Operation state processing method, device and equipment of special electric vehicle and medium

Abstract

The invention relates to the technical field of electric power systems, in particular to an electric power special vehicle operation state processing method and device, equipment and a medium, and the method comprises the steps: obtaining a classic domain and a node domain of each single index in a preset state risk level, and an actual measurement index value of each single index; calculating a first correlation degree between the single index and each state risk level according to a classic domain and a node domain of the single index in a preset state risk level and an actually measured index value of the single index; taking the state risk level with the highest first association degree as the state risk level of the single index; and based on the first association degree between each single index and each state risk level, determining the state risk level of each functional module and the state risk level of the special electric vehicle. According to the technical scheme, the intelligent operation and maintenance capability of the electric power special vehicle can be improved, comprehensive, real-time and accurate monitoring of the running state of the vehicle is achieved, and the method is mainly used for monitoring the state of the electric power special vehicle.

Description

Technical Field

[0001] The present disclosure relates to the technical field of electric power systems, and in particular to a method, device, equipment and medium for processing the operating status of an electric special vehicle. Background Art

[0002] With the acceleration of global energy transformation and smart grid construction, the demand for special electric vehicles has surged, becoming an important tool for grid operation and maintenance, emergency repairs and emergency response. However, the current digital capabilities of special electric vehicles are generally insufficient and urgently need to be upgraded. In existing technologies, the monitoring methods of special electric vehicles are relatively backward, mainly relying on manual inspections and simple sensor monitoring. These methods are not only inefficient, but also difficult to achieve comprehensive, real-time and accurate monitoring of the vehicle's operating status. Summary of the invention

[0003] In order to solve the problems in the related art, the embodiments of the present disclosure provide a method, device, equipment and medium for processing the operating status of an electric special vehicle.

[0004] In a first aspect, an embodiment of the present disclosure provides a method for processing the operating state of an electric special vehicle, comprising:

[0005] Obtaining each single indicator of the electric special vehicle in the classic domain and the section domain of the preset state risk level, wherein the electric special vehicle corresponds to a plurality of functional modules, and each functional module corresponds to a plurality of single indicators;

[0006] Get the measured indicator value of each single indicator;

[0007] For each single indicator, according to the classical domain and the section domain of the preset state risk level of the single indicator and the measured indicator value of the single indicator, calculate the first correlation degree between the single indicator and each state risk level;

[0008] Taking the state risk level with the highest first correlation as the state risk level of the single indicator;

[0009] Based on the first correlation between each single indicator and each state risk level, the state risk level of each functional module and the state risk level of the electric special vehicle are determined.

[0010] In a possible implementation, determining the state risk level of each functional module and the state risk level of the electric special vehicle based on the first correlation between each single indicator and each state risk level includes:

[0011] For each functional module, based on the first correlations between the plurality of single indicators corresponding to the functional module and the risk levels of each state, determine a second correlation between the functional module and the risk levels of each state;

[0012] Using the state risk level with the second highest correlation as the state risk level of the functional module;

[0013] Based on the second correlation between each functional module and each state risk level, determining the third correlation between the electric special vehicle and each state risk level;

[0014] The state risk level with the third highest correlation degree is used as the state risk level of the electric special vehicle.

[0015] In a possible implementation, the calculating the first correlation between the single indicator and each state risk level according to the single indicator in the classical domain and the section domain of the preset state risk level and the measured indicator value of the single indicator includes:

[0016] Calculate the i-th single indicator s according to the following formula i The first correlation with the k-th state risk level

[0017]

[0018] in, and They are single indicators s i The lower and upper thresholds of the classic domain of the k-th state risk level, a i and b i They are single indicators s i The lower and upper thresholds of the section domain, i > for the single indicator s i The measured index value.

[0019] In a possible implementation, determining the second correlation between the functional module and each state risk level based on the first correlation between the plurality of single indicators corresponding to the functional module and each state risk level includes:

[0020] Using a hierarchical analysis method, obtaining a first judgment matrix corresponding to the functional module, and calculating a basic weight of each single indicator in the functional module based on the first judgment matrix, wherein the first judgment matrix is ​​used to represent a comparison value of the importance between multiple single indicators in the functional module;

[0021] The basic weights of the single indicators are modified by using a variable weight comprehensive model based on factor space theory to obtain the variable weight vectors of the single indicators in the functional module;

[0022] ​Based on the variable weight vector and the first correlation between the plurality of single indicators corresponding to the functional module and the risk level of each state, a second correlation between the functional module and the risk level of each state is calculated.

[0023] In a possible implementation, the variable weight comprehensive model based on factor space theory is used to modify the basic weight of each single indicator to obtain the variable weight vector of each single indicator in the functional module, including:

[0024] According to the following formula, the jth function module is calculated The variable weight vector of a single indicator

[0025] in, is the total number of single indicators in the jth functional module, For the jth functional module The basic weight of a single indicator, i j > is the i-th single indicator s in the j-th functional module i j The measured index value, α is the preset value, a i j and b i j are the i-th single indicator s in the j-th functional module i j The upper and lower thresholds of the section domain.

[0026] In a possible implementation, the calculating the second correlation between the functional module and each state risk level based on the variable weight vector and the first correlation between the plurality of single indicators corresponding to the functional module and each state risk level includes:

[0027] The second correlation between the jth functional module and the kth level of state risk level is calculated according to the following formula:

[0028]

[0029] in, is the i-th single indicator s in the j-th functional module i j The first degree of association with the k-th state risk level.

[0030] In a possible implementation, the determining of the third degree of association between the electric special vehicle and each state risk level based on the second degree of association between each functional module and each state risk level includes:

[0031] ​A hierarchical analysis method is used to obtain a second judgment matrix corresponding to the electric special vehicle, and a basic weight of each functional module in the electric special vehicle is calculated based on the second judgment matrix, wherein the second judgment matrix is ​​used to represent a comparison value of the importance between multiple functional modules in the electric special vehicle;

[0032] Based on the basic weight of each functional module in the special electric vehicle and the second correlation between each functional module in the special electric vehicle and each state risk level, the third correlation between the special electric vehicle and each state risk level is calculated.

[0033] In a possible implementation, the calculation of the third correlation between the electric special vehicle and each state risk level based on the basic weight of each functional module in the electric special vehicle and the second correlation between each functional module in the electric special vehicle and each state risk level includes:

[0034] The third correlation R between the electric special vehicle and the k-th level state risk level is calculated according to the following formula: k :

[0035]

[0036] Wherein, M2 is the total number of functional modules in the electric special vehicle, w 0,j is the basic weight of the jth functional module in the electric special vehicle, is the second correlation degree between the j-th functional module and the k-th state risk level.

[0037] In a possible implementation, the method further includes:

[0038] When the state risk level of the electric special vehicle is a risk level that requires operation and maintenance work, the state risk level of each single indicator is input into a pre-trained recognition model, and the recognition model is executed to obtain the type of operation and maintenance work output by the recognition model, and the operation and maintenance work type includes edge-side operation and maintenance, cloud-side operation and maintenance, and cloud-edge collaborative operation and maintenance;

[0039] When the operation and maintenance work type is side operation and maintenance, outputting first reminder information, wherein the first reminder information is used to remind side personnel to perform operation and maintenance work;

[0040] When the operation and maintenance work type is cloud-side operation and maintenance, the state risk level of each single indicator of the special electric vehicle, the state risk level of each functional module and the state risk level of the special electric vehicle are sent to the cloud;

[0041] When the operation and maintenance work type is cloud-edge collaborative operation and maintenance, a second reminder message is output to remind the side personnel to perform operation and maintenance work and send the status risk level of each single indicator of the special electric vehicle, the status risk level of each functional module and the status risk level of the special electric vehicle to the cloud.

[0042] In a second aspect, an embodiment of the present disclosure provides a running state processing device for an electric special vehicle, comprising:

[0043] A first acquisition module is configured to acquire each single indicator of the electric special vehicle in a classic domain and a section domain of a preset state risk level, wherein the electric special vehicle corresponds to a plurality of functional modules, and each functional module corresponds to a plurality of single indicators;

[0044] A second acquisition module is configured to obtain the measured indicator value of each single indicator;

[0045] A calculation module is configured to calculate, for each single indicator, a first correlation degree between the single indicator and each state risk level according to the single indicator in a preset classical domain and a section domain of the state risk level and a measured indicator value of the single indicator;

[0046] A first level determination module is configured to use the state risk level with the highest first correlation as the state risk level of the single indicator;

[0047] The second level determination module is configured to determine the state risk level of each functional module and the state risk level of the electric special vehicle based on the first correlation between each single indicator and each state risk level.

[0048] In a possible implementation manner, the second level determination module is configured to:

[0049] For each functional module, based on the first correlations between the plurality of single indicators corresponding to the functional module and the risk levels of each state, determine a second correlation between the functional module and the risk levels of each state;

[0050] Using the state risk level with the second highest correlation as the state risk level of the functional module;

[0051] Based on the second correlation between each functional module and each state risk level, determining the third correlation between the electric special vehicle and each state risk level;

[0052] The state risk level with the third highest correlation degree is used as the state risk level of the electric special vehicle.

[0053] In a possible implementation, the calculation module is configured as follows:

[0054] Calculate the i-th single indicator s according to the following formula i The first correlation with the k-th state risk level

[0055]

[0056] in, and They are single indicators s i The lower and upper thresholds of the classic domain of the k-th state risk level, a i and b i They are single indicators s i The lower and upper thresholds of the section domain, i > for the single indicator s i The measured index value.

[0057] In a possible implementation manner, the part of the second level determination module that determines the second degree of association between the functional module and each state risk level based on the first degree of association between the multiple single indicators corresponding to the functional module and each state risk level is configured as follows:

[0058] Using a hierarchical analysis method, obtaining a first judgment matrix corresponding to the functional module, and calculating a basic weight of each single indicator in the functional module based on the first judgment matrix, wherein the first judgment matrix is ​​used to represent a comparison value of the importance between multiple single indicators in the functional module;

[0059] The basic weights of the single indicators are modified by using a variable weight comprehensive model based on factor space theory to obtain the variable weight vectors of the single indicators in the functional module;

[0060] Based on the variable weight vector and the first correlation between the plurality of single indicators corresponding to the functional module and the risk level of each state, a second correlation between the functional module and the risk level of each state is calculated.

[0061] In a possible implementation manner, the second level determination module adopts a variable weight comprehensive model based on factor space theory to correct the basic weights of the single indicators, and the part of obtaining the variable weight vector of each single indicator in the functional module is configured as follows:

[0062] According to the following formula, the jth function module is calculated The variable weight vector of a single indicator

[0063] in, is the total number of single indicators in the jth functional module, For the jth functional module​ The basic weight of a single indicator, i j > is the i-th single indicator s in the j-th functional module i j The measured index value, α is the preset value, a i j and b i j are the i-th single indicator s in the j-th functional module i j The upper and lower thresholds of the section domain.

[0064] In a possible implementation manner, the part of the second level determination module that calculates the second correlation between the functional module and each state risk level based on the variable weight vector and the first correlation between the plurality of single indicators corresponding to the functional module and each state risk level is configured as follows:

[0065] The second correlation between the jth functional module and the kth level of state risk level is calculated according to the following formula:

[0066]

[0067] in, is the i-th single indicator s in the j-th functional module i j The first degree of association with the k-th state risk level.

[0068] In a possible implementation manner, the part of the second level determination module that determines the third degree of association between the electric special vehicle and each state risk level based on the second degree of association between each functional module and each state risk level is configured as follows:

[0069] A hierarchical analysis method is used to obtain a second judgment matrix corresponding to the electric special vehicle, and a basic weight of each functional module in the electric special vehicle is calculated based on the second judgment matrix, wherein the second judgment matrix is ​​used to represent a comparison value of the importance between multiple functional modules in the electric special vehicle;

[0070] Based on the basic weight of each functional module in the special electric vehicle and the second correlation between each functional module in the special electric vehicle and each state risk level, the third correlation between the special electric vehicle and each state risk level is calculated.

[0071] ​In a possible implementation manner, the part of the second level determination module that calculates the third degree of association between the electric special vehicle and each state risk level based on the basic weight of each functional module in the electric special vehicle and the second degree of association between each functional module in the electric special vehicle and each state risk level is configured as follows:

[0072] The third correlation R between the electric special vehicle and the k-th level state risk level is calculated according to the following formula: k :

[0073]

[0074] Wherein, M2 is the total number of functional modules in the electric special vehicle, w 0,j is the basic weight of the jth functional module in the electric special vehicle, is the second correlation degree between the j-th functional module and the k-th state risk level.

[0075] In a possible implementation, the device further includes:

[0076] A type identification module is configured to input the state risk level of each single indicator into a pre-trained identification model when the state risk level of the electric special vehicle is a risk level that requires operation and maintenance work, execute the identification model, and obtain the type of operation and maintenance work output by the identification model, wherein the operation and maintenance work type includes edge-side operation and maintenance, cloud-side operation and maintenance, and cloud-edge collaborative operation and maintenance;

[0077] The output module is configured to output a first reminder message when the operation and maintenance work type is edge operation and maintenance, and the first reminder message is used to remind edge personnel to perform operation and maintenance work; when the operation and maintenance work type is cloud-side operation and maintenance, the status risk level of each single indicator of the special electric vehicle, the status risk level of each functional module, and the status risk level of the special electric vehicle are sent to the cloud; when the operation and maintenance work type is cloud-edge collaborative operation and maintenance, the second reminder message is output to remind edge personnel to perform operation and maintenance work and the status risk level of each single indicator of the special electric vehicle, the status risk level of each functional module, and the status risk level of the special electric vehicle are sent to the cloud.

[0078] In a third aspect, an embodiment of the present disclosure provides an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer instructions, and wherein the one or more computer instructions are executed by the processor to implement a method as described in any one of the first aspects.

[0079] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium on which computer instructions are stored. When the computer instructions are executed by a processor, a method as described in any one of the first aspects is implemented.

[0080] According to the technical solution provided by the present disclosure, the present disclosure proposes a multi-level evaluation system, which integrates a single indicator, a functional module and an operating status evaluation of an electric special vehicle, and realizes multi-granularity status monitoring and evaluation from fine to coarse. At the same time, the present implementation method makes full use of the object-element extension model, fully combines it with the operating status evaluation at each level, and uses the correlation of the object-element extension model to characterize the state risk level of a single indicator, and then determines the state risk level of each functional module and the state risk level of the electric special vehicle based on the first correlation between the single indicator and each state risk level, simplifies the operating status evaluation steps at each level, and can be conveniently deployed in the Internet of Things terminal corresponding to the electric special vehicle, thereby improving the intelligent operation and maintenance capabilities of the electric special vehicle, realizing comprehensive, real-time and accurate monitoring of the vehicle's operating status, and realizing pre-fault warning, in-process monitoring and post-fault review based on the state risk levels at each level.

[0081] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Other features, objectives and advantages of the present disclosure will become more apparent through the following detailed description of non-limiting embodiments in conjunction with the accompanying drawings. In the accompanying drawings:

[0083] Figure 1 A flow chart of a method for processing the operating status of an electric special vehicle provided in an embodiment of the present disclosure is shown.

[0084] Figure 2 A schematic diagram of a multi-level operating status evaluation system provided by an embodiment of the present disclosure is shown.

[0085] Figure 3 A structural block diagram of an operating status processing device for an electric special vehicle provided in an embodiment of the present disclosure is shown.

[0086] Figure 4 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0087] Figure 5 A schematic diagram showing the structure of a computer system suitable for implementing the method of the embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0088] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. In addition, for the sake of clarity, parts not related to the description of the exemplary embodiments are omitted in the accompanying drawings.

[0089] In the present disclosure, it should be understood that terms such as "include" or "have" are intended to indicate the presence of features, numbers, steps, behaviors, components, parts, or a combination thereof disclosed in the present specification, and are not intended to exclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or a combination thereof exist or are added.

[0090] It should also be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0091] In order to solve the problems raised in the background technology, the present embodiment proposes a multi-level evaluation system, which integrates a single indicator, a functional module and an operating status evaluation of an electric special vehicle, and realizes multi-granularity status monitoring and evaluation from fine to coarse. At the same time, the present embodiment makes full use of the physical element extension model, fully combines it with the operating status evaluation at each level, and uses the correlation of the physical element extension model to characterize the state risk level of a single indicator, and then determines the state risk level of each functional module and the state risk level of the electric special vehicle based on the first correlation between the single indicator and each state risk level, which simplifies the operating status evaluation steps at each level and can be conveniently deployed in the Internet of Things terminal corresponding to the electric special vehicle, thereby improving the intelligent operation and maintenance capabilities of the electric special vehicle, realizing comprehensive, real-time and accurate monitoring of the vehicle's operating status, and realizing pre-fault warning, in-process monitoring and post-fault review based on the state risk levels at each level.

[0092] In addition, this implementation can also guide the subsequent operation and maintenance of the vehicle based on the single indicator, each functional module and the status risk level of the electric special vehicle. When the status risk level of the electric special vehicle is a risk level that requires operation and maintenance work, the side Internet of Things terminal can implement a side operation and maintenance work diversion strategy based on the status risk of the single indicator, determine the attribution of the operation and maintenance work, form a fault warning-operation and maintenance allocation closed loop, and realize accurate allocation of operation and maintenance work. The owner of the operation and maintenance work can perform operation and maintenance work based on the single indicator, each functional module and the status risk level of the electric special vehicle to achieve rapid response to faults.

[0093] Figure 1 FIG. 1 is a flow chart showing a method for processing the operating status of an electric special vehicle provided by an embodiment of the present disclosure. Figure 1 As shown, the method for processing the running state of the electric special vehicle includes the following steps S101-S105:

[0094] In step S101, the classic domain and the section domain of each single indicator of the electric special vehicle in the preset state risk level are obtained, and the electric special vehicle corresponds to multiple functional modules, and each functional module corresponds to multiple single indicators;

[0095] In step S102, the measured index value of each single index is obtained;

[0096] In step S103, for each single indicator, according to the classical domain and the section domain of the preset state risk level of the single indicator and the measured indicator value of the single indicator, a first correlation degree between the single indicator and each state risk level is calculated;

[0097] In step S104, the state risk level with the highest first correlation degree is used as the state risk level of the single indicator;

[0098] In step S105, based on the first correlation between each single indicator and each state risk level, the state risk level of each functional module and the state risk level of the electric special vehicle are determined.

[0099] In one possible implementation, the method for processing the operating status of the special electric vehicle can be used in a computer device that can execute the operating status processing of the special electric vehicle, for example, it can be an Internet of Things terminal installed on the special electric vehicle, or an Internet of Things terminal located near the special electric vehicle and capable of communicating with the special electric vehicle.

[0100] In one possible implementation, a special electric vehicle refers to a vehicle specially designed and equipped for specific tasks in the power industry. They play an important role in ensuring power supply, emergency repairs, equipment transportation, etc. For example, the special electric vehicle can be a mobile ring network cabinet vehicle, a mobile energy storage vehicle, a mobile power generation vehicle, and many other vehicles.

[0101] In a possible implementation, in view of the operation and maintenance requirements and operating characteristics of electric special vehicles, this implementation proposes a multi-level operating status evaluation system. Figure 2 A schematic diagram of a multi-level operation status evaluation system provided by an embodiment of the present disclosure is shown, Figure 2 As shown, the multi-level operation status evaluation system includes three levels of operation status evaluation: the status of a single indicator, the status of a functional module, and the status of an electric special vehicle. Each electric special vehicle corresponds to multiple functional modules, and each functional module corresponds to multiple single indicators. The status evaluation of the functional module in the middle enables the operation and maintenance personnel to quickly locate the fault site and obtain an overall understanding of the status of the functional module, which is convenient for rapid judgment and disposal of the fault.

[0102] In a possible implementation, the functional module may be a plurality of modules divided according to the functions of the vehicle, and the single indicator is an indicator selected by experts in this field by studying the influencing factors of each functional module and following the principles of completeness, typicality, comparability, operability, and quantifiability. For example, for a mobile power generation vehicle, it may correspond to multiple functional modules such as a vehicle chassis, a generator set, and an environmental monitoring module. For the vehicle chassis, the following single indicators may be set: loading mass, distance from the low point to the ground, climbing angle, turning radius, and other indicators; for the generator set, the following single indicators may be set: voltage, current, power, frequency, power generation, circuit breaker opening and closing positions, oil pressure, coolant temperature, working hours, battery voltage, and other indicators. These indicators are used to monitor and warn of abnormal faults such as over-temperature of the unit coolant, low oil level, overload, abnormal frequency, abnormal voltage, and low unit battery voltage, which helps the staff to handle the fault on the spot; for the environmental monitoring module, the following single indicators may be set: temperature, humidity, vehicle position, and other indicators. Of course, for other types of special electric vehicles, there may be other functional modules. For example, for mobile ring network cabinet vehicles, there may also be corresponding ring network cabinet modules, for mobile energy storage vehicles, there may also be corresponding energy storage modules, and so on. We will not list them one by one here.

[0103] In a possible implementation, the preset status risk level can be pre-set by experts in the field based on actual conditions. For example, the status risk levels of the above three levels of operating status can all be set to three levels: k=1, 2, 3 correspond to the three levels of status risk levels, where k=1 refers to "abnormal", k=2 refers to "warning", and k=3 refers to "normal". Of course, in other examples, the status risk levels of the above three levels of operating status can also be set to 4 or more levels, for example, they can also be set to "normal", "normal warning", "emergency warning", "abnormal", etc., which are not limited here.

[0104] In a possible implementation, the multi-level operating status of the electric special vehicle can be evaluated using the matter-element extension model. Assume that there are M1 single indicators corresponding to the electric special vehicle, where the i-th single indicator can be recorded as s i , i=1,...,M1; the range of the classical domain and the section domain of each single indicator can be determined according to relevant technical specifications, expert knowledge, etc. It should be noted here that, in order to facilitate data processing, the upper and lower limits of the section domain range can be used to normalize the measured indicator values ​​of the section domain, the classical domain, and the subsequent single indicators.

[0105] Here, the classical domain refers to an important concept in the matter-element extension model. When evaluating the operating status of a single indicator, each single indicator can be evaluated according to the preset state risk level. Each single indicator has a standard value range corresponding to each level. This range is the classical domain. The classical domain of the above M1 single indicators can be expressed as:

[0106]

[0107] Among them, N k Refers to the state risk level of a single indicator, S = s1, M1 is a single indicator, Q k Refers to the upper and lower thresholds of the classic domain of the corresponding single indicator, for example, is the lower threshold of the single indicator s1 in the classic domain of the k-th state risk level, It is the upper threshold of the single indicator s1 in the classical domain of the kth state risk level.

[0108] The domain refers to the range of all possible values ​​of the single indicator, which is obtained by taking the union of the classic domains of the three-level state risk level A=A1∪A2∪A3, which can be expressed as:

[0109]

[0110] Where N refers to the set of state risk levels of a single indicator, S = s1, There are M1 single indicators, Q refers to the upper and lower thresholds of the section domain of the corresponding single indicator. For example, a1 is the lower threshold of the section domain of the single indicator s1, and b1 is the upper threshold of the section domain of the single indicator s1.

[0111] In a possible implementation, the measured index values ​​of M1 single indicators can be expressed as:

[0112]

[0113] Among them, N0 refers to the state risk level of all single indicators. <s>Refers to the measured index value of a single index quantity S, for example, <s1>It is the measured index value of the single index s1.

[0114] In a possible implementation, for each single indicator, the measured indicator value of the single indicator can be determined based on the measured indicator value of the single indicator to determine within which state risk level's classical domain range the measured indicator value of the single indicator is located, and combined with the position of the measured indicator value of the single indicator within the classical domain range and the position within the node domain range of the single indicator, the first correlation degree between the single indicator and each state risk level can be comprehensively determined, and the state risk level with the highest first correlation degree can be used as the state risk level of the single indicator.

[0115] In a possible implementation, the state risk level of each functional module and the state risk level of the electric special vehicle can be determined according to the first correlation between each single indicator and each state risk level. For example, for multiple single indicators in each functional module, the state risk level with the highest first correlation can be obtained as the state risk level of the single indicator, and the state risk level of the functional module can be determined according to the state risk levels of the multiple single indicators in the functional module and the importance of the multiple single indicators in the functional module to the functional module; then, the state risk level of the electric special vehicle can be determined according to the state risk level of each functional module and the importance of each functional module to the electric special vehicle.

[0116] This implementation method proposes a multi-level evaluation system, which integrates single indicators, functional modules and operation status evaluation of special electric vehicles, and realizes multi-granularity status monitoring and evaluation from fine to coarse. At the same time, this implementation method makes full use of the physical element extension model, fully combines it with the operation status evaluation at all levels, and uses the correlation of the physical element extension model to characterize the state risk level of a single indicator. Then, based on the first correlation between the single indicator and each state risk level, the state risk level of each functional module and the state risk level of the special electric vehicle are determined, which simplifies the operation status evaluation steps at all levels and can be conveniently deployed in the Internet of Things terminal corresponding to the special electric vehicle, thereby improving the intelligent operation and maintenance capabilities of the special electric vehicle, realizing comprehensive, real-time and accurate monitoring of the vehicle operation status, and realizing pre-fault warning, in-process monitoring and post-fault review based on the state risk levels at all levels.

[0117] In a possible implementation, determining the state risk level of the functional module and the state risk level of the electric special vehicle based on the first correlation between each single indicator and each state risk level includes:

[0118] For each functional module, based on the first correlations between the plurality of single indicators corresponding to the functional module and the risk levels of each state, determine a second correlation between the functional module and the risk levels of each state;

[0119] Using the state risk level with the second highest correlation as the state risk level of the functional module;

[0120] Based on the second correlation between each functional module and each state risk level, determining the third correlation between the electric special vehicle and each state risk level;

[0121] The third state risk level with the highest correlation degree is used as the state risk level of the electric special vehicle.

[0122] In this embodiment, for each functional module, a weighted calculation can be performed on the first correlation between the multiple single indicators corresponding to the functional module and the corresponding state risk level based on the importance of the multiple single indicators corresponding to the functional module to the functional module, so as to obtain a second correlation between the functional module and the corresponding state risk level; the state risk level with the highest second correlation is taken as the state risk level of the functional module, and then, based on the importance of the multiple functional modules corresponding to the special electric vehicle to the special electric vehicle, a weighted calculation is performed on the second correlation between the multiple functional modules corresponding to the special electric vehicle and the corresponding state risk level, so as to obtain a third correlation between the special electric vehicle and the corresponding state risk level; the state risk level with the highest third correlation is taken as the state risk level of the special electric vehicle.

[0123] In a possible implementation, the calculating the first correlation between the single indicator and each state risk level according to the single indicator in the classical domain and the section domain of the preset state risk level and the measured indicator value of the single indicator includes:

[0124] Calculate the i-th single indicator s according to the following formula i The first correlation with the k-th state risk level

[0125]

[0126] in, and They are single indicators s i The lower and upper thresholds of the classic domain of the k-th state risk level, a i and b i They are single indicators s i The lower and upper thresholds of the section domain, i > for the single indicator s i The measured index value.

[0127] ​In this embodiment, it is assumed that there are three levels of preset status risk levels: k=1, 2, and 3 correspond to the three levels of status risk levels, where k=1 refers to "abnormal", k=2 refers to "warning", and k=3 refers to "normal". Then the i-th single indicator s i The status risk level is The corresponding k value.

[0128] In a possible implementation, determining the second correlation between the functional module and each state risk level based on the first correlation between the plurality of single indicators corresponding to the functional module and each state risk level includes:

[0129] Using a hierarchical analysis method, obtaining a first judgment matrix corresponding to the functional module, and calculating a basic weight of each single indicator in the functional module based on the first judgment matrix, wherein the first judgment matrix is ​​used to represent the importance between multiple single indicators in the functional module;

[0130] The basic weights are modified by using a variable weight comprehensive model based on factor space theory to obtain a variable weight vector of each single indicator in the functional module;

[0131] Based on the variable weight vector and the first correlation between the plurality of single indicators corresponding to the functional module and the risk level of each state, a second correlation between the functional module and the risk level of each state is calculated.

[0132] In this embodiment, it is assumed that the electric special vehicle has a total of M2 functional modules, and the jth functional module has Single indicator j=1,...,M2, the basic weight of the jth functional module can be constructed through expert knowledge and hierarchical analysis method.

[0133] In this embodiment, a first judgment matrix of a single indicator relative to a functional module is first constructed and recorded as The first judgment matrix is ​​used to represent the comparative values ​​of the importance of multiple single indicators in the functional module relative to the functional module. The diagonal elements of the first judgment matrix are all 1, and the remaining elements are the importance of a single indicator compared with another single indicator to the functional module. The more important it is, the greater the value. The AHP (hierarchical analysis method) method can be used to calculate the basic weight of a single indicator. For example, in order to ensure the consistency of the judgment matrix, a consistency test can be performed. If the consistency test passes, each column of the first judgment matrix can be normalized, that is, the elements of each column are divided by the sum of the column; in this way, a new matrix can be obtained, in which the sum of each column is 1; then the average value of each row of the normalized matrix is ​​obtained to obtain a vector, which is the basic weight of each single indicator; if the consistency test fails, the first judgment matrix is ​​readjusted until the consistency test passes, and then the basic weight of each single indicator is calculated.

[0134] In this embodiment, in order to achieve continuous fault warning, the variable weight comprehensive model based on factor space theory can be used to correct the basic weight. The variable weight comprehensive model based on factor space theory is a decision analysis method. It determines the weight of each factor by considering the importance of each factor in the decision and the degree of state balance. The factors in this embodiment are single indicators. The importance and state balance of each single indicator in the functional module can be considered to correct the basic weight of each single indicator and determine the variable weight vector of each single indicator.

[0135] In this implementation, based on the variable weight vector, weighted calculation may be performed on the first correlations between the plurality of single indicators corresponding to the functional module and the corresponding state risk levels to obtain the second correlation between the functional module and each state risk level.

[0136] In a possible implementation, the variable weight comprehensive model based on factor space theory is used to modify the basic weight to obtain the variable weight vector, including:

[0137] According to the following formula, the jth function module is calculated The variable weight vector of a single indicator

[0138] in, is the total number of single indicators in the jth functional module, For the jth functional module The basic weight of a single indicator, i j > is the i-th single indicator s in the j-th functional module i j The measured index value, α is the preset value, a​ i j and b i j are the i-th single indicator s in the j-th functional module i j The upper and lower thresholds of the section domain.

[0139] In a possible implementation, the calculating the second correlation between the functional module and each state risk level based on the variable weight vector and the first correlation between the plurality of single indicators corresponding to the functional module and each state risk level includes:

[0140] The second correlation between the jth functional module and the kth level of state risk level is calculated according to the following formula:

[0141]

[0142] Among them, the is the i-th single indicator s in the j-th functional module i j The first correlation with the k-th state risk level has been calculated previously.

[0143] In this embodiment, it is assumed that there are three levels of preset status risk levels: k=1, 2, and 3 correspond to three levels of status risk levels, where k=1 refers to "abnormal", k=2 refers to "warning", and k=3 refers to "normal". The status risk level of the jth functional module is The corresponding k value.

[0144] In a possible implementation, the determining of the third degree of association between the electric special vehicle and each state risk level based on the second degree of association between each functional module and each state risk level includes:

[0145] A hierarchical analysis method is used to obtain a second judgment matrix corresponding to the electric special vehicle, and a basic weight of each functional module in the electric special vehicle is calculated based on the second judgment matrix, wherein the second judgment matrix is ​​used to represent the importance between multiple functional modules in the electric special vehicle;

[0146] Based on the basic weight of each functional module in the special electric vehicle and the second correlation between each functional module in the special electric vehicle and each state risk level, the third correlation between the special electric vehicle and each state risk level is calculated.

[0147] In this embodiment, it is assumed that the electric special vehicle has a total of M2 functional modules, and the basic weights of the M2 functions are constructed through expert knowledge and hierarchical analysis method.

[0148] In this embodiment, a second judgment matrix of M2 functional modules relative to electric special vehicles is first constructed. The second judgment matrix is ​​used to represent the comparative values ​​of the importance of the M2 functional modules in the special electric vehicle relative to the special electric vehicle. The diagonal elements of the second judgment matrix are all 1, and the remaining elements are the importance of one functional module to the special electric vehicle compared with another functional module. The more important it is, the larger the value is. The hierarchical analysis method can be used to calculate the basic weight of the functional module. The specific calculation process can refer to the calculation of the basic weight of the above-mentioned single indicator, which will not be repeated here.

[0149] In this embodiment, based on the basic weight of each functional module in the electric special vehicle, the second correlation between each functional module in the electric special vehicle and the corresponding state risk level can be weighted calculated to obtain the third correlation between the functional module and the corresponding state risk level.

[0150] In a possible implementation, the calculating of the third correlation between the electric special vehicle and each state risk level based on the basic weight of each functional module in the electric special vehicle and the second correlation between each functional module in the electric special vehicle and each state risk level includes:

[0151] The third correlation R between the electric special vehicle and the k-th level state risk level is calculated according to the following formula: k :

[0152]

[0153] Wherein, M2 is the total number of functional modules in the electric special vehicle, w 0,j is the basic weight of the M2 functional modules in the electric special vehicle, is the second correlation degree between the j-th functional module and the k-th state risk level.

[0154] In this implementation, it is assumed that there are three preset status risk levels: k=1, 2, 3 correspond to three status risk levels, where k=1 refers to "abnormal", k=2 refers to "warning", and k=3 refers to "normal". The status risk level of the electric special vehicle is the k value corresponding to max(R1, R2, R3).

[0155] In a possible implementation, the method further includes:

[0156] When the state risk level of the electric special vehicle is a risk level that requires operation and maintenance work, the state risk level of each single indicator is input into a pre-trained recognition model, and the recognition model is executed to obtain the type of operation and maintenance work output by the recognition model, and the operation and maintenance work type includes edge-side operation and maintenance, cloud-side operation and maintenance, and cloud-edge collaborative operation and maintenance;

[0157] When the operation and maintenance work type is side operation and maintenance, outputting first reminder information, wherein the first reminder information is used to remind side personnel to perform operation and maintenance work;

[0158] When the operation and maintenance work type is cloud-side operation and maintenance, the state risk level of each single indicator of the special electric vehicle, the state risk level of each functional module and the state risk level of the special electric vehicle are sent to the cloud;

[0159] When the operation and maintenance work type is cloud-edge collaborative operation and maintenance, a second reminder message is output to remind the side personnel to perform operation and maintenance work and send the status risk level of each single indicator of the special electric vehicle, the status risk level of each functional module and the status risk level of the special electric vehicle to the cloud.

[0160] In this embodiment, when the state risk level of the electric special vehicle is "abnormal" or "warning", it is the risk level that requires operation and maintenance work. At this time, the side operation and maintenance work diversion strategy can be executed to determine the responsible party who needs to perform the operation and maintenance work under the current state of the electric special vehicle. The state risk level of each single indicator of the electric special vehicle can be input into a pre-trained recognition model. For example, the state risk level of each single indicator can be binary encoded: "00" for "abnormal"; "01" for "warning"; "10" for "normal". The input of the pre-trained recognition model is the state code of all single indicators, and the output code "100" corresponds to side operation and maintenance, the output code "010" for cloud-side operation and maintenance, and the output code "001" corresponds to cloud-edge collaborative operation and maintenance. It should be noted here that the recognition model can be a neural network model, which can be obtained through training samples. The training samples can be the state risk level of the single indicator of the electric special vehicle in the historical time period and the actual operation and maintenance work type.

[0161] In this implementation, edge-side operation and maintenance refers to the operation and maintenance work that can be completed by edge-side staff, such as drivers, vehicle-following staff, etc. when a vehicle fails; cloud-side operation and maintenance refers to the operation and maintenance work that cannot be completed by edge-side staff, which requires professional operation and maintenance personnel to be dispatched by the cloud to go for repairs; cloud-edge collaborative operation and maintenance means that edge-side staff can keep the vehicle in short-term operation through limited operation and maintenance capabilities, but professional operation and maintenance personnel need to be dispatched by the cloud to carry out thorough repairs.

[0162] In this embodiment, when the operation and maintenance work type identified by the recognition model is edge operation and maintenance, the first reminder information can be directly output, and the first reminder information is used to remind the edge personnel to perform operation and maintenance work; if the operation and maintenance work type identified by the recognition model is cloud-side operation and maintenance, the status risk level of each single indicator of the special electric vehicle, the status risk level of each functional module, and the status risk level of the special electric vehicle are sent to the cloud, and the cloud will dispatch professional maintenance personnel accordingly; if the operation and maintenance work type identified by the recognition model is cloud-edge collaborative operation and maintenance, the second reminder information can be output immediately to remind the edge staff to inspect the vehicle, keep the vehicle in normal operation, and keep the vehicle in a short-term operating state, and the status risk level of each single indicator of the special electric vehicle, the status risk level of each functional module, and the status risk level of the special electric vehicle are sent to the cloud, and the cloud will dispatch professional maintenance personnel to perform a thorough maintenance of the vehicle accordingly.

[0163] In this implementation, the edge IoT terminal has edge computing capabilities and AI modules, which can realize on-site intelligent analysis of the terminal. In order to enhance the edge data analysis capabilities, reduce the demand for edge communication resources, and alleviate the pressure on cloud computing, this implementation can use the edge IoT terminal to implement an edge operation and maintenance work diversion strategy to achieve rapid response to faults and precise allocation of operation and maintenance work.

[0164] Figure 3 The structure block diagram of the running state processing device of the electric special vehicle provided by the embodiment of the present disclosure is shown. The device can be implemented as part or all of the electronic device through software, hardware or a combination of both. Figure 3 As shown, the running state processing device of the electric special vehicle includes:

[0165] The first acquisition module 301 is configured to acquire each single indicator of the electric special vehicle in the classical domain and the section domain of the preset state risk level, wherein the electric special vehicle corresponds to a plurality of functional modules, and each functional module corresponds to a plurality of single indicators;

[0166] The second acquisition module 302 is configured to obtain the measured indicator value of each single indicator;

[0167] The calculation module 303 is configured to calculate, for each single indicator, a first correlation degree between the single indicator and each state risk level according to the single indicator in the classical domain and the section domain of the preset state risk level and the measured indicator value of the single indicator;

[0168] A first level determination module 304 is configured to use the state risk level with the highest first correlation as the state risk level of the single indicator;

[0169] The second level determination module 305 is configured to determine the state risk level of each functional module and the state risk level of the electric special vehicle based on the first correlation between each single indicator and each state risk level.

[0170] In a possible implementation manner, the second level determination module is configured to:

[0171] For each functional module, based on the first correlations between the plurality of single indicators corresponding to the functional module and the risk levels of each state, determine a second correlation between the functional module and the risk levels of each state;

[0172] Using the state risk level with the second highest correlation as the state risk level of the functional module;

[0173] Based on the second correlation between each functional module and each state risk level, determining the third correlation between the electric special vehicle and each state risk level;

[0174] The state risk level with the third highest correlation degree is used as the state risk level of the electric special vehicle.

[0175] In a possible implementation, the calculation module is configured as follows:

[0176] Calculate the i-th single indicator s according to the following formula i The first correlation with the k-th state risk level

[0177]

[0178] in, and They are single indicators s i The lower and upper thresholds of the classic domain of the k-th state risk level, a i and b i They are single indicators s i The lower and upper thresholds of the section domain, i > for the single indicator s i The measured index value.

[0179] In a possible implementation manner, the part of the second level determination module that determines the second degree of association between the functional module and each state risk level based on the first degree of association between the multiple single indicators corresponding to the functional module and each state risk level is configured as follows:

[0180] ​Using a hierarchical analysis method, obtaining a first judgment matrix corresponding to the functional module, and calculating a basic weight of each single indicator in the functional module based on the first judgment matrix, wherein the first judgment matrix is ​​used to represent a comparison value of the importance between multiple single indicators in the functional module;

[0181] The basic weights of the single indicators are modified by using a variable weight comprehensive model based on factor space theory to obtain the variable weight vectors of the single indicators in the functional module;

[0182] Based on the variable weight vector and the first correlation between the plurality of single indicators corresponding to the functional module and the risk level of each state, a second correlation between the functional module and the risk level of each state is calculated.

[0183] In a possible implementation manner, the second level determination module adopts a variable weight comprehensive model based on factor space theory to correct the basic weights of the single indicators, and the part of obtaining the variable weight vector of each single indicator in the functional module is configured as follows:

[0184] According to the following formula, the jth function module is calculated The variable weight vector of a single indicator

[0185] in, is the total number of single indicators in the jth functional module, For the jth functional module The basic weight of a single indicator, i j > is the i-th single indicator s in the j-th functional module i j The measured index value, α is the preset value, a i j and b i j are the i-th single indicator s in the j-th functional module i j The upper and lower thresholds of the section domain.

[0186] In a possible implementation manner, the part of the second level determination module that calculates the second correlation between the functional module and each state risk level based on the variable weight vector and the first correlation between the plurality of single indicators corresponding to the functional module and each state risk level is configured as follows:

[0187] The second correlation between the jth functional module and the kth level of state risk level is calculated according to the following formula:

[0188] ​

[0189] in, is the i-th single indicator s in the j-th functional module i j The first degree of association with the k-th state risk level.

[0190] In a possible implementation manner, the part of the second level determination module that determines the third degree of association between the electric special vehicle and each state risk level based on the second degree of association between each functional module and each state risk level is configured as follows:

[0191] A hierarchical analysis method is used to obtain a second judgment matrix corresponding to the electric special vehicle, and a basic weight of each functional module in the electric special vehicle is calculated based on the second judgment matrix, wherein the second judgment matrix is ​​used to represent a comparison value of the importance between multiple functional modules in the electric special vehicle;

[0192] Based on the basic weight of each functional module in the special electric vehicle and the second correlation between each functional module in the special electric vehicle and each state risk level, the third correlation between the special electric vehicle and each state risk level is calculated.

[0193] In a possible implementation manner, the part of the second level determination module that calculates the third degree of association between the electric special vehicle and each state risk level based on the basic weight of each functional module in the electric special vehicle and the second degree of association between each functional module in the electric special vehicle and each state risk level is configured as follows:

[0194] The third correlation R between the electric special vehicle and the k-th level state risk level is calculated according to the following formula: k :

[0195]

[0196] Wherein, M2 is the total number of functional modules in the electric special vehicle, w 0,j is the basic weight of the jth functional module in the electric special vehicle, is the second correlation degree between the j-th functional module and the k-th state risk level.

[0197] In a possible implementation, the device further includes:

[0198] A type identification module is configured to input the state risk level of each single indicator into a pre-trained identification model when the state risk level of the electric special vehicle is a risk level that requires operation and maintenance work, execute the identification model, and obtain the type of operation and maintenance work output by the identification model, wherein the operation and maintenance work type includes edge-side operation and maintenance, cloud-side operation and maintenance, and cloud-edge collaborative operation and maintenance;

[0199] The output module is configured to output a first reminder message when the operation and maintenance work type is edge operation and maintenance, and the first reminder message is used to remind edge personnel to perform operation and maintenance work; when the operation and maintenance work type is cloud-side operation and maintenance, the status risk level of each single indicator of the special electric vehicle, the status risk level of each functional module, and the status risk level of the special electric vehicle are sent to the cloud; when the operation and maintenance work type is cloud-edge collaborative operation and maintenance, the second reminder message is output to remind edge personnel to perform operation and maintenance work and the status risk level of each single indicator of the special electric vehicle, the status risk level of each functional module, and the status risk level of the special electric vehicle are sent to the cloud.

[0200] The technical terms and technical features mentioned in the implementation manner of the present device are the same as or similar to those mentioned in the implementation manner of the above method. For the explanation and description of the technical terms and technical features involved in the present device, reference may be made to the explanation and description of the implementation manner of the above method, and they will not be repeated here.

[0201] The present disclosure also discloses an electronic device, Figure 4 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0202] like Figure 4 As shown, the electronic device 400 includes a memory 401 and a processor 402, wherein the memory 401 is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor 402 to implement the method according to an embodiment of the present disclosure.

[0203] Figure 5 A schematic diagram showing the structure of a computer system suitable for implementing the method of the embodiment of the present disclosure is shown.

[0204] like Figure 5 As shown, the computer system 500 includes a processing unit 501, which can perform various processes in the above-mentioned embodiments according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the computer system 500 are also stored. The processing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0205] The following components are connected to the I / O interface 505: an input part 506 including a keyboard, a mouse, etc.; an output part 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage part 508 including a hard disk, etc.; and a communication part 509 including a network interface card such as a LAN card, a modem, etc. The communication part 509 performs communication processing via a network such as the Internet. The drive 510 is also connected to the I / O interface 505 as needed. Removable media 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., are installed on the drive 510 as needed, so that the computer program read therefrom is installed into the storage part 508 as needed. Among them, the processing unit 501 can be implemented as a processing unit such as a CPU, a GPU, a TPU, an FPGA, an NPU, etc.

[0206] In particular, according to an embodiment of the present disclosure, the method described above can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes computer instructions, and the computer instructions are executed by a processor to implement the method steps described above. In such an embodiment, the computer program product can be downloaded and installed from a network through the communication part 509, and / or installed from a removable medium 511.

[0207] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0208] The units or modules involved in the embodiments described in the present disclosure may be implemented by software or programmable hardware. The units or modules described may also be set in a processor, and the names of these units or modules do not constitute limitations on the units or modules themselves in some cases.

[0209] As another aspect, the present disclosure further provides a computer-readable storage medium, which may be a computer-readable storage medium included in the electronic device or computer system in the above embodiment; or a computer-readable storage medium that exists independently and is not assembled into a device. The computer-readable storage medium stores one or more programs, and the programs are used by one or more processors to execute the method described in the present disclosure.

[0210] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present disclosure is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other. < / s>

Claims

1. A method for processing the operating status of an electric special vehicle, characterized in that: include: Obtaining each single indicator of the electric special vehicle in the classic domain and the section domain of the preset state risk level, wherein the electric special vehicle corresponds to a plurality of functional modules, and each functional module corresponds to a plurality of single indicators; Get the measured indicator value of each single indicator; For each single indicator, according to the classical domain and the section domain of the preset state risk level of the single indicator and the measured indicator value of the single indicator, calculate the first correlation degree between the single indicator and each state risk level; Taking the state risk level with the highest first correlation as the state risk level of the single indicator; Based on the first correlation between each single indicator and each state risk level, the state risk level of each functional module and the state risk level of the electric special vehicle are determined.

2. The method according to claim 1, characterized in that The determining of the state risk level of each functional module and the state risk level of the electric special vehicle based on the first correlation between each single indicator and each state risk level includes: For each functional module, based on the first correlations between the plurality of single indicators corresponding to the functional module and the risk levels of each state, determine a second correlation between the functional module and the risk levels of each state; Using the state risk level with the second highest correlation as the state risk level of the functional module; Based on the second correlation between each functional module and each state risk level, determining the third correlation between the electric special vehicle and each state risk level; The state risk level with the third highest correlation degree is used as the state risk level of the electric special vehicle.

3. The method according to claim 1, characterized in that The calculating, according to the single indicator in the classical domain and the section domain of the preset state risk level and the measured indicator value of the single indicator, the first correlation degree between the single indicator and each state risk level comprises: Calculate the i-th single indicator s according to the following formula i The first correlation with the k-th state risk level in, and They are single indicators s i The lower and upper thresholds of the classic domain of the k-th state risk level, a i and b i They are single indicators s i The lower and upper thresholds of the section domain, i > for the single indicator s i The measured index value.​ 4. The method according to claim 2, characterized in that: The determining, based on the first correlation between the plurality of single indicators corresponding to the functional modules and the risk levels of the respective states, the second correlation between the functional modules and the risk levels of the respective states comprises: Using a hierarchical analysis method, obtaining a first judgment matrix corresponding to the functional module, and calculating a basic weight of each single indicator in the functional module based on the first judgment matrix, wherein the first judgment matrix is ​​used to represent a comparison value of the importance between multiple single indicators in the functional module; The basic weights of the single indicators are modified by using a variable weight comprehensive model based on factor space theory to obtain the variable weight vectors of the single indicators in the functional module; Based on the variable weight vector and the first correlation between the plurality of single indicators corresponding to the functional module and the risk level of each state, a second correlation between the functional module and the risk level of each state is calculated.

5. The method according to claim 4, characterized in that The variable weight comprehensive model based on factor space theory is used to modify the basic weight of each single indicator to obtain the variable weight vector of each single indicator in the functional module, including: According to the following formula, the jth function module is calculated The variable weight vector of a single indicator in, is the total number of single indicators in the jth functional module, For the jth functional module The basic weight of a single indicator, i j > is the i-th single indicator in the j-th functional module The measured index value, α is the preset value, and are the i-th single indicator s in the j-th functional module i j The upper and lower thresholds of the section domain.​ 6. The method according to claim 5, characterized in that The calculating the second correlation between the functional module and each state risk level based on the variable weight vector and the first correlation between the plurality of single indicators corresponding to the functional module and each state risk level includes: The second correlation between the jth functional module and the kth level of state risk level is calculated according to the following formula: in, is the i-th single indicator s in the j-th functional module i j The first degree of association with the k-th state risk level.

7. The method according to claim 2, characterized in that The determining of the third degree of association between the electric special vehicle and each state risk level based on the second degree of association between each functional module and each state risk level includes: A hierarchical analysis method is used to obtain a second judgment matrix corresponding to the electric special vehicle, and a basic weight of each functional module in the electric special vehicle is calculated based on the second judgment matrix, wherein the second judgment matrix is ​​used to represent a comparison value of the importance between multiple functional modules in the electric special vehicle; Based on the basic weight of each functional module in the special electric vehicle and the second correlation between each functional module in the special electric vehicle and each state risk level, the third correlation between the special electric vehicle and each state risk level is calculated.

8. The method according to claim 7, characterized in that The calculating of the third correlation between the electric special vehicle and each state risk level based on the basic weight of each functional module in the electric special vehicle and the second correlation between each functional module in the electric special vehicle and each state risk level includes: The third correlation R between the electric special vehicle and the k-th level state risk level is calculated according to the following formula: k : Wherein, M2 is the total number of functional modules in the electric special vehicle, w 0,j is the basic weight of the jth functional module in the electric special vehicle, is the second correlation degree between the j-th functional module and the k-th state risk level.

9. The method according to claim 1, characterized in that: The method further comprises: When the state risk level of the electric special vehicle is a risk level that requires operation and maintenance work, the state risk level of each single indicator is input into a pre-trained recognition model, and the recognition model is executed to obtain the type of operation and maintenance work output by the recognition model, and the operation and maintenance work type includes edge-side operation and maintenance, cloud-side operation and maintenance, and cloud-edge collaborative operation and maintenance; When the operation and maintenance work type is side operation and maintenance, outputting first reminder information, wherein the first reminder information is used to remind side personnel to perform operation and maintenance work; When the operation and maintenance work type is cloud-side operation and maintenance, the state risk level of each single indicator of the special electric vehicle, the state risk level of each functional module and the state risk level of the special electric vehicle are sent to the cloud; When the operation and maintenance work type is cloud-edge collaborative operation and maintenance, a second reminder message is output to remind the side personnel to perform operation and maintenance work and send the status risk level of each single indicator of the special electric vehicle, the status risk level of each functional module and the status risk level of the special electric vehicle to the cloud.

10. A running state processing device for electric special vehicles, characterized in that: include: A first acquisition module is configured to acquire each single indicator of the electric special vehicle in a classic domain and a section domain of a preset state risk level, wherein the electric special vehicle corresponds to a plurality of functional modules, and each functional module corresponds to a plurality of single indicators; A second acquisition module is configured to obtain the measured indicator value of each single indicator; A calculation module is configured to calculate, for each single indicator, a first correlation degree between the single indicator and each state risk level according to the single indicator in a preset classical domain and a section domain of the state risk level and a measured indicator value of the single indicator; A first level determination module is configured to use the state risk level with the highest first correlation as the state risk level of the single indicator; The second level determination module is configured to determine the state risk level of each functional module and the state risk level of the electric special vehicle based on the first correlation between each single indicator and each state risk level.

11. The device according to claim 10, characterized in that The second level determination module is configured to: For each functional module, based on the first correlations between the plurality of single indicators corresponding to the functional module and the risk levels of each state, determine a second correlation between the functional module and the risk levels of each state; Using the state risk level with the second highest correlation as the state risk level of the functional module; Based on the second correlation between each functional module and each state risk level, determining the third correlation between the electric special vehicle and each state risk level; The state risk level with the third highest correlation degree is used as the state risk level of the electric special vehicle.

12. The device according to claim 10, characterized in that The computing module is configured as follows: Calculate the i-th single indicator s according to the following formula i The first correlation with the k-th state risk level in, and They are single indicators s i The lower and upper thresholds of the classic domain of the k-th state risk level, a i and b i They are single indicators s i The lower and upper thresholds of the section domain, i > for the single indicator s i The measured index value.​ 13. The device according to claim 11, characterized in that The part of the second level determination module that determines the second correlation between the functional module and each state risk level based on the first correlation between the multiple single indicators corresponding to the functional module and each state risk level is configured as follows: Using a hierarchical analysis method, obtaining a first judgment matrix corresponding to the functional module, and calculating a basic weight of each single indicator in the functional module based on the first judgment matrix, wherein the first judgment matrix is ​​used to represent a comparison value of the importance between multiple single indicators in the functional module; The basic weights of the single indicators are modified by using a variable weight comprehensive model based on factor space theory to obtain the variable weight vectors of the single indicators in the functional module; Based on the variable weight vector and the first correlation between the plurality of single indicators corresponding to the functional module and the risk level of each state, a second correlation between the functional module and the risk level of each state is calculated.

14. The device according to claim 13, characterized in that The second level determination module adopts a variable weight comprehensive model based on factor space theory to correct the basic weights of the single indicators, and the part of the variable weight vector of each single indicator in the functional module is configured as follows: According to the following formula, the jth function module is calculated The variable weight vector of a single indicator in, is the total number of single indicators in the jth functional module, For the jth functional module The basic weight of a single indicator, i j > is the i-th single indicator s in the j-th functional module i j The measured index value, α is the preset value, a i j and b i j are the i-th single indicator s in the j-th functional module i j The upper and lower thresholds of the section domain.​ 15. The device according to claim 14, characterized in that The part of the second level determination module that calculates the second correlation between the functional module and each state risk level based on the variable weight vector and the first correlation between the plurality of single indicators corresponding to the functional module and each state risk level is configured as follows: The second correlation between the jth functional module and the kth level of state risk level is calculated according to the following formula: in, is the i-th single indicator s in the j-th functional module i j The first degree of association with the k-th state risk level.

16. The device according to claim 11, characterized in that The part of the second level determination module that determines the third degree of association between the electric special vehicle and each state risk level based on the second degree of association between each functional module and each state risk level is configured as follows: A hierarchical analysis method is used to obtain a second judgment matrix corresponding to the electric special vehicle, and a basic weight of each functional module in the electric special vehicle is calculated based on the second judgment matrix, wherein the second judgment matrix is ​​used to represent a comparison value of the importance between multiple functional modules in the electric special vehicle; Based on the basic weight of each functional module in the special electric vehicle and the second correlation between each functional module in the special electric vehicle and each state risk level, the third correlation between the special electric vehicle and each state risk level is calculated.

17. The device according to claim 16, characterized in that The part of the second level determination module that calculates the third correlation between the electric special vehicle and each state risk level based on the basic weight of each functional module in the electric special vehicle and the second correlation between each functional module in the electric special vehicle and each state risk level is configured as follows: The third correlation R between the electric special vehicle and the k-th level state risk level is calculated according to the following formula: k : Wherein, M2 is the total number of functional modules in the electric special vehicle, w 0,j is the basic weight of the jth functional module in the electric special vehicle, is the second correlation degree between the j-th functional module and the k-th state risk level.

18. The device according to claim 10, characterized in that The device also includes: A type identification module is configured to input the state risk level of each single indicator into a pre-trained identification model when the state risk level of the electric special vehicle is a risk level that requires operation and maintenance work, execute the identification model, and obtain the type of operation and maintenance work output by the identification model, wherein the operation and maintenance work type includes edge-side operation and maintenance, cloud-side operation and maintenance, and cloud-edge collaborative operation and maintenance; The output module is configured to output a first reminder message when the operation and maintenance work type is edge operation and maintenance, and the first reminder message is used to remind edge personnel to perform operation and maintenance work; when the operation and maintenance work type is cloud-side operation and maintenance, the status risk level of each single indicator of the special electric vehicle, the status risk level of each functional module, and the status risk level of the special electric vehicle are sent to the cloud; when the operation and maintenance work type is cloud-edge collaborative operation and maintenance, the second reminder message is output to remind edge personnel to perform operation and maintenance work and the status risk level of each single indicator of the special electric vehicle, the status risk level of each functional module, and the status risk level of the special electric vehicle are sent to the cloud.

19. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method according to any one of claims 1 to 9.

20. A readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed by a processor, the method described in any one of claims 1 to 9 is implemented.

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