Vehicle abnormal driving energy consumption identification method, device, equipment, medium and product

Abstract

The invention discloses a method, a device, equipment, a medium and a product for identifying abnormal driving energy consumption of a vehicle, and relates to the technical field of vehicle engineering.The method comprises the steps that target driving working condition data and target driving road condition data corresponding to a to-be-detected vehicle in a to-be-detected travel section are acquired; according to the target driving condition data and the target driving road condition data, clustering the to-be-detected travel segment, and determining a target cluster to which the to-be-detected travel segment belongs; and determining a target energy consumption baseline value corresponding to the target cluster, determining a to-be-detected energy consumption value generated by the to-be-detected vehicle in the to-be-detected travel section, and determining whether the to-be-detected vehicle has abnormal driving energy consumption in the to-be-detected travel section according to the target energy consumption baseline value and the to-be-detected energy consumption value. According to the method, the dynamic target energy consumption baseline value is used for identifying the abnormal driving energy consumption of the vehicle, and compared with the prior art that a single fixed energy consumption baseline value is used for identifying the abnormal driving energy consumption of the vehicle, the abnormal driving energy consumption identification accuracy is improved, and the phenomenon of misidentification is reduced.

Description

Technical Field

[0001] The present invention relates to the field of vehicle engineering technology, and in particular to a method, device, equipment, medium and product for identifying abnormal vehicle driving energy consumption. Background Art

[0002] In the field of commercial vehicle operations, energy consumption costs account for a considerable proportion. Accurately identifying abnormal energy consumption of vehicles is of vital importance to reducing operating costs and improving energy efficiency.

[0003] Existing technologies usually use a single fixed energy consumption baseline value to identify abnormal driving energy consumption of a vehicle, which has the problem of low recognition accuracy and is prone to misidentification. Summary of the Invention

[0004] The present invention provides a method, device, equipment, medium and product for identifying abnormal vehicle driving energy consumption, so as to solve the problems of low recognition accuracy and easy misidentification in existing abnormal vehicle driving energy consumption identification.

[0005] According to one aspect of the present invention, a method for identifying abnormal vehicle driving energy consumption is provided, the method comprising:

[0006] Obtaining target driving condition data corresponding to the vehicle to be tested in the travel section to be tested, as well as target driving road condition data;

[0007] Clustering the to-be-detected travel segment according to the target driving condition data and the target driving road condition data to determine a target cluster to which the to-be-detected travel segment belongs;

[0008] Determine the target energy consumption baseline value corresponding to the target cluster, and determine the energy consumption value to be detected generated by the vehicle to be detected in the travel segment to be detected, and determine whether the vehicle to be detected has abnormal driving energy consumption in the travel segment to be detected based on the target energy consumption baseline value and the energy consumption value to be detected.

[0009] According to another aspect of the present invention, a device for identifying abnormal vehicle energy consumption is provided, the device comprising:

[0010] A data acquisition module is used to obtain target driving condition data corresponding to the vehicle to be tested in the travel section to be tested, as well as target driving road condition data;

[0011] a clustering module, configured to cluster the to-be-detected travel segment according to the target driving condition data and the target driving road condition data, and determine a target cluster to which the to-be-detected travel segment belongs;

[0012] The abnormal driving energy consumption identification module is used to determine the target energy consumption baseline value corresponding to the target cluster, and to determine the energy consumption value to be detected generated by the vehicle to be detected in the travel segment to be detected, and to determine whether the vehicle to be detected has abnormal driving energy consumption in the travel segment to be detected based on the target energy consumption baseline value and the energy consumption value to be detected.

[0013] According to another aspect of the present invention, an electronic device is provided, comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed 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 identifying abnormal vehicle driving energy consumption as described in any one of the present inventions.

[0017] 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 identifying abnormal vehicle driving energy consumption as described in any one of the present inventions when executed.

[0018] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method for identifying abnormal vehicle driving energy consumption according to any one of the present inventions.

[0019] The present invention obtains target driving condition data and target driving road condition data corresponding to a to-be-detected vehicle in a to-be-detected travel segment; clusters the to-be-detected travel segment according to the target driving condition data and the target driving road condition data to determine a target cluster to which the to-be-detected travel segment belongs; determines a target energy consumption baseline value corresponding to the target cluster, and determines an energy consumption value to be detected generated by the to-be-detected vehicle in the to-be-detected travel segment; and determines whether the to-be-detected vehicle has abnormal driving energy consumption in the to-be-detected travel segment based on the target energy consumption baseline value and the energy consumption value to be detected. The beneficial effects are:

[0020] Since the target energy consumption baseline value is dynamically determined based on the target driving condition data and the target driving road condition data, the present invention uses the dynamic target energy consumption baseline value to identify the vehicle's abnormal driving energy consumption. Compared with the prior art that uses a single fixed energy consumption baseline value to identify the vehicle's abnormal driving energy consumption, the accuracy of identifying abnormal driving energy consumption is improved and the phenomenon of misidentification is reduced.

[0021] 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

[0022] 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.

[0023] Figure 1 A flowchart of a method for identifying abnormal vehicle driving energy consumption provided in the first embodiment of the present invention;

[0024] Figure 2 This is a flow chart of a method for identifying abnormal vehicle driving energy consumption provided in the second embodiment of the present invention;

[0025] Figure 3 A schematic diagram of the structure of a device for identifying abnormal vehicle energy consumption provided by a third embodiment of the present invention;

[0026] Figure 4 The present invention is a schematic diagram of the structure of an electronic device for implementing the method for identifying abnormal vehicle driving energy consumption according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] 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.

[0028] It should be noted that the terms "candidate", "target", 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 terms used in this way are interchangeable 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 that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0029] Example 1

[0030] Figure 1 This is a flow chart of a method for identifying abnormal vehicle driving energy consumption provided by the first embodiment of the present invention. This embodiment is applicable to the case of identifying whether a vehicle to be detected has abnormal driving energy consumption during a travel section to be detected. The method can be executed by a device for identifying abnormal vehicle driving energy consumption. The device for identifying abnormal vehicle driving energy consumption can be implemented in the form of hardware and / or software, such as by using an onboard computer. Figure 1 As shown, the method includes:

[0031] S101. Obtain target driving condition data corresponding to the to-be-detected vehicle in the to-be-detected travel segment, as well as target driving road condition data.

[0032] The vehicle to be tested refers to a specific vehicle that needs to be analyzed and evaluated for abnormal driving energy consumption. The trip segment to be tested refers to a specific driving range defined during the driving of the vehicle to be tested that needs to be analyzed and evaluated for abnormal driving energy consumption. The trip segment to be tested can be divided based on the ignition and shutdown operations of the vehicle to be tested, such as the complete trip segment of the vehicle to be tested between a set of ignition and shutdown operations as the trip segment to be tested. The trip segment to be tested can also be a segment intercepted from a complete trip segment, etc. This embodiment does not limit the specific method for identifying the trip segment to be tested.

[0033] The target driving condition data is a time series feature that describes the motion state of the vehicle to be tested in the travel segment to be tested, including but not limited to average speed, average torque, average speed, fuel consumption per 100 kilometers, vehicle load, etc. This embodiment does not limit the specific type of the target driving condition data.

[0034] The target driving road condition data describes the external environment and road conditions of the vehicle to be tested in the travel segment to be tested, including but not limited to the road type ratio, terrain ratio, traffic congestion conditions, road slope information, etc. This embodiment does not limit the specific type of the target driving road condition data.

[0035] In one embodiment, basic driving condition data (e.g., GPS speed, torque, rotational speed, fuel consumption, etc.) and basic road condition data (e.g., latitude and longitude, slope, terrain, traffic conditions, and road type) are extracted from the vehicle's onboard remote communication terminal during the test segment. Furthermore, the basic driving condition data and basic road condition data are cleaned to identify data with obvious errors or anomalies. Missing data is also addressed, with linear interpolation and other methods used to supplement short periods of missing data. Longer periods of missing data are marked as invalid segments and temporarily excluded from analysis, thereby obtaining optimized basic driving condition data and basic road condition data.

[0036] Furthermore, the optimized basic driving condition data are statistically analyzed to determine the target driving condition data, and the optimized basic driving road condition data are statistically analyzed to determine the target driving road condition data.

[0037] S102 : Clustering the to-be-detected travel segment according to the target driving condition data and the target driving road condition data to determine the target cluster to which the to-be-detected travel segment belongs.

[0038] Clustering the segments to be tested refers to grouping them into groups with common patterns based on their similar characteristics. A target cluster is a collection of segments identified through clustering that share similar characteristics with the segments to be tested.

[0039] In one embodiment, a driving condition feature vector is determined based on the target driving condition data, and a driving road condition feature vector is determined based on the target driving road condition data. Furthermore, based on the driving condition feature vector and the driving road condition feature vector, a predetermined clustering algorithm, such as a density-based spatial clustering algorithm, is employed to cluster the trip segments to be detected, thereby determining a target cluster to which the trip segment to be detected belongs from among the candidate clusters obtained through clustering.

[0040] S103. Determine the target energy consumption baseline value corresponding to the target cluster, and determine the energy consumption value to be detected generated by the vehicle to be detected in the travel segment to be detected, and determine whether the vehicle to be detected has abnormal driving energy consumption in the travel segment to be detected based on the target energy consumption baseline value and the energy consumption value to be detected.

[0041] The process involves determining the historical travel segments of the vehicles under test that are included in each candidate cluster. A statistical analysis is then performed on the energy consumption values ​​of these segments, and a target percentile value is determined as the candidate energy consumption baseline value for each candidate cluster. The candidate energy consumption baseline value is a reasonable energy consumption reference standard set for the corresponding candidate cluster, used to assess whether the actual energy consumption of the segments within each candidate cluster is within a normal range. It is understood that the candidate energy consumption baseline value for the target cluster is used as the target energy consumption baseline value.

[0042] The energy consumption value to be tested generated by the vehicle to be tested during the travel section to be tested refers to the total energy consumption consumed by the vehicle to be tested during the travel section to be tested. The energy consumption value to be tested can be a fuel consumption value or an electricity consumption value.

[0043] In one embodiment, a target energy consumption baseline value corresponding to the target cluster is determined, and an energy consumption value to be detected generated by the vehicle to be detected during the travel segment to be detected is determined. Furthermore, a numerical comparison is performed between the target energy consumption baseline value and the energy consumption value to be detected. If the energy consumption value to be detected is less than or equal to the target energy consumption baseline value, it indicates that the actual energy consumption of the vehicle to be detected during the travel segment to be detected is within a normal range, i.e., there is no abnormal driving energy consumption. If the energy consumption value to be detected is greater than the target energy consumption baseline value, it indicates that the actual energy consumption of the vehicle to be detected during the travel segment to be detected is outside the normal range, i.e., there is abnormal driving energy consumption.

[0044] The embodiment of the present invention obtains target driving condition data and target driving road condition data corresponding to the to-be-detected vehicle in the to-be-detected travel segment; clusters the to-be-detected travel segment according to the target driving condition data and the target driving road condition data to determine a target cluster to which the to-be-detected travel segment belongs; determines a target energy consumption baseline value corresponding to the target cluster, and determines an energy consumption value to be detected generated by the to-be-detected vehicle in the to-be-detected travel segment; and determines whether the to-be-detected vehicle has abnormal driving energy consumption in the to-be-detected travel segment based on the target energy consumption baseline value and the energy consumption value to be detected. The beneficial effects are:

[0045] Since the target energy consumption baseline value is dynamically determined based on the target driving condition data and the target driving road condition data, the present invention uses the dynamic target energy consumption baseline value to identify the vehicle's abnormal driving energy consumption. Compared with the prior art that uses a single fixed energy consumption baseline value to identify the vehicle's abnormal driving energy consumption, the accuracy of identifying abnormal driving energy consumption is improved and the phenomenon of misidentification is reduced.

[0046] Example 2

[0047] Figure 2 This is a flow chart of a method for identifying abnormal vehicle energy consumption provided by the second embodiment of the present invention. This embodiment further optimizes and expands the above embodiment and can be combined with the above optional implementation methods. Figure 2 As shown, the method includes:

[0048] S201: Obtain target driving condition data corresponding to the to-be-detected vehicle in the to-be-detected travel segment, as well as target driving road condition data.

[0049] S202 : performing feature extraction based on the target driving condition data to determine a driving condition feature vector, and performing feature extraction based on the target driving road condition data to determine a driving road condition feature vector.

[0050] Feature extraction refers to the process of extracting feature vectors representing the vehicle's operating state from target driving cycle data, and extracting feature vectors representing environmental characteristics from target road condition data. The core of this process is to convert target driving cycle data and target road condition data into low-dimensional, interpretable feature vectors through mathematical modeling or algorithmic processing.

[0051] In one embodiment, a feature extraction algorithm is used to extract features from the target driving condition data, and a driving condition feature vector is determined based on the feature extraction results. Furthermore, a feature extraction algorithm is used to extract features from the target driving road condition data, and a driving road condition feature vector is determined based on the feature extraction results.

[0052] S203 , integrating features based on the driving condition feature vector and the driving road condition feature vector to generate an integrated feature vector, clustering the to-be-detected travel segment based on the integrated feature vector, and determining a target cluster to which the to-be-detected travel segment belongs.

[0053] In one embodiment, a feature integration algorithm is used to integrate the driving condition feature vector and the road condition feature vector to generate a multi-dimensional integrated feature vector that comprehensively reflects the vehicle's operating state and environmental characteristics. Furthermore, based on the integrated feature vector, a predetermined clustering algorithm, such as a density-based spatial clustering algorithm, is used to cluster the segments to be tested. The target cluster to which the segment to be tested belongs is determined from the candidate clusters generated by the clustering.

[0054] By extracting features based on target driving condition data to determine a driving condition feature vector, and extracting features based on target driving road condition data to determine a driving road condition feature vector; integrating features based on the driving condition feature vector and the driving road condition feature vector to generate an integrated feature vector, and clustering the to-be-detected travel segments based on the integrated feature vector to determine the target cluster to which the to-be-detected travel segments belong, the beneficial effect is that by integrating the driving condition feature vector and the driving road condition feature vector, the integrated feature vector contains multi-dimensional information, thereby improving the accuracy of identifying abnormal vehicle driving energy consumption.

[0055] S204 , obtaining at least one candidate travel segment generated by the vehicle to be detected included in the target cluster at a historical moment, and determining candidate energy consumption values ​​generated by the vehicle to be detected in each candidate travel segment.

[0056] In addition to the segment to be detected, the target cluster also includes at least one candidate segment generated by the vehicle to be detected at a historical moment. The clustering process for candidate segments is similar to the clustering process for segments to be detected disclosed in this embodiment and will not be further described here. It will be appreciated that since each candidate segment and the segment to be detected belong to the same target cluster, they share similar driving condition and road condition feature vectors. The candidate energy consumption value refers to the total energy consumed by the vehicle to be detected during each candidate segment.

[0057] In one embodiment, the target cluster is parsed to determine at least one candidate trip segment contained therein, and candidate energy consumption values ​​generated by the vehicle to be detected in each candidate trip segment are determined based on label information associated with each candidate trip segment.

[0058] S205 : Determine a target energy consumption baseline value corresponding to the target cluster according to each candidate energy consumption value.

[0059] In one embodiment, a mean value is calculated based on each candidate energy consumption value, and a target energy consumption baseline value corresponding to the target cluster is determined based on the mean value calculation result.

[0060] In another embodiment, a quantile value calculation is performed based on each candidate energy consumption value and the expected quantile, and an expected quantile value corresponding to the expected quantile is determined as the target energy consumption baseline value corresponding to the target cluster.

[0061] In another embodiment, each candidate energy consumption value is weighted according to the candidate generation time of each candidate energy consumption value to generate an optimized energy consumption value, and a target quantile value is calculated based on each optimized energy consumption value and the target quantile as the target energy consumption baseline value corresponding to the target cluster.

[0062] By obtaining at least one candidate travel segment generated by the vehicle to be detected included in the target cluster at a historical moment, and determining the candidate energy consumption value generated by the vehicle to be detected in each candidate travel segment; determining the target energy consumption baseline value corresponding to the target cluster based on each candidate energy consumption value, the beneficial effect is: through the dynamic baseline generation mechanism based on historical energy consumption data, the problem of poor adaptability of traditional static thresholds is solved, and the accuracy of abnormal driving energy consumption identification is improved.

[0063] Optionally, determining a target energy consumption baseline value corresponding to the target cluster based on each candidate energy consumption value includes:

[0064] S2051. Determine the candidate generation time corresponding to each candidate trip segment, and determine the timeliness weight corresponding to each candidate trip segment based on each candidate generation time.

[0065] The candidate generation time refers to the time at which the candidate segment is fully generated and recorded. The timeliness weight is a dynamically calculated weight coefficient based on the freshness of the candidate segment at the time of its generation, which is used to quantify the contribution of each candidate segment to the generation of the target energy consumption baseline value.

[0066] In one embodiment, the candidate time difference between each candidate generation time and the current time is determined, and then the timeliness weight corresponding to each candidate trip segment is determined according to the candidate time difference.

[0067] Optionally, determining the timeliness weight corresponding to each candidate trip segment according to the time when each candidate was generated includes:

[0068] Determine the candidate time difference between each candidate generation time and the current time, and determine the timeliness weight corresponding to each candidate trip segment based on the candidate time difference and the mapping relationship between the time difference and the candidate timeliness weight.

[0069] Among them, the time difference is inversely proportional to the candidate timeliness weight, that is, the smaller the time difference, the greater the contribution of the corresponding candidate trip segment to the generation of the target energy consumption baseline value, and therefore the greater the candidate timeliness weight.

[0070] For example, assuming that the candidate time difference between the candidate generation time and the current time of any candidate trip segment is T1, and assuming that there is a mapping relationship between the time difference T1 and the candidate timeliness weight w1, the timeliness weight corresponding to the candidate trip segment is determined to be w1.

[0071] By determining the candidate time difference between each candidate generation time and the current time, and based on the candidate time difference and the mapping relationship between the time difference and the candidate timeliness weight, the timeliness weight corresponding to each candidate travel segment is determined. The beneficial effect is that: through the mapping relationship between the time difference and the candidate timeliness weight, the newly generated candidate travel segment obtains a higher timeliness weight, realizes the effect of dynamically determining the target energy consumption baseline value, and improves the accuracy of determining the target energy consumption baseline value.

[0072] S2052: Perform weighted calculation on the associated candidate energy consumption values ​​using each timeliness weight to generate optimized energy consumption values ​​respectively, and determine the target energy consumption baseline value corresponding to the target cluster according to each optimized energy consumption value.

[0073] In one embodiment, a weighted calculation is performed on the candidate energy consumption values ​​of each candidate segment based on the timeliness weight of each candidate segment to determine the optimized energy consumption value for each candidate segment. Furthermore, a target energy consumption baseline value corresponding to the target cluster is determined based on the optimized energy consumption value of each candidate segment.

[0074] By determining the candidate generation time corresponding to each candidate travel segment, and determining the timeliness weight corresponding to each candidate travel segment according to each candidate generation time; using each timeliness weight to perform weighted calculation on the respectively associated candidate energy consumption values, respectively generate optimized energy consumption values, and determine the target energy consumption baseline value corresponding to the target cluster according to each optimized energy consumption value. The beneficial effect is: by dynamically determining the timeliness weight corresponding to each candidate travel segment, the dynamic adaptability of the target energy consumption baseline value can be improved. Specifically, the newly generated candidate travel segment will obtain a higher timeliness weight, thereby occupying a dominant position in the weighted calculation, accurately reflecting the real-time working conditions, and avoiding the target energy consumption baseline value from being interfered with by historical data.

[0075] Optionally, determining a target energy consumption baseline value corresponding to the target cluster according to each optimized energy consumption value includes:

[0076] The quantile value is calculated according to each optimized energy consumption value and the target quantile, and the target quantile value corresponding to the target quantile is determined; and the target energy consumption baseline value corresponding to the target cluster is determined according to the target quantile value.

[0077] The target quantile represents a critical value that divides the sorted optimized energy consumption values ​​into a specific ratio. For example, the target quantile may be the 0.95 quantile.

[0078] In one embodiment, a preset target quantile is obtained, and a quantile value calculation is performed based on each optimized energy consumption value and the target quantile to obtain a target quantile value corresponding to the target quantile in the set of optimized energy consumption values, and then the target quantile value is used as the target energy consumption baseline value corresponding to the target cluster.

[0079] By calculating the quantile value based on each optimized energy consumption value and the target quantile, the target quantile value corresponding to the target quantile is determined; the target energy consumption baseline value corresponding to the target cluster is determined based on the target quantile value. The beneficial effect is that the target energy consumption baseline value generated by the target quantile has the ability to resist outlier interference, thereby improving the reliability of identifying abnormal vehicle driving energy consumption.

[0080] S206 , performing a numerical comparison between the target energy consumption baseline value and the energy consumption value to be detected; if the energy consumption value to be detected is greater than the target energy consumption baseline value, determining that the vehicle to be detected has abnormal driving energy consumption in the travel segment to be detected.

[0081] In one embodiment, a numerical comparison is performed between the target energy consumption baseline value and the energy consumption value to be detected. When the energy consumption value to be detected is less than or equal to the target energy consumption baseline value, it indicates that the actual energy consumption of the vehicle to be detected in the journey segment to be detected is within a normal range, that is, there is no abnormal driving energy consumption; when the energy consumption value to be detected is greater than the target energy consumption baseline value, it indicates that the actual energy consumption of the vehicle to be detected in the journey segment to be detected exceeds the normal range, that is, there is abnormal driving energy consumption.

[0082] By comparing the target energy consumption baseline value with the energy consumption value to be tested; if the energy consumption value to be tested is greater than the target energy consumption baseline value, it is determined that the vehicle to be tested has abnormal driving energy consumption in the to-be-tested travel section. The beneficial effects are:

[0083] First, by directly comparing the energy consumption value to be tested with the dynamically generated target energy consumption baseline value, the response speed is improved compared to the traditional machine learning model, and the real-time performance of abnormal driving energy consumption identification is further improved.

[0084] Secondly, accurate identification of abnormal driving energy consumption can provide early warning of potential failures of the vehicle to be tested, which is conducive to reducing the operation and maintenance costs and equipment losses of the vehicle to be tested.

[0085] Example 3

[0086] Figure 3 This is a schematic diagram of a structure of a vehicle abnormal driving energy consumption identification device provided in the third embodiment of the present invention, which can be applied to the situation where the vehicle to be detected has abnormal driving energy consumption in the to-be-detected travel section, such as Figure 3 As shown, the device includes:

[0087] The data acquisition module 31 is used to acquire target driving condition data corresponding to the vehicle to be detected in the travel section to be detected, as well as target driving road condition data;

[0088] a clustering module 32 for clustering the to-be-detected travel segment according to the target driving condition data and the target driving road condition data, and determining a target cluster to which the to-be-detected travel segment belongs;

[0089] The abnormal driving energy consumption identification module 33 is used to determine the target energy consumption baseline value corresponding to the target cluster, as well as the energy consumption value to be detected generated by the vehicle to be detected in the travel segment to be detected, and determine whether the vehicle to be detected has abnormal driving energy consumption in the travel segment to be detected based on the target energy consumption baseline value and the energy consumption value to be detected.

[0090] Optionally, the clustering module 32 is specifically configured to:

[0091] Extracting features based on the target driving condition data to determine a driving condition feature vector, and extracting features based on the target driving road condition data to determine a driving road condition feature vector;

[0092] Feature integration is performed according to the driving condition feature vector and the driving road condition feature vector to generate an integrated feature vector, and the to-be-detected travel segments are clustered according to the integrated feature vector.

[0093] Optionally, the abnormal driving energy consumption identification module 33 is specifically configured to:

[0094] Obtain at least one candidate travel segment generated by the vehicle to be detected included in the target cluster at a historical moment, and determine a candidate energy consumption value generated by the vehicle to be detected in each of the candidate travel segments;

[0095] A target energy consumption baseline value corresponding to the target cluster is determined according to each of the candidate energy consumption values.

[0096] Optionally, the abnormal driving energy consumption identification module 33 is further configured to:

[0097] Obtain at least one candidate travel segment generated by the vehicle to be detected included in the target cluster at a historical moment, and determine a candidate energy consumption value generated by the vehicle to be detected in each of the candidate travel segments;

[0098] A target energy consumption baseline value corresponding to the target cluster is determined according to each of the candidate energy consumption values.

[0099] Optionally, the abnormal driving energy consumption identification module 33 is further configured to:

[0100] Determining a candidate generation time corresponding to each candidate trip segment, and determining a timeliness weight corresponding to each candidate trip segment based on each candidate generation time;

[0101] The respective timeliness weights are used to perform weighted calculation on the respectively associated candidate energy consumption values ​​to generate optimized energy consumption values ​​respectively, and the target energy consumption baseline value corresponding to the target cluster is determined according to the optimized energy consumption values.

[0102] Optionally, the abnormal driving energy consumption identification module 33 is further configured to:

[0103] Determine the candidate time difference between each candidate generation time and the current time, and determine the timeliness weight corresponding to each candidate trip segment based on the candidate time difference and the mapping relationship between the time difference and the candidate timeliness weight.

[0104] Optionally, the abnormal driving energy consumption identification module 33 is further configured to:

[0105] Calculate the target quantile value according to the optimized energy consumption value and the target quantile value, and determine the target quantile value corresponding to the target quantile value;

[0106] A target energy consumption baseline value corresponding to the target cluster is determined according to the target percentile value.

[0107] Optionally, the abnormal driving energy consumption identification module 33 is further configured to:

[0108] Performing a numerical comparison between the target energy consumption baseline value and the energy consumption value to be detected;

[0109] When the energy consumption value to be detected is greater than the target energy consumption baseline value, it is determined that the vehicle to be detected has abnormal driving energy consumption in the travel section to be detected.

[0110] The device for identifying abnormal vehicle driving energy consumption provided by an embodiment of the present invention can execute the method for identifying abnormal vehicle driving energy consumption provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the execution method.

[0111] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0112] Example 4

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 identifying abnormal vehicle driving energy consumption.

[0117] In some embodiments, the method for identifying abnormal vehicle driving energy consumption can 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 can 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 identifying abnormal vehicle driving energy consumption described above can be performed. Alternatively, in other embodiments, the processor 41 can be configured to execute the method for identifying abnormal vehicle driving energy consumption in any other appropriate manner (for example, by means of firmware).

[0118] Various embodiments of the systems and techniques described above 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), complex 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 identifying abnormal vehicle energy consumption, characterized in that: The method comprises: Obtaining target driving condition data corresponding to the vehicle to be tested in the travel section to be tested, as well as target driving road condition data; Clustering the to-be-detected travel segment according to the target driving condition data and the target driving road condition data to determine a target cluster to which the to-be-detected travel segment belongs; Determine the target energy consumption baseline value corresponding to the target cluster, and determine the energy consumption value to be detected generated by the vehicle to be detected in the travel segment to be detected, and determine whether the vehicle to be detected has abnormal driving energy consumption in the travel segment to be detected based on the target energy consumption baseline value and the energy consumption value to be detected.

2. The method according to claim 1, characterized in that Clustering the to-be-detected travel segments according to the target driving condition data and the target driving road condition data includes: Extracting features based on the target driving condition data to determine a driving condition feature vector, and extracting features based on the target driving road condition data to determine a driving road condition feature vector; Feature integration is performed according to the driving condition feature vector and the driving road condition feature vector to generate an integrated feature vector, and the to-be-detected travel segments are clustered according to the integrated feature vector.

3. The method according to claim 1, characterized in that Determining the target energy consumption baseline value corresponding to the target cluster includes: Obtain at least one candidate travel segment generated by the vehicle to be detected included in the target cluster at a historical moment, and determine a candidate energy consumption value generated by the vehicle to be detected in each of the candidate travel segments; A target energy consumption baseline value corresponding to the target cluster is determined according to each of the candidate energy consumption values.

4. The method according to claim 3, characterized in that The determining the target energy consumption baseline value corresponding to the target cluster according to each of the candidate energy consumption values ​​includes: Determining a candidate generation time corresponding to each candidate trip segment, and determining a timeliness weight corresponding to each candidate trip segment based on each candidate generation time; The respective timeliness weights are used to perform weighted calculation on the respectively associated candidate energy consumption values ​​to generate optimized energy consumption values ​​respectively, and the target energy consumption baseline value corresponding to the target cluster is determined according to the optimized energy consumption values.

5. The method according to claim 4, characterized in that Determining the timeliness weight corresponding to each candidate trip segment according to the generation time of each candidate segment includes: Determine the candidate time difference between each candidate generation time and the current time, and determine the timeliness weight corresponding to each candidate trip segment based on the candidate time difference and the mapping relationship between the time difference and the candidate timeliness weight.

6. The method according to claim 4, characterized in that Determining the target energy consumption baseline value corresponding to the target cluster according to each of the optimized energy consumption values ​​includes: Calculate the target quantile value according to the optimized energy consumption value and the target quantile value, and determine the target quantile value corresponding to the target quantile value; A target energy consumption baseline value corresponding to the target cluster is determined according to the target percentile value.

7. The method according to claim 1, characterized in that The determining, based on the target energy consumption baseline value and the energy consumption value to be detected, whether the vehicle to be detected has abnormal driving energy consumption in the travel section to be detected includes: Performing a numerical comparison between the target energy consumption baseline value and the energy consumption value to be detected; When the energy consumption value to be detected is greater than the target energy consumption baseline value, it is determined that the vehicle to be detected has abnormal driving energy consumption in the travel section to be detected.

8. A device for identifying abnormal vehicle energy consumption, characterized in that: The device comprises: A data acquisition module is used to obtain target driving condition data corresponding to the vehicle to be tested in the travel section to be tested, as well as target driving road condition data; a clustering module, configured to cluster the to-be-detected travel segment according to the target driving condition data and the target driving road condition data, and determine a target cluster to which the to-be-detected travel segment belongs; The abnormal driving energy consumption identification module is used to determine the target energy consumption baseline value corresponding to the target cluster, and to determine the energy consumption value to be detected generated by the vehicle to be detected in the travel segment to be detected, and to determine whether the vehicle to be detected has abnormal driving energy consumption in the travel segment to be detected based on the target energy consumption baseline value and the energy consumption value to be detected.

9. The device according to claim 8, characterized in that The clustering module is specifically used to: Extracting features based on the target driving condition data to determine a driving condition feature vector, and extracting features based on the target driving road condition data to determine a driving road condition feature vector; Feature integration is performed according to the driving condition feature vector and the driving road condition feature vector to generate an integrated feature vector, and the to-be-detected travel segments are clustered according to the integrated feature vector.

10. The device according to claim 8, characterized in that The abnormal driving energy consumption identification module is specifically used to: Obtain at least one candidate travel segment generated by the vehicle to be detected included in the target cluster at a historical moment, and determine a candidate energy consumption value generated by the vehicle to be detected in each of the candidate travel segments; A target energy consumption baseline value corresponding to the target cluster is determined according to each of the candidate energy consumption values.

11. 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 that can be executed 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 identifying abnormal vehicle driving energy consumption according to any one of claims 1 to 7.

12. 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 execute the method for identifying abnormal vehicle driving energy consumption according to any one of claims 1 to 7.

13. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method for identifying abnormal vehicle driving energy consumption according to any one of claims 1 to 7.

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