167] In some embodiments, the first abnormal prediction result determination module is also used to determine the charging standard voltage level of the charging gun based on the charging standard voltage in the target information after data cleaning, a pre-set first prediction threshold interval and the number of orders; determine the gun tip temperature level based on the gun tip temperature in the target information after data cleaning, a pre-set second prediction threshold interval and the number of orders; determine the plug-in number level based on the plug-in number in the plug-in information and a pre-set third prediction threshold interval; perform a first abnormal prediction on the charging gun based on the charging standard voltage level, the gun tip temperature level, the plug-in number level and the pre-set level coefficient to obtain a first abnormal prediction result.

[0168] In some embodiments, the first abnormal prediction result determination module is also used to determine the charging standard voltage level coefficient corresponding to the charging gun based on the charging standard voltage level and a pre-set level coefficient; determine the gun head temperature level coefficient corresponding to the charging gun based on the gun head temperature level and a pre-set level coefficient; determine the plug-in number level coefficient corresponding to the charging gun based on the plug-in number level and a pre-set level coefficient; determine the comprehensive level coefficient of the charging gun based on the charging standard voltage level coefficient, the gun head temperature level coefficient, the plug-in number level coefficient and a pre-set weight ratio; determine the first abnormal prediction result based on the comprehensive level coefficient and the coefficient threshold range; the first abnormal prediction result includes: the charging gun is currently damaged, the charging gun is currently in normal use, and the charging gun is damaged within the predicted time.

[0169] In some embodiments, the abnormality elimination module is also used to eliminate abnormalities of the charging pile corresponding to the charging gun based on the charging voltage and the charging current; after starting the self-repair function of the charging pile, if the charging voltage does not meet the voltage range threshold, or the charging current does not meet the current range threshold, the elimination result is determined to be that there is an abnormality in the charging pile.

[0170] In some embodiments, the second abnormal prediction result determination module is also used to determine the number of maintenance times and damage rate of the charging gun based on life characteristics; perform a second prediction of the abnormality of the charging gun based on the number of maintenance times and the damage rate to obtain a second abnormal prediction result.

[0171] In some embodiments, the abnormality determination module is also used to store the abnormality detection results and the measurement data records of the charging gun in correspondence; the measurement data records include location-related information of the charging gun; the location-related information includes: data date, power station name, centralized control address, terminal number, equipment type and equipment unique identification number; the correctness of the abnormality detection result is verified; if the abnormality detection result is correct, an early warning reminder is given to the operation and maintenance team of the charging gun based on the location-related information corresponding to the abnormality detection result.

[0172] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0173] Example 5

[0174] The embodiment of the present invention also provides an electronic device for executing the abnormality detection method of the charging gun; see Figure 6 A structural schematic diagram of an electronic device is shown, the electronic device includes a memory 500 and a processor 501, wherein the memory 500 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor 501 to implement the above-mentioned abnormality detection method of the charging gun.

[0175] Further, Figure 6 The electronic device shown further includes a bus 502 and a communication interface 503 , and the processor 501 , the communication interface 503 and the memory 500 are connected via the bus 502 .

[0176] The memory 500 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 503 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 502 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0177] The processor 501 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 501. The above processor 501 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 500, and the processor 501 reads the information in the memory 500 and completes the steps of the method of the above embodiment in combination with its hardware.

[0178] An embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the above-mentioned abnormality detection method for the charging gun. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0179] The computer program product of the method for detecting abnormality of a charging gun provided in an embodiment of the present invention includes a computer-readable storage medium storing a non-volatile program code executable by a processor. The instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0180] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0181] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0182] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0183] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0184] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0185] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for detecting abnormality of a charging gun, characterized in that: The method comprises: Obtain the charging status message information, measurement data records and station characteristic environment of the charging gun; Determine the plugging and unplugging information of the charging gun based on the charging status message information; Performing data cleaning on target information of the charging gun based on the measurement data record of the charging gun, the characteristic environment of the station and the plugging and unplugging information; Based on the target information after data cleaning and a preset abnormality determination method, an abnormality detection is performed on the charging gun to obtain an abnormality detection result.

2. The method according to claim 1, characterized in that The method further comprises: If the abnormality detection result indicates that the charging gun is normal, a first abnormality prediction is performed on the charging gun based on the target information after data cleaning and the plugging and unplugging information to obtain a first abnormality prediction result; If the abnormality detection result indicates that the charging gun is of the first type of abnormality, performing abnormality elimination on the charging pile corresponding to the charging gun to obtain an elimination result; If the exclusion result is that the charging pile is abnormal, analyzing the life characteristics of the charging gun based on the maintenance and scrapping data of the charging gun acquired in advance; A second abnormality prediction is performed on the charging gun based on the life characteristics corresponding to the charging gun to obtain a second abnormality prediction result.

3. The method according to claim 2, characterized in that The determining the plugging and unplugging information of the charging gun based on the charging status message information includes: Determine the current state and the previous state of the charging gun based on the charging state message information; Record the current state and the previous state to form a plug-in and pull-out detailed data record; Determine the number of plugging and unplugging of the charging gun based on each current state in the plugging and unplugging detailed data record and a previous state corresponding to each current state; The plugging and unplugging detailed data record and the plugging and unplugging times are used as the plugging and unplugging information.

4. The method according to claim 3, characterized in that The performing data cleaning on the target information of the charging gun based on the measurement data record of the charging gun, the station characteristic environment and the plugging and unplugging information includes: Determine whether the terminal in the station characteristic environment corresponding to the charging gun is a fast charging terminal based on the measurement data record; If the terminal in the station characteristic environment corresponding to the charging gun is a fast charging terminal, the number of plugging and unplugging times and plugging and unplugging detailed data records corresponding to the charging gun are determined based on the plugging and unplugging information; Based on the plug-in and plug-out detail data record, data cleaning is performed on the target information corresponding to the plug-in and plug-out times in the measurement data record.

5. The method according to claim 4, characterized in that The abnormality determination of the charging gun based on the target information after data cleaning and the preset abnormality determination method to obtain the abnormality detection result includes: If the number of times that the charging standard voltage in the target information after data cleaning is greater than or equal to the voltage threshold within the preset time satisfies the first number threshold in the abnormality determination method, the abnormality detection result is that a first abnormality occurs; If the number of times that the temperature of the gun tip in the target information after data cleaning is in the preset temperature range within the preset time meets the second number threshold in the abnormality determination method, the abnormality detection result is that no abnormality has occurred, otherwise the abnormality detection result is that the second abnormality has occurred; If the target information after data cleaning within the preset time meets the time threshold in the abnormality determination method, the abnormality detection result is that a third abnormality occurs; If the number of orders in the target information after data cleaning within the preset time does not meet the order number threshold in the abnormality determination method, and the order numbers of the remaining charging guns in the station characteristic environment corresponding to the charging gun all meet the order number threshold, then the abnormality detection result is that a fourth abnormality occurs; If the charging voltage in the target information after data cleaning within the preset time does not meet the preset voltage range threshold, the abnormality detection result is that the fifth abnormality occurs; If the charging current in the target information after data cleaning within the preset time does not meet the preset current range threshold, the abnormality detection result is that the sixth abnormality occurs.

6. The method according to claim 5, characterized in that If the abnormality detection result does not include the first abnormality, the second abnormality, the third abnormality, the fourth abnormality, the fifth abnormality and the sixth abnormality, the abnormality detection result indicates that the charging gun has no abnormality; If the abnormality detection result does not include the first abnormality and the second abnormality, and includes the third abnormality and the fourth abnormality, then the abnormality detection result indicates that the charging gun is of the first type of abnormality; If the abnormality detection result does not include the first abnormality, the second abnormality, the third abnormality, and the fourth abnormality, the abnormality detection result indicates that the charging gun is of the second type of abnormality.

7. The method according to claim 5, characterized in that The performing a first abnormality prediction on the charging gun based on the target information after the data cleaning and the plugging and unplugging information to obtain a first abnormality prediction result includes: Determining the charging standard voltage level of the charging gun based on the charging standard voltage in the target information after data cleaning, the preset first prediction threshold interval and the number of orders; Determine the temperature level of the gun tip based on the gun tip temperature in the target information after the data cleaning, the preset second prediction threshold interval and the number of orders; Determining a plug-in number level based on the plug-in number in the plug-in information and a preset third prediction threshold interval; A first abnormality prediction is performed on the charging gun based on the charging standard voltage level, the gun head temperature level, the plugging and unplugging times level and a preset level coefficient to obtain a first abnormality prediction result.

8. The method according to claim 7, characterized in that The performing a first abnormality prediction on the charging gun based on the charging standard voltage level, the gun head temperature level, the plugging and unplugging times level and a preset coefficient threshold interval to obtain a first abnormality prediction result includes: Determine a charging standard voltage level coefficient corresponding to the charging gun based on the charging standard voltage level and a preset level coefficient; Determine a gun tip temperature grade coefficient corresponding to the charging gun based on the gun tip temperature grade and a preset grade coefficient; Determine a plug-in / plug-out number level coefficient corresponding to the charging gun based on the plug-in / plug-out number level and a preset level coefficient; Determine the comprehensive grade coefficient of the charging gun based on the charging standard voltage grade coefficient, the gun head temperature grade coefficient, the plugging and unplugging times grade coefficient and a preset weight ratio; Based on the comprehensive level coefficient and the coefficient threshold interval, the first abnormal prediction result is determined; the first abnormal prediction result includes: the charging gun is currently damaged, the charging gun is currently in normal use, and the charging gun is damaged within the predicted time.

9. The method according to claim 5, characterized in that The abnormality elimination of the charging pile corresponding to the charging gun to obtain the elimination result includes: Eliminate abnormalities in the charging pile corresponding to the charging gun based on the charging voltage and the charging current; After the self-repair function is started for the charging pile, if the charging voltage does not meet the voltage range threshold, or the charging current does not meet the current range threshold, it is determined that the exclusion result is that there is an abnormality in the charging pile.

10. The method according to claim 5, characterized in that The performing a second prediction on the abnormality of the charging gun based on the life feature corresponding to the charging gun to obtain a second abnormality prediction result includes: Determining the number of maintenance times and the damage rate of the charging gun based on the life characteristics; A second abnormality prediction is performed on the charging gun based on the number of maintenance times and the damage rate to obtain a second abnormality prediction result.

11. The method according to claim 2, characterized in that After performing abnormality detection on the charging gun based on the target information after data cleaning and the preset abnormality determination method to obtain the abnormality detection result, the method further includes: The abnormal detection result is stored correspondingly with the measurement data record of the charging gun; the measurement data record includes the location association information of the charging gun; the location association information includes: data date, power station name, centralized control address, terminal number, equipment type and equipment unique identification number; Verifying the correctness of the abnormality detection result; If the abnormality detection result is correct, an early warning reminder is given to the operation and maintenance team of the charging gun based on the position association information corresponding to the abnormality detection result.

12. An abnormality detection device for a charging gun, characterized in that: The device comprises: Information acquisition module, used to obtain the charging status message information, measurement data records and station characteristic environment of the charging gun; A plug-in information determination module, used to determine the plug-in information of the charging gun based on the charging status message information; A data cleaning module, used for cleaning the target information of the charging gun based on the measurement data record of the charging gun, the characteristic environment of the station and the plugging and unplugging information; The abnormality determination module is used to perform abnormality detection on the charging gun based on the target information after data cleaning and a preset abnormality determination method to obtain an abnormality detection result.

13. An electronic device, characterized in that: The invention comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the abnormality detection method of the charging gun according to any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to implement the abnormality detection method for the charging gun according to any one of claims 1 to 11.

15. A computer program product, characterized in that A computer-readable storage medium storing a non-volatile program code executable by a processor, wherein the program code includes instructions for executing the abnormality detection method for a charging gun according to any one of claims 1 to 11.

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