Method, device, equipment and medium for handling hazardousness of charging equipment

Abstract

The present invention discloses a method, apparatus, device, and medium for handling the criticality of charging equipment. The method includes: determining criticality measurement factors and fuzzy levels of the criticality measurement factors for the fault type based on the historical operation records of the charging equipment; determining fuzzy level membership intervals for the fuzzy levels of the factors within a trapezoidal membership function; generating the trapezoidal membership function based on the correlation between the fuzzy levels of the factors and the criticality values; determining the membership fuzziness and total fuzziness of the membership relationships corresponding to the fuzzy levels of the factors based on the fuzzy level membership intervals; and determining the gray correlation degree of the fault type based on the membership fuzziness and total fuzziness of the fuzzy levels of the factors. Embodiments of the present invention can accurately measure the criticality of faults.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis, and in particular to a method, device, equipment and medium for handling the hazards of charging equipment. Background Art

[0002] With the increasing popularity of electric vehicles and the large-scale operation of charging equipment, the operation and maintenance of charging stations are receiving increasing attention from operators. Charging equipment operation and maintenance is a crucial process for ensuring the charging experience and profitability. Analyzing the hazard potential of charging equipment failures during operation and making operational and maintenance decisions are crucial tasks and a pressing need for charging operators.

[0003] Currently, the operation and maintenance of charging equipment primarily relies on decentralized operations and planned overhauls, supplemented by post-event repairs, performed according to operational work quotas. Due to the widespread and fragmented distribution of charging stations, maintenance personnel struggle to respond to on-site failures. Furthermore, charging equipment, a highly integrated and automated system integrating multiple disciplines, including electrical, automation, software, and mechanical engineering, features closely interconnected systems. Failure types and their severity are therefore interrelated. Traditional post-event repair and planned overhauls not only impact operational efficiency but also fail to effectively eliminate the risk factors associated with failures and ensure equipment reliability.

[0004] Conducting a criticality analysis of charging equipment failures allows for a comprehensive assessment of the severity and probability of each failure type across all subsystems and components of the charging equipment, allowing maintenance decisions to be made based on the combined impact of these two factors. Currently, most charging stations have a short operating life, resulting in insufficient accumulated failure data. Furthermore, operators and maintenance personnel vary widely in their experience. Accurately quantifying the probability and severity of equipment failures is difficult in actual equipment maintenance, hindering effective guidance. Therefore, measuring the criticality of failures, both in terms of probability and severity, is essential to assist in operational decision-making. Summary of the Invention

[0005] The present invention provides a method, device, equipment and medium for handling the hazard of charging equipment, so as to achieve accurate measurement of the hazard of faults.

[0006] According to one aspect of the present invention, a method for handling hazardousness of a charging device is provided, the method comprising:

[0007] Determine the criticality measurement factors of the fault types and the fuzzy levels of the criticality measurement factors based on the historical operation records of the charging equipment;

[0008] Determining a fuzzy level membership interval of the fuzzy level of the factor in a trapezoidal membership function; the trapezoidal membership function is generated according to the correlation between the fuzzy level of the factor and the hazard value;

[0009] Determining the membership fuzziness and total fuzziness of the membership relationship corresponding to the factor fuzzy level according to the fuzzy level membership interval;

[0010] The grey relational degree of the fault type is determined according to the membership fuzziness and the total fuzziness of the fuzzy levels of the factors.

[0011] According to another aspect of the present invention, a device for handling hazardousness of a charging device is provided, the device comprising:

[0012] A fuzzy level determination module is used to determine the criticality measurement factors of the fault type and the fuzzy level of the criticality measurement factors based on the historical operation records of the charging equipment;

[0013] A membership interval determination module, configured to determine the fuzzy level membership interval of the factor fuzzy level in a trapezoidal membership function; the trapezoidal membership function is generated based on the correlation between the factor fuzzy level and the hazard value;

[0014] a fuzziness determination module, configured to determine the membership fuzziness and the total fuzziness of the membership relationship corresponding to the fuzzy level of the factor according to the fuzzy level membership interval;

[0015] The grey relational degree determination module is used to determine the grey relational degree of the fault type according to the membership fuzziness and the total fuzziness of the fuzzy level of the factor.

[0016] According to another aspect of the present invention, an electronic device is provided, comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for handling the hazard of a charging device according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for handling the hazard of a charging device according to any embodiment of the present invention when executed.

[0021] The embodiment of the present invention obtains a grey correlation degree that can characterize the criticality of the fault by fuzzy grading and membership calculation of the fault. Due to the use of trapezoidal membership function and membership calculation based on fuzzy levels, the accuracy and effectiveness of the quantification result are improved while achieving effective quantification of the criticality of the fault.

[0022] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1A This is a flow chart of a method for handling hazardousness of a charging device provided according to an embodiment of the present invention;

[0025] Figure 1B is a schematic diagram of a severity trapezoidal membership function provided according to an embodiment of the present invention;

[0026] Figure 1C is a schematic diagram of a trapezoidal membership function of occurrence probability provided according to an embodiment of the present invention;

[0027] Figure 2 is a flow chart of a method for handling hazardousness of a charging device provided according to another embodiment of the present invention;

[0028] Figure 3 is a schematic structural diagram of a device for handling hazardousness of a charging device according to another embodiment of the present invention;

[0029] Figure 4 It is a schematic structural diagram of an electronic device implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only 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 making creative efforts should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] Figure 1A This is a flow chart of a method for handling the hazard of a charging device provided by one embodiment of the present invention. This embodiment can be applied to situations where the hazard of a fault type has been determined through the operation record of the charging device. The method can be executed by a device for handling the hazard of a charging device. The device can be implemented in the form of hardware and / or software and can be configured in an electronic device with corresponding data processing capabilities. Figure 1A As shown, the method includes:

[0033] S110 : Determine, based on historical operation records of the charging equipment, a criticality measurement factor of the fault type and a fuzzy level of the criticality measurement factor.

[0034] Fault types refer to abnormalities occurring within a subsystem or component of the charging equipment under specified conditions and within a specified timeframe. These abnormalities include: failure to complete specified functions; failure to maintain relevant performance indicators within specified ranges; impacts on personnel, the environment, energy, and materials exceeding permitted limits; and failure to meet the requirements of technical agreements or other documents. Criticality metrics are used to determine the specific criticality of a fault, including fault severity, repair cost, and probability of occurrence. The criticality of a fault type can be comprehensively assessed based on these factors, including severity, repair cost, and probability of occurrence.

[0035] Specifically, based on historical operating records, the fault types and corresponding fault data that occurred during the operation of the charging equipment are obtained. Based on the severity, probability of occurrence, and other hazard measurement factors, the hazard reference data for different dimensions is determined. To unify the reference value and significance of each hazard measurement factor, a corresponding fuzzy grade classification standard can be set for each hazard measurement factor. Hazard measurement factors with different semantic contents can be converted into fuzzy factor fuzzy grades.

[0036] For example, if the hazard measurement factors include severity and probability of occurrence, respective fuzzy level classification standards can be set in advance for the two, as follows.

[0037]

[0038] Table 1: Fuzzy classification criteria for probability of occurrence

[0039]

[0040]

[0041] Table 2: Severity fuzzy classification standards

[0042] S120 , determining a fuzzy level membership interval of the factor fuzzy level in a trapezoidal membership function; the trapezoidal membership function is generated according to a correlation between the factor fuzzy level and the hazard value.

[0043] Specifically, it is difficult to directly compare the relationship between the hazard measurement factors of different dimensions, such as the repair time and repair amount of a fault, in terms of the hazard size of the two different units of time and amount. In the present invention, a unified unit of hazard value score is set, and each hazard measurement factor uniformly uses the hazard value score value to determine its hazard value of different fuzzy levels. In this process, each hazard measurement factor has its own trapezoidal membership function, which reflects the correlation between the fuzzy level of the factor and the hazard value pre-set for the hazard measurement factor. Based on the trapezoidal membership function of the hazard measurement factor, the hazard value associated with each fuzzy level of the hazard measurement factor can be known, and the fuzzy level membership interval represents the hazard value that may be associated with the fuzzy level.

[0044] For example, if the hazard measurement factors include severity and probability of occurrence, both of which use a unified 0-10 scale as the hazard value score, based on the fuzzy grade division principle in the above example and the following hazard value scoring standard, we can get Figure 1B a trapezoidal membership function for medium severity, and Figure 1C For example, for the fuzzy level of occurrence probability, the hazard value of level E is between 0 and 1, and the corresponding membership trapezoidal interval is between 0 and 1. The hazard value of level A is between 9 and 10, and the corresponding membership trapezoidal interval is between 9 and 10.

[0045]

[0046]

[0047] Table 3: Severity and probability of occurrence fuzzy level hazard value scoring criteria

[0048] For example, if the hazard measurement factors are severity and probability of occurrence, based on the correlation between the hazard value and the fuzzy level in the above example, we can get Figure 1B a trapezoidal membership function for medium severity, and Figure 1C Trapezoidal membership function of the probability of occurrence in .

[0049] S130 , determining the membership fuzziness and the total fuzziness of the membership relationship corresponding to the factor fuzzy level according to the fuzzy level membership interval.

[0050] Among them, the fuzzy membership represents the fuzziness corresponding to different membership relationships.

[0051] Specifically, two horizontal coordinates with a range of 0 are located in the fuzzy membership interval, representing the maximum and minimum possible hazard scores corresponding to the current fuzzy level membership interval. Two horizontal coordinates with a range of 1 represent the two hazard scores with the highest membership probability corresponding to the current fuzzy level membership interval. The maximum and minimum hazard scores for all fuzzy levels of the hazard metric factor are simultaneously obtained. Based on these six hazard scores, the membership ambiguity and total ambiguity of the membership relationship corresponding to the factor's fuzzy level are calculated.

[0052] Optionally, determining the membership ambiguity and total ambiguity of the membership relationship corresponding to the factor fuzzy level according to the fuzzy level membership interval includes:

[0053] According to the fuzzy level membership interval, the area occupied by the maximum level and the minimum level in the membership relationship of the factor fuzzy level is determined as the first fuzziness; according to the fuzzy level membership interval, the area occupied by the minimum level and the maximum level in the membership relationship of the factor fuzzy level is determined as the second fuzziness; the first fuzziness is used as the membership fuzziness of the membership relationship corresponding to the factor fuzzy level, and the sum of the first fuzziness and the second fuzziness is used as the total fuzziness of the membership relationship corresponding to the factor fuzzy level.

[0054] Specifically, the first fuzziness and the second fuzziness, as well as the fuzziness ratio of the hazardousness measurement factor obtained based on the first fuzziness and the second fuzziness, are calculated using the following formula:

[0055]

[0056] Among them, a 0n 、b 0n and a 1n 、b 1n The fuzzy level membership interval of the fuzzy level of the nth factor is represented. The value range of the membership function corresponding to the membership interval is 0 or 1, where 0 indicates no membership and 1 indicates membership. 0n to b 0nis the level range that does not belong to a certain fuzzy level, a 1n to b 1n is the range of grades belonging to a certain fuzzy grade, c and d are the lowest and highest hazard values in the trapezoidal membership function corresponding to the hazard measurement factor. 0n -c) and (b) 1n -c) represents the area between the largest level and the smallest level in the affiliation, da 0n and(da 1n ) represents the area between the smaller and largest levels in the affiliation relationship, (b 0n -c)+(b 1n -c) represents the membership ambiguity corresponding to different membership relations, [(b 0n -c)+(b 1n -c)]+[(da 0n )+(da 1n )] represents the total fuzziness, and K(n) represents the fuzziness ratio, so that the fuzzy level discrete data of the hazard measurement factors can be mapped with the specific evaluation indicators to facilitate mathematical operations.

[0057] For example, if the hazard measurement factors are severity and probability of occurrence, based on the trapezoidal membership function and fuzzy level membership interval in the above example, the fuzziness ratio of each fuzzy level can be obtained.

[0058]

[0059] Table 4 Fuzzy level membership intervals and fuzziness ratios of severity and probability of occurrence

[0060] Taking the probability level B as an example, according to the membership function of the probability of occurrence, it can be known that when the value range is 0, the corresponding horizontal coordinates (hazard value score) a0 and b0 are 7 and 10; when the value range is 1, the corresponding horizontal coordinates (hazard value score) a1 and b1 are 8 and 9; and the maximum score d and minimum score c of all probability levels of AD are 10 and 0 respectively. That is, after determining that the probability of occurrence of a certain fault type is classified as Class B based on the fault data, the fuzzy level membership interval corresponding to the Class B fuzzy level is used to calculate the fuzziness ratio of the criticality measurement factor, the probability of occurrence corresponding to the fault type, to be 0.7917.

[0061] S140 , determining the grey relational degree of the fault type according to the membership fuzziness and the total fuzziness of the fuzzy levels of the factors.

[0062] Among them, the grey correlation degree of the fault type objectively reflects the harmfulness of the fault type. The lower the grey correlation degree of the fault type, the greater the harmfulness of the fault type.

[0063] Specifically, based on the subordinate fuzziness and total fuzziness corresponding to the fuzzy level of the hazard measurement factor, the fuzziness ratio and grey correlation coefficient of the hazard measurement factor are obtained. If the hazard of a fault type is measured based on only one hazard measurement factor, the correlation coefficient of that hazard measurement factor can be used as the grey correlation value of the fault type. If the hazard of a fault type is measured based on multiple hazard measurement factors, the grey correlation coefficients of these hazard measurement factors need to be combined to obtain the grey correlation value of the fault type.

[0064] The embodiment of the present invention obtains a grey correlation degree that can characterize the criticality of the fault by fuzzy grading and membership calculation of the fault. Due to the use of trapezoidal membership function and membership calculation based on fuzzy levels, the accuracy and effectiveness of the quantification result are improved while achieving effective quantification of the criticality of the fault.

[0065] Figure 2 This is a flow chart of a method for handling the hazards of a charging device provided by another embodiment of the present invention. This embodiment is optimized and improved on the basis of the above embodiment. Figure 2 As shown, the method includes:

[0066] S210: Determine, based on historical operation records of the charging device, a criticality measurement factor of the fault type and a fuzzy level of the criticality measurement factor.

[0067] S220 , determining a fuzzy level membership interval of the factor fuzzy level in a trapezoidal membership function; the trapezoidal membership function is generated according to a correlation between the factor fuzzy level and the hazard value.

[0068] S230 : Determine the membership fuzziness and total fuzziness of the membership relationship corresponding to the factor fuzzy level according to the fuzzy level membership interval.

[0069] S241. Calculate the ratio of the subordinate ambiguity to the total ambiguity as the ambiguity ratio of the criticality measurement factor;

[0070] S242. Generate a comparison sequence of the fault types according to the fuzziness ratio of the criticality measurement factor;

[0071] S243, calculating a difference sequence between the comparison sequence and the standard sequence;

[0072] S244. Calculate the grey correlation coefficient of the hazard measurement factor in the difference sequence.

[0073] S245 , multiplying the grey correlation coefficient of each hazard measurement factor by the corresponding weight and then summing the results to obtain the grey correlation degree of the fault type.

[0074] Specifically, (1) the fuzziness proportion K(n) corresponding to each hazard measurement factor is formed into a comparison sequence X, and the standard sequence X0 is defined

[0075]

[0076] Where: x1, x2, ..., x m Represents the comparison sequence corresponding to i fault types: {x i (1), x i (2),…,x i (m)} represents the brittleness coefficient corresponding to the fuzzy semantics of the k criticality measurement factors of the i-th fault type.

[0077] (2) At the same time, a standard sequence X0 is generated, which reflects the ideal or expected levels of all hazard measurement factors.

[0078] X0=[x0(1),x0(2),…,x0(k)]

[0079] (3) Calculate the difference sequence D between the comparison sequence and the standard sequence i .

[0080] D i =[Δx i (1), Δx i (2),…,Δx i (k)]

[0081] Where Δx i (k) = x i (k)-x0(k).

[0082] (4) Calculate the grey correlation coefficient r[x0(k), x i (k)].

[0083]

[0084] Where x0(k) is the fuzziness ratio corresponding to the kth hazard measurement factor in the standard sequence; i (k) is the fuzzy ratio corresponding to the kth criticality measurement factor of the i-th fault type in the comparison sequence matrix; δ is the resolution coefficient, which only affects the relative risk value, δ∈(0,1), and is preferably 0.5.

[0085] (5) The grey relational degree r(x0, x i ):

[0086]

[0087] Where, β kis the weight coefficient of each hazard measurement factor. The numerical value is determined by the preferred hierarchical analysis method. For the sake of simplicity of calculation, it is generally preferred to divide it equally and ensure that the sum of the weights of each evaluation element is 1.

[0088] In addition, after calculating the gray correlation value of each fault mode, the charging equipment fault modes can be sorted from low to high according to the gray correlation value; for fault modes with equal values, they are sorted according to the order of occurrence. Fault modes with low ranking numbers represent greater hazards and present more potential risks. During daily operation and maintenance, it is necessary to strengthen the investigation and timely handling of alarm phenomena. Fault modes with high ranking numbers have relatively simple potential risks. When there are insufficient operation and maintenance personnel or it is difficult to handle them in time, the alarm prompts of the equipment can be delayed.

[0089] For example, when the hazard measurement factors are the severity and occurrence probability of the fault type, the respective grey relational coefficients are calculated, and the grey relational degrees are calculated with a weight of 0.5, thereby obtaining the ranking results of the fault types.

[0090]

[0091]

[0092] Table 5: Grey correlation coefficient, value and ranking results when the criticality measurement factors are the severity and probability of the fault type

[0093] Based on the operation and maintenance principles, assist operators in formulating operation and maintenance strategies and plans: For faults ranked 1 to 10, these involve important functions such as whether charging equipment can be used and charging safety, and are highly harmful to charging equipment and people. Corresponding resources need to be allocated in priority for operation and maintenance, and charging station operators should prioritize the reserve of a certain amount of spare parts; for faults ranked 11 to 16, the harm to charging equipment and people is relatively small, but there are obvious hidden dangers. Communication with suppliers should be strengthened, and design issues should be promptly checked and rectified; faults ranked 1 to 10 can be further divided into three categories: those that pose a risk of harm to people, such as those ranked 2 and 3; those that damage equipment, such as those ranked 4 and 5; and those that cannot be used. This guides operation and maintenance personnel to formulate more specific maintenance times and methods.

[0094] Optionally, a historical database of fault-criticality countermeasures can be constructed based on the sorting results of the gray correlation values of fault types and their corresponding countermeasures (e.g., timely handling, inclusion in the spare parts category, etc.). Each fault type in the database is associated with one or more countermeasures. This database can be used for big data analysis in subsequent customer service and maintenance resource allocation, technical development resource allocation, and other related operations to provide reference data for related operations.

[0095] The embodiment of the present invention calculates the grey correlation coefficient by comparing the sequence and the standard sequence, thereby improving the accuracy of the grey correlation coefficient calculation. When there are multiple hazard measurement factors, the grey correlation value of the fault type is obtained based on their respective weights and the grey correlation coefficient, thereby achieving a quantitative value of the fault hazard with an emphasis on the hazard measurement factors.

[0096] Figure 3 This is a schematic diagram of a device for handling hazardous conditions of a charging device according to another embodiment of the present invention. Figure 3 As shown, the device includes:

[0097] A fuzzy level determination module 310 is configured to determine a criticality measurement factor of a fault type and a fuzzy level of the criticality measurement factor based on historical operation records of the charging device;

[0098] A membership interval determination module 320 is configured to determine the fuzzy level membership interval of the factor fuzzy level in a trapezoidal membership function generated based on the correlation between the factor fuzzy level and the hazard value;

[0099] The fuzziness determination module 330 is configured to determine the membership fuzziness and the total fuzziness of the membership relationship corresponding to the factor fuzzy level according to the fuzzy level membership interval;

[0100] The grey relational degree determination module 340 is configured to determine the grey relational degree of the fault type according to the membership fuzziness and the total fuzziness of the fuzzy levels of the factors.

[0101] The device for handling the hazard of a charging device provided in an embodiment of the present invention can execute the method for handling the hazard of a charging device provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0102] Optionally, the membership interval determination module 320 is specifically configured to:

[0103] According to the fuzzy level membership interval, the area occupied by the maximum level and the minimum level in the membership relationship of the factor fuzzy level is determined as the first fuzziness; according to the fuzzy level membership interval, the area occupied by the minimum level and the maximum level in the membership relationship of the factor fuzzy level is determined as the second fuzziness; the first fuzziness is used as the membership fuzziness of the membership relationship corresponding to the factor fuzzy level, and the sum of the first fuzziness and the second fuzziness is used as the total fuzziness of the membership relationship corresponding to the factor fuzzy level.

[0104] Optionally, the grey relational degree determination module 340 is specifically configured to:

[0105]

[0106] Among them, a 0n 、b 0n and a 1n 、b 1n represents the fuzzy level membership interval of the fuzzy level of the nth factor, the value range of the membership function corresponding to the membership interval is 0 or 1, 0 represents no membership, and 1 represents membership, c and d are the lowest and highest hazard values in the trapezoidal membership function corresponding to the hazard measurement factor, and K(n) is the fuzziness ratio of the fuzzy level of the nth factor.

[0107] Optionally, the grey relational degree determination module 340 is specifically configured to:

[0108] The ratio of the subordinate fuzziness to the total fuzziness is calculated as the fuzziness ratio of the hazard measurement factor; a comparison sequence of the fault type is generated according to the fuzziness ratio of the hazard measurement factor; a difference sequence between the comparison sequence and the standard sequence is calculated; the grey relational coefficient of the hazard measurement factor is calculated in the difference sequence; the grey relational coefficient of each hazard measurement factor is multiplied by the corresponding weight and the summed up to obtain the grey relational degree of the fault type.

[0109] Optionally, the historical operation record of the charging device is specifically used to:

[0110] According to the historical operation records of the charging device, the type of fault that has occurred in the charging device and the fault data of the fault type are determined; and the criticality measurement factor and the fuzzy level of the criticality measurement factor of the fault type are determined based on the fault data of the fault type.

[0111] The device for handling the hazard of a charging device further described can also execute the method for handling the hazard of a charging device provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0112] Figure 4 A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0113] like Figure 4As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., which is communicatively connected to the at least one processor 41. The memory stores a computer program that can be executed by the at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 into the random access memory (RAM) 43. Various programs and data required for the operation of the electronic device 40 can also be stored in the RAM 43. The processor 41, ROM 42, and RAM 43 are connected to each other via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0114] Multiple components in the electronic device 40 are connected to the I / O interface 45, including an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a magnetic disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0115] Processor 41 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any other suitable processor, controller, microcontroller, etc. Processor 41 executes the various methods and processes described above, such as the method for handling the hazardousness of charging equipment.

[0116] In some embodiments, the method for handling the hazardousness of a charging device may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the method for handling the hazardousness of a charging device described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to execute the method for handling the hazardousness of a charging device in any other appropriate manner (e.g., by means of firmware).

[0117] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0118] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0119] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0120] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0121] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0122] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0123] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0124] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for handling the hazards of charging equipment, characterized in that: The method comprises: Determine the criticality measurement factors of the fault types and the fuzzy levels of the criticality measurement factors based on the historical operation records of the charging equipment; Determining a fuzzy level membership interval of the fuzzy level of the factor in a trapezoidal membership function; the trapezoidal membership function is generated according to the correlation between the fuzzy level of the factor and the hazard value; Determining the membership fuzziness and total fuzziness of the membership relationship corresponding to the factor fuzzy level according to the fuzzy level membership interval; Determining the grey relational degree of the fault type according to the membership fuzziness and the total fuzziness of the fuzzy levels of the factors; The step of determining the grey relational degree of the fault type according to the membership fuzziness and the total fuzziness of the fuzzy levels of the factors includes: Calculating the ratio of the subordinate ambiguity to the total ambiguity as the ambiguity ratio of the criticality measurement factor; generating a comparison sequence of the fault types according to the fuzziness ratio of the criticality measurement factor; Calculating a difference sequence between the comparison sequence and the standard sequence; Calculating the grey relational coefficient of the hazard measurement factor in the difference sequence; The grey relational coefficient of each hazard measurement factor is multiplied by the corresponding weight and then summed up to obtain the grey relational degree of the fault type.

2. The method according to claim 1, wherein: The determining, based on the fuzzy level membership interval, the membership fuzziness and the total fuzziness of the membership relationship corresponding to the factor fuzzy level includes: Determining, according to the fuzzy level membership interval, an area occupied by a larger level and a minimum level in the membership relationship of the factor fuzzy levels as a first fuzziness degree; determining, according to the fuzzy level membership interval, an area occupied by a smaller level and a maximum level in the membership relationship of the factor fuzzy levels as a second fuzziness degree; using the first fuzziness as the membership fuzziness of the membership relationship corresponding to the factor fuzzy level, and using the sum of the first fuzziness and the second fuzziness as the total fuzziness of the membership relationship corresponding to the factor fuzzy level; The first ambiguity, the second ambiguity, the subordinate ambiguity, the total ambiguity and the ambiguity ratio are calculated by the following formula: Among them, a 0n 、b 0n and a 1n 、b 1n represents the fuzzy level membership interval of the fuzzy level of the nth factor, the value range of the membership function corresponding to the membership interval is 0 or 1, 0 represents no membership, 1 represents membership, c and d are the lowest and highest hazard values in the trapezoidal membership function corresponding to the hazard measurement factor, and K(n) is the fuzziness ratio of the fuzzy level of the nth factor; (b 0n -c) and (b) 1n -c) represents the area between the larger level and the smallest level in the membership relationship, which serves as the first fuzziness; da 0n and(da 1n ) represents the area between the smaller level and the largest level in the affiliation relationship, which serves as the second fuzziness; (b 0n -c)+(b 1n -c) represents the membership ambiguity; [(b 0n -c)+(b 1n -c)]+[(da 0n )+(da 1n )] represents the total ambiguity.

3. The method according to claim 1, characterized in that Determining the criticality measurement factors and fuzzy levels of the fault types based on the historical operation records of the charging equipment includes: Determining, based on historical operation records of the charging device, a type of fault that has occurred in the charging device and fault data of the fault type; The criticality measurement factors and fuzzy levels of the criticality measurement factors of the fault types are determined through the fault data of the fault types.

4. A device for handling hazards of charging equipment, characterized in that: The device comprises: A fuzzy level determination module is used to determine the criticality measurement factors of the fault type and the fuzzy level of the criticality measurement factors based on the historical operation records of the charging equipment; A membership interval determination module, configured to determine the fuzzy level membership interval of the factor fuzzy level in a trapezoidal membership function; the trapezoidal membership function is generated based on the correlation between the factor fuzzy level and the hazard value; a fuzziness determination module, configured to determine the membership fuzziness and the total fuzziness of the membership relationship corresponding to the fuzzy level of the factor according to the fuzzy level membership interval; A grey relational degree determination module, configured to determine the grey relational degree of the fault type according to the membership fuzziness and the total fuzziness of the fuzzy levels of the factors; Among them, the grey relational degree determination module is specifically used to: calculate the ratio of the subordinate ambiguity and the total ambiguity as the ambiguity ratio of the hazard measurement factor; generate a comparison sequence of the fault type according to the ambiguity ratio of the hazard measurement factor; calculate the difference sequence between the comparison sequence and the standard sequence; calculate the grey relational coefficient of the hazard measurement factor in the difference sequence; multiply the grey relational coefficient of each hazard measurement factor by the corresponding weight and sum them up to obtain the grey relational degree of the fault type.

5. The device according to claim 4, characterized in that The membership interval determination module is specifically configured to: Determining, according to the fuzzy level membership interval, an area occupied by a larger level and a minimum level in the membership relationship of the factor fuzzy levels as a first fuzziness degree; determining, according to the fuzzy level membership interval, an area occupied by a smaller level and a maximum level in the membership relationship of the factor fuzzy levels as a second fuzziness degree; using the first fuzziness as the membership fuzziness of the membership relationship corresponding to the factor fuzzy level, and using the sum of the first fuzziness and the second fuzziness as the total fuzziness of the membership relationship corresponding to the factor fuzzy level; The first ambiguity, the second ambiguity, the subordinate ambiguity, the total ambiguity and the ambiguity ratio are calculated by the following formula: Among them, a 0n 、b 0n and a 1n 、b 1n represents the fuzzy level membership interval of the fuzzy level of the nth factor, the value range of the membership function corresponding to the membership interval is 0 or 1, 0 represents no membership, 1 represents membership, c and d are the lowest and highest hazard values in the trapezoidal membership function corresponding to the hazard measurement factor, and K(n) is the fuzziness ratio of the fuzzy level of the nth factor; (b 0n -c) and (b) 1n -c) represents the area between the larger level and the smallest level in the membership relationship, which serves as the first fuzziness; da 0n and(da 1n ) represents the area between the smaller level and the largest level in the affiliation relationship, which serves as the second fuzziness; (b 0n -c)+(b 1n -c) represents the membership ambiguity; [(b 0n -c)+(b 1n -c)]+[(da 0n )+(da 1n )] represents the total ambiguity.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for handling hazardousness of a charging device according to any one of claims 1 to 3.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for handling the hazard of a charging device according to any one of claims 1 to 3 when executed.

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