Vehicle illegal driving detection method, device and equipment and storage medium

Abstract

The invention discloses a vehicle illegal driving detection method, device and equipment and a storage medium. The method comprises the following steps: acquiring real-time operation data of a to-be-detected vehicle and real-time positioning data of the to-be-detected vehicle; performing data preprocessing on the obtained real-time operation data and real-time positioning data of the to-be-detected vehicle to obtain standardized time sequence data of the to-be-detected vehicle; analyzing the standardized time sequence data of the to-be-detected vehicle according to a preset idle speed judgment rule and a preset oil pumping and leakage judgment rule, and judging whether the to-be-detected vehicle has an idle speed behavior or an oil pumping and leakage behavior or not; and carrying out violation confirmation on the to-be-detected vehicle with the idling behavior or the oil pumping and leakage behavior, and generating a vehicle violation brief report for the to-be-detected vehicle with the violation confirmation. According to the method, the standardized time sequence data are analyzed through the preset idle speed judgment rule and the preset oil pumping and leakage judgment rule, and the accuracy of illegal driving detection of the vehicle is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle driving technology, and in particular to a vehicle illegal driving detection method, device, equipment and storage medium. Background Art

[0002] With the rapid development of Internet of Vehicles technology, real-time monitoring of vehicle operation status has become an important requirement for traffic management, environmental supervision and transportation enterprise operations. In particular, real-time monitoring of vehicle violations not only plays an important role in preventing road traffic accidents, but also is an important basis for intelligent transportation systems to achieve vehicle-road collaboration and refined management. Traditional real-time monitoring methods for vehicle violations mainly rely on global positioning system positioning and basic sensor data to obtain basic information such as vehicle location and speed. However, it is difficult to accurately capture complex vehicle behaviors by relying solely on global positioning system positioning and basic sensor data, especially in complex urban road conditions, which can easily lead to false alarms or missed alarms.

[0003] In view of this, how to improve the accuracy of vehicle violation detection is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present invention provides a vehicle illegal driving detection method, device, equipment and storage medium, which are used to solve the technical problem of low accuracy of existing vehicle illegal driving detection.

[0005] In order to solve the above technical problems, in a first aspect, the present invention provides a vehicle illegal driving detection method, the method comprising: Obtaining real-time operation data of the vehicle to be detected and real-time positioning data of the vehicle to be detected; Performing data preprocessing on the acquired real-time operation data and real-time positioning data of the vehicle to be detected to obtain standardized time series data of the vehicle to be detected; According to the preset idle speed judgment rules and oil leakage judgment rules, the standardized time series data of the vehicle to be tested is analyzed to determine whether the vehicle to be tested has idle speed behavior or oil leakage behavior; Violations are confirmed for vehicles to be inspected that are detected to be idling or leaking oil, and a vehicle violation briefing is generated for the vehicles to be inspected that are confirmed to have violated the regulations.

[0006] Optionally, the data preprocessing of the acquired real-time operation data and real-time positioning data of the vehicle to be detected to obtain standardized time series data of the vehicle to be detected includes: The real-time operation data and real-time positioning data of the vehicle to be detected are subjected to noise reduction processing by using a mean filtering algorithm to obtain the real-time operation data and real-time positioning data of the vehicle to be detected after noise reduction; Perform multimodal fusion processing on the real-time operation data and real-time positioning data of the vehicle to be detected after noise reduction to obtain multimodal fusion data of the vehicle to be detected; The multimodal fusion data of the vehicle to be detected is normalized to obtain the standardized time series data of the vehicle to be detected.

[0007] Optionally, the multimodal fusion processing is performed on the real-time operation data and real-time positioning data of the vehicle to be detected after noise reduction to obtain the multimodal fusion data of the vehicle to be detected, including: Performing time stamp alignment processing on the real-time operation data and real-time positioning data of the vehicle to be detected, so as to obtain the real-time operation data and real-time positioning data of the vehicle to be detected with consistent time reference; Resampling the real-time operation data and real-time positioning data of the vehicle to be detected with the same time reference to obtain the real-time operation data and real-time positioning data of the vehicle to be detected with the same frequency; Extract key features of the real-time operation data and real-time positioning data of the vehicle to be detected with the same frequency to obtain key feature vectors of the real-time operation data and real-time positioning data of the vehicle to be detected; The key feature vectors of the real-time operation data and real-time positioning data of the vehicle to be detected are spliced ​​to obtain multimodal fusion data of the vehicle to be detected.

[0008] Optionally, the step of analyzing the standardized time series data of the vehicle to be detected according to a preset idle determination rule to determine whether the vehicle to be detected has idle behavior includes: When it is detected that the engine speed of the vehicle to be detected is not zero and the vehicle speed is zero, determining whether the duration of the engine speed of the vehicle to be detected being not zero and the vehicle speed being zero exceeds a first preset time threshold; If the engine speed of the vehicle to be detected is not zero and the duration of the zero speed exceeds a first preset time threshold, analyzing the positioning coordinates and the engine continuous working time in the standardized time series data of the vehicle to be detected; If it is detected that the positioning coordinates of the vehicle to be detected do not change and the engine continues to work for a period of time exceeding a second preset time threshold, it is determined that the vehicle to be detected is idling.

[0009] Optionally, the step of analyzing the standardized time series data of the vehicle to be detected according to a preset oil leakage extraction judgment rule to determine whether the vehicle to be detected has oil leakage extraction behavior includes: When it is detected that the parking time of the vehicle to be detected is greater than or equal to the third preset time threshold and the duration after starting is greater than or equal to the fourth preset time threshold, the fuel tank liquid level data within the fifth preset time threshold before the parking of the vehicle to be detected and the fuel tank liquid level data within the sixth preset time threshold after starting are sampled; Based on the sampled fuel tank level data within the fifth preset time threshold before the vehicle to be tested stops and the fuel tank level data within the sixth preset time threshold after starting, the configured vehicle model and fuel tank level data in the standardized time series data of the vehicle to be tested are analyzed to determine whether the vehicle to be tested has oil leakage.

[0010] Optionally, the configuration vehicle model and the fuel tank liquid level data in the standardized time series data of the vehicle to be detected are analyzed according to the sampled fuel tank liquid level data within the fifth preset time threshold before the vehicle stops and the fuel tank liquid level data within the sixth preset time threshold after the vehicle starts, to determine whether the vehicle to be detected has oil pumping leakage behavior, including: Preset a first fuel tank liquid level drop rate corresponding to the configuration model of the vehicle to be detected, and calculate a second fuel tank liquid level drop rate between the fuel tank liquid level data within a fifth preset time threshold before the vehicle to be detected stops and the fuel tank liquid level data within a sixth preset time threshold after the vehicle to be detected starts; The drop rate difference between the second fuel tank liquid level drop rate and the first fuel tank liquid level drop rate is calculated. If the calculated drop rate difference exceeds the preset drop rate difference, it is determined that the vehicle to be detected has oil leakage behavior.

[0011] Optionally, the step of confirming the violation of the vehicle to be detected in which idling behavior or oil leakage behavior is detected, and generating a vehicle violation briefing for the vehicle to be detected that is confirmed to have violated the regulations, includes: Obtain historical operating data of the vehicle to be tested during the violation time period, and confirm the violation of the vehicle to be tested that is detected to have idling behavior or oil leakage behavior based on the acquired historical operating data, to obtain the vehicle to be tested that is confirmed to have violated the law; Configure the splicing rules of the vehicle violation briefing, and generate a vehicle violation briefing for the confirmed violating vehicles to be detected according to the configured splicing rules of the vehicle violation briefing.

[0012] In a second aspect, the present invention provides a vehicle violation driving detection device, including an acquisition module, a processing module, an analysis module and a confirmation module: The acquisition module is used to acquire real-time operation data of the vehicle to be detected and real-time positioning data of the vehicle to be detected; The processing module is used to perform data preprocessing on the acquired real-time operation data and real-time positioning data of the vehicle to be detected, so as to obtain standardized time series data of the vehicle to be detected; The analysis module is used to analyze the standardized time series data of the vehicle to be detected according to the preset idle speed determination rules and oil leakage determination rules, and determine whether the vehicle to be detected has idle speed behavior or oil leakage behavior; The confirmation module is used to confirm the violation of the vehicle to be detected in which idling behavior or oil leakage behavior is detected, and generate a vehicle violation briefing for the vehicle to be detected that is confirmed to have violated the law.

[0013] In a third aspect, the present invention provides a vehicle violation driving detection device, including a memory and a processor, wherein: The memory is used to store computer programs; The processor is used to read the program in the memory and execute the steps of a vehicle illegal driving detection method provided in the first aspect above.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium having a readable computer program stored thereon, which, when executed by a processor, implements the steps of a vehicle violation driving detection method provided in the first aspect above.

[0015] Compared with the prior art, the vehicle violation detection method, device, equipment and storage medium provided by the present invention have the following beneficial effects: The embodiment of the present invention processes the real-time operation data and real-time positioning data of the vehicle to be detected into standardized time series data, thereby ensuring the temporal consistency of different types of data and eliminating errors caused by inconsistent time references of different data sources. At the same time, according to preset idle judgment rules and oil leakage judgment rules, the standardized time series data of the vehicle to be detected is analyzed to determine whether the vehicle to be detected has idle behavior or oil leakage behavior. By automatically analyzing the standardized time series data, manual intervention is reduced, and the accuracy of vehicle violation detection is effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only part of the embodiments of the present invention, rather than all of the embodiments. For ordinary technicians in this field, without paying creative work, other drawings obtained based on these drawings all fall within the scope of protection of this application.

[0017] Figure 1 It is a flow chart of a vehicle illegal driving detection method provided by an embodiment of the present invention.

[0018] Figure 2 The present invention provides a flow chart of a method for preprocessing data of a vehicle to be detected.

[0019] Figure 3 The present invention provides a flowchart of an idle speed determination method for a vehicle to be detected.

[0020] Figure 4 The present invention is a flowchart of a method for determining oil leakage of a vehicle to be detected provided by an embodiment of the present invention.

[0021] Figure 5 A vehicle illegal driving detection device is provided in an embodiment of the present invention.

[0022] Figure 6 It is a structural schematic diagram of a vehicle violation driving detection device provided by an embodiment of the present invention.

[0023] Figure 7 It is a structural schematic diagram of a computer-readable storage medium provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0025] In order to make the description of the disclosed content more detailed and complete, the following is an illustrative description of the implementation mode and specific examples of the present invention; however, this is not the only form of implementing or applying the specific embodiments of the present invention. The implementation mode covers the features of multiple specific embodiments and the method steps and their sequence for constructing and operating these specific embodiments. However, other specific embodiments can also be used to achieve the same or equal functions and step sequences. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein.

[0027] In the description of the embodiments of the present invention, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two, and other quantifiers are similar. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention, and the embodiments of the present application and the features in the embodiments may be combined with each other without conflict.

[0028] Example 1 like Figure 1 The flowchart of a vehicle violation driving detection method provided in an embodiment of the present invention includes the following steps.

[0029] Step S101, obtaining real-time operation data of the vehicle to be detected and real-time positioning data of the vehicle to be detected.

[0030] The embodiment of the present invention can obtain the real-time operation data of the vehicle to be detected through the OBD acquisition interface, and obtain the real-time positioning data of the vehicle to be detected through the global positioning system, wherein the real-time operation data of the vehicle to be detected includes information such as engine speed, vehicle speed, fuel tank level, vehicle status signal, etc. By analyzing these real-time operation data, the performance and operation status of the vehicle can be understood in real time, and potential problems can be discovered in time. The real-time positioning data of the vehicle to be detected includes vehicle positioning coordinate data. By analyzing the vehicle positioning coordinate data, the driving trajectory of the vehicle can be monitored, whether the vehicle is active in a specific area, and whether the driving route of the vehicle complies with regulations can be analyzed.

[0031] Step S102, preprocessing the acquired real-time operation data and real-time positioning data of the vehicle to be detected to obtain standardized time series data of the vehicle to be detected.

[0032] In step S101, after obtaining the real-time operation data of the vehicle to be detected and the real-time positioning data of the vehicle to be detected, the embodiment of the present invention can pre-process the real-time operation data and real-time positioning data of the vehicle to be detected into standardized time series data, and standardize data of different types and ranges to make them have the same dimension and value range, so as to facilitate the subsequent analysis of the real-time operation data and real-time positioning data of the vehicle to be detected, and avoid analysis deviations caused by different data scales. In addition, arranging the data in chronological order to form time series data can better reflect the changing trend of the vehicle status, and help to discover the regularity and abnormal patterns in the real-time data of the vehicle to be detected.

[0033] Step S103, analyzing the standardized time series data of the vehicle to be detected according to the preset idle speed determination rule and the oil leakage extraction determination rule, and determining whether the vehicle to be detected has idle speed behavior or oil leakage extraction behavior.

[0034] In step S102, the embodiment of the present invention performs data preprocessing on the real-time operation data and real-time positioning data of the vehicle to be detected. After obtaining the standardized time series data of the vehicle to be detected, the standardized time series data of the vehicle to be detected can be analyzed according to the preset idle speed judgment rule and oil leakage judgment rule to quickly identify potential violations. Step S103 of the embodiment of the present invention specifically includes: S1031. According to the preset idle speed determination rule, the standardized time series data of the vehicle to be detected is analyzed to determine whether the vehicle to be detected has idle speed behavior. The embodiment of the present invention can determine whether the vehicle to be detected is in an idle state based on data such as engine speed and vehicle speed. Different types of vehicles may have different idle speed determination thresholds, which need to be set according to actual conditions.

[0035] S1032, according to the preset oil leakage extraction judgment rule, the standardized time series data of the vehicle to be detected is analyzed to determine whether the vehicle to be detected has oil leakage extraction behavior. The present invention can determine whether the vehicle to be detected has oil leakage extraction behavior based on parameters such as the vehicle's oil pressure and oil temperature.

[0036] Step S104, confirming the violation of the vehicle to be detected that is detected to have idling behavior or oil leakage behavior, and generating a vehicle violation briefing for the vehicle to be detected that is confirmed to have violated the law.

[0037] In step S103, after the embodiment of the present invention detects that the vehicle to be detected has idling behavior or oil leakage behavior, it is necessary to further confirm whether it is a violation, which can be combined with specific regulations and standards, for example, it can be combined with the historical operation monitoring information of the vehicle to be detected to further confirm whether the vehicle to be detected is in violation. After confirming that the vehicle to be detected is in violation, it is also necessary to generate a vehicle violation briefing for the confirmed vehicle to be detected.

[0038] As an optional implementation, Figure 2 The flowchart of a data preprocessing method for a vehicle to be detected provided by an embodiment of the present invention, in step S102, the data preprocessing is performed on the acquired real-time operation data and real-time positioning data of the vehicle to be detected to obtain standardized time series data of the vehicle to be detected, including: S1021, performing noise reduction processing on the acquired real-time operation data and real-time positioning data of the vehicle to be detected by using a mean filtering algorithm to obtain the real-time operation data and real-time positioning data of the vehicle to be detected after noise reduction. The embodiment of the present invention can effectively remove random noise in the real-time operation data and positioning data of the vehicle to be detected by using a mean filtering algorithm, thereby improving the accuracy of the data.

[0039] S1022, multimodal fusion processing is performed on the real-time operation data and real-time positioning data of the vehicle to be detected after noise reduction to obtain multimodal fusion data of the vehicle to be detected. The embodiment of the present invention can obtain richer context information by fusing data from different sources. This multidimensional data perspective enables a more comprehensive understanding of the vehicle status and helps to better evaluate the performance and behavior of the vehicle.

[0040] S1023, normalizing the multimodal fusion data of the vehicle to be detected to obtain standardized time series data of the vehicle to be detected. By normalizing the multimodal fusion data of the vehicle to be detected, the embodiment of the present invention can ensure that all the data of the vehicle to be detected are at the same level, and avoid a certain feature dominating the analysis result due to a large value.

[0041] As an optional implementation, in step S1022, multimodal fusion processing is performed on the real-time operation data and real-time positioning data of the vehicle to be detected after noise reduction to obtain multimodal fusion data of the vehicle to be detected, including: The real-time operation data and real-time positioning data of the vehicle to be detected are timestamped and aligned to obtain the real-time operation data and real-time positioning data of the vehicle to be detected with consistent time references. The embodiment of the present invention ensures that different types of data are consistent in time by timestamping the real-time operation data and real-time positioning data of the vehicle to be detected, thereby eliminating errors caused by inconsistent time references of different data sources.

[0042] The real-time operation data and real-time positioning data of the vehicle to be detected with the same time reference are resampled to obtain the real-time operation data and real-time positioning data of the vehicle to be detected with the same frequency. The embodiment of the present invention resamples the data with the same time reference so that the real-time operation data and real-time positioning data of the vehicle to be detected have the same sampling frequency, making subsequent data processing and analysis easier and avoiding analysis errors caused by data frequency mismatch.

[0043] The key feature extraction is performed on the real-time operation data and real-time positioning data of the vehicle to be detected with the same frequency to obtain the key feature vector of the real-time operation data and real-time positioning data of the vehicle to be detected. The embodiment of the present invention can extract key feature information from a large amount of raw data by performing key feature extraction on the data with the same frequency.

[0044] The key feature vectors of the real-time operation data and the real-time positioning data of the vehicle to be detected are feature spliced ​​to obtain multimodal fusion data of the vehicle to be detected. The embodiment of the present invention forms multimodal fusion data of the vehicle to be detected by feature splicing the key feature vectors of the real-time operation data and the real-time positioning data. This multimodal fusion not only increases the dimension of the data, but also provides a richer information background, which helps to more comprehensively understand the driving status of the vehicle.

[0045] As an optional implementation, Figure 3 The above is a flow chart of an idle speed determination method for a vehicle to be detected provided by an embodiment of the present invention. In step S1031, according to a preset idle speed determination rule, the standardized time series data of the vehicle to be detected is analyzed to determine whether the vehicle to be detected has idle speed behavior, including: S10311. When it is detected that the engine speed of the vehicle to be detected is not zero and the vehicle speed is zero, determine whether the duration of the engine speed of the vehicle to be detected being not zero and the vehicle speed being zero exceeds a first preset time threshold. For example, when it is detected that the engine speed of the vehicle to be detected is 5r / min and the vehicle speed is 0KM / h, determine whether the state lasts for 10 minutes. If it lasts for 10 minutes, idling behavior may occur. The embodiment of the present invention avoids the misjudgment of a short-term transient state as idling behavior by setting a first preset time threshold and a second preset time threshold. Only when a certain duration is reached will it be identified as idling, thereby reducing the false alarm rate of the idle judgment of the vehicle to be detected.

[0046] S10312, if the engine speed of the vehicle to be detected is not zero and the duration of the zero speed exceeds the first preset time threshold, the positioning coordinates and the engine continuous working time in the standardized time series data of the vehicle to be detected are analyzed. For example, when it is detected that the engine speed of the vehicle to be detected is 5r / min and the speed is 0KM / h for 10 minutes, it is necessary to analyze the positioning coordinates and the engine continuous working time in the standardized time series data of the vehicle to be detected, and further confirm whether the vehicle is in an idling state by analyzing the changes in the positioning coordinates.

[0047] S10313. If it is detected that the positioning coordinates of the vehicle to be detected do not change and the engine continues to work for more than a second preset time threshold, it is determined that the vehicle to be detected has an idling behavior. For example, if it is detected that the positioning coordinates of the vehicle to be detected do not change and the engine continues to work for more than 30 minutes, it is determined that the vehicle to be detected has an idling behavior. The embodiment of the present invention further determines whether the vehicle has an idling behavior when the positioning coordinates of the vehicle to be detected do not change and the engine continues to work, thereby effectively improving the accuracy of determining the idling behavior of the vehicle to be detected.

[0048] As an optional implementation, Figure 4 The flowchart of the oil leakage determination method for a vehicle to be detected provided by an embodiment of the present invention, in step S1032, the standardized time series data of the vehicle to be detected is analyzed according to the preset oil leakage determination rule to determine whether the vehicle to be detected has oil leakage, including: S10321. When it is detected that the parking time of the vehicle to be detected is greater than or equal to the third preset time threshold and the duration after starting is greater than or equal to the fourth preset time threshold, the oil tank level data within the fifth preset time threshold before the parking of the vehicle to be detected and the oil tank level data within the sixth preset time threshold after starting are sampled. For example, when it is detected that the parking time of the vehicle to be detected is greater than or equal to 10 minutes and the duration after starting is greater than or equal to 20 minutes, 3 oil tank level data within 1 minute before the parking of the vehicle to be detected are sampled, and 3 oil tank level data within 1 minute after starting are sampled to obtain the oil tank level data before and after the parking of the vehicle to be detected, so as to provide a basis for the subsequent judgment of whether the vehicle to be detected has oil leakage.

[0049] S10322. According to the sampled fuel tank level data within the fifth preset time threshold before the vehicle to be detected stops and the sampled fuel tank level data within the sixth preset time threshold after the vehicle to be detected starts, the configuration vehicle model and fuel tank level data in the standardized time series data of the vehicle to be detected are analyzed to determine whether the vehicle to be detected has oil leakage. For example, according to the sampled fuel tank level data within 1 minute before the vehicle to be detected stops and the sampled fuel tank level data within 1 minute after the vehicle to be detected starts, the configuration vehicle model and fuel tank level data in the standardized time series data of the vehicle to be detected are analyzed to determine whether the vehicle to be detected has oil leakage. The embodiment of the present invention can accurately reflect the actual state of the fuel tank by analyzing the fuel tank level data in a specific time period before and after parking.

[0050] As an optional implementation, the configuration model and tank level data in the standardized time series data of the vehicle to be detected are analyzed based on the sampled tank level data within the fifth preset time threshold before the vehicle stops and the tank level data within the sixth preset time threshold after the vehicle starts, to determine whether the vehicle to be detected has oil leakage behavior, including: presetting a first tank level drop rate corresponding to the configuration model of the vehicle to be detected, and calculating a second tank level drop rate between the tank level data within the fifth preset time threshold before the vehicle to be detected stops and the tank level data within the sixth preset time threshold after the vehicle to be detected starts; calculating the descent rate difference between the second tank level drop rate and the first tank level drop rate, and if the calculated descent rate difference exceeds the preset descent rate difference, it is determined that the vehicle to be detected has oil leakage behavior.

[0051] For example, the fuel tank level drop rate of the corresponding configuration model of the vehicle to be tested is preset to be 10% within 30 minutes. When it is detected that the parking time of the vehicle to be tested is greater than or equal to 10 minutes and the duration after starting is greater than or equal to 30 minutes, the three fuel tank level data within 1 minute before the vehicle to be tested stops are sampled. If the sampled fuel tank level data within 1 minute before the vehicle to be tested stops is 75%, and the fuel tank level data within 1 minute after the vehicle to be tested starts is 50%, then the second fuel tank level drop rate between the fuel tank level data before the vehicle to be tested stops and after the vehicle to be tested starts is 25%, and the drop rate difference between the second fuel tank level drop rate and the first fuel tank level drop rate is 15%. If the preset drop rate difference is 5%, and the calculated drop rate difference of 15% exceeds the preset drop rate difference of 5%, it is determined that the vehicle to be tested has oil leakage.

[0052] As an optional implementation, the method of confirming the violation of the vehicle to be detected in which idling behavior or oil leakage behavior is detected, and generating a vehicle violation briefing for the confirmed violation of the vehicle to be detected includes: obtaining the historical operation data of the vehicle to be detected in the violation time period, and confirming the violation of the vehicle to be detected in which idling behavior or oil leakage behavior is detected according to the obtained historical operation data, so as to obtain the confirmed violation of the vehicle to be detected; configuring the splicing rules of the vehicle violation briefing, and generating the vehicle violation briefing for the confirmed violation of the vehicle to be detected according to the configured splicing rules of the vehicle violation briefing. For example, the pictures taken at regular intervals during the violation time period and the historical videos of the vehicle monitoring equipment are obtained to further verify whether the vehicle to be detected is an idling behavior or an oil leakage behavior. If it is verified that the vehicle to be detected has an idling behavior or an oil leakage behavior, it is necessary to configure the splicing rules of the vehicle violation briefing. For example, the splicing rules can be "responsible person + driver's name + license plate number + vehicle model + artificial idling start time + to + end time determined as artificial idling, + illegal idling + cumulative illegal idling time + briefing after verification". The embodiment of the present invention can more comprehensively understand the actual operation status of the vehicle by analyzing the historical operation data of the vehicle to be detected during the violation time period. The consistency and professionalism of the generated report are ensured by setting the splicing rules of the vehicle violation briefing.

[0053] The embodiment of the present invention processes the real-time operation data and real-time positioning data of the vehicle to be detected into standardized time series data, thereby ensuring the temporal consistency of different types of data and eliminating errors caused by inconsistent time references of different data sources. At the same time, according to preset idle judgment rules and oil leakage judgment rules, the standardized time series data of the vehicle to be detected is analyzed to determine whether the vehicle to be detected has idle behavior or oil leakage behavior. By automatically analyzing the standardized time series data, manual intervention is reduced, and the accuracy of vehicle violation detection is effectively improved.

[0054] Example 2 Based on the above-mentioned vehicle illegal driving detection method, an embodiment of the present invention provides a vehicle illegal driving detection device, such as Figure 5 As shown, its structural schematic diagram is shown in Figure 5. The vehicle violation detection device includes an acquisition module 51, a processing module 52, an analysis module 53 and a confirmation module 54: The acquisition module 51 is used to acquire the real-time operation data of the vehicle to be detected and the real-time positioning data of the vehicle to be detected; The processing module 52 is used to perform data preprocessing on the acquired real-time operation data and real-time positioning data of the vehicle to be detected to obtain standardized time series data of the vehicle to be detected; The analysis module 53 is used to analyze the standardized time series data of the vehicle to be detected according to the preset idle speed determination rules and oil leakage determination rules to determine whether the vehicle to be detected has idle speed behavior or oil leakage behavior; The confirmation module 54 is used to confirm the violation of the vehicle to be detected in which idling behavior or oil leakage behavior is detected, and generate a vehicle violation briefing for the vehicle to be detected that is confirmed to have violated the law.

[0055] For other details about how the modules in the above-mentioned vehicle violation driving detection device implement the above-mentioned technical solution, please refer to the description of the vehicle violation driving detection method provided in the above-mentioned invention embodiment, which will not be repeated here.

[0056] Example 3 Based on the above vehicle violation detection method, Figure 6 As shown, a schematic diagram of the structure of a vehicle violation driving detection device provided by an embodiment of the present invention, the identification device includes a processor 61 and a memory 62 coupled to the processor 61. The memory 62 stores a computer program, and when the computer program is executed by the processor 61, the processor 61 executes the steps of the vehicle violation driving detection method in the above embodiment.

[0057] For other details about how the processor 61 in the above-mentioned vehicle violation driving detection device implements the above-mentioned technical solution, please refer to the description of the vehicle violation driving detection method provided in the above-mentioned invention embodiment, which will not be repeated here.

[0058] Among them, the processor 61 can also be called a CPU (Central Processing Unit), and the processor 61 may be an integrated circuit chip with signal processing capabilities; the processor 61 can also be a general-purpose processor, DSP (Digital Signal Process), ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, among which the general-purpose processor can be a microprocessor or the processor 61 can also be any conventional processor, etc.

[0059] Example 4 like Figure 7 As shown, a schematic diagram of the structure of a computer-readable storage medium provided by an embodiment of the present invention, on which a readable computer program 71 is stored; wherein the computer program 71 can be stored in the above storage medium in the form of a software product, including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk, a ROM (Read-Only Memory), a RAM (Random Access Memory), or a terminal device such as a computer, a server, a mobile phone, or a tablet.

[0060] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0061] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0062] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0063] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0064] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server, or a data center to another website site, a computer, a server, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0065] The technical solution provided by the present application is introduced in detail above. The principles and implementation methods of the present application are explained by using specific examples in the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0066] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0067] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0068] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0069] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0070] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A vehicle illegal driving detection method, characterized in that: The following steps are involved: Obtaining real-time operation data of the vehicle to be detected and real-time positioning data of the vehicle to be detected; Performing data preprocessing on the acquired real-time operation data and real-time positioning data of the vehicle to be detected to obtain standardized time series data of the vehicle to be detected; According to the preset idle speed judgment rules and oil leakage judgment rules, the standardized time series data of the vehicle to be tested is analyzed to determine whether the vehicle to be tested has idle speed behavior or oil leakage behavior; Violations are confirmed for vehicles to be inspected that are detected to be idling or leaking oil, and a vehicle violation briefing is generated for the vehicles to be inspected that are confirmed to have violated the regulations.

2. The vehicle illegal driving detection method according to claim 1, characterized in that: The method of preprocessing the acquired real-time operation data and real-time positioning data of the vehicle to be detected to obtain standardized time series data of the vehicle to be detected includes: The real-time operation data and real-time positioning data of the vehicle to be detected are subjected to noise reduction processing by using a mean filtering algorithm to obtain the real-time operation data and real-time positioning data of the vehicle to be detected after noise reduction; Perform multimodal fusion processing on the real-time operation data and real-time positioning data of the vehicle to be detected after noise reduction to obtain multimodal fusion data of the vehicle to be detected; The multimodal fusion data of the vehicle to be detected is normalized to obtain the standardized time series data of the vehicle to be detected.

3. The vehicle violation driving detection method according to claim 2, characterized in that: The multimodal fusion processing is performed on the real-time operation data and real-time positioning data of the vehicle to be detected after noise reduction to obtain the multimodal fusion data of the vehicle to be detected, including: Performing time stamp alignment processing on the real-time operation data and real-time positioning data of the vehicle to be detected, so as to obtain the real-time operation data and real-time positioning data of the vehicle to be detected with consistent time reference; Resampling the real-time operation data and real-time positioning data of the vehicle to be detected with the same time reference to obtain the real-time operation data and real-time positioning data of the vehicle to be detected with the same frequency; Extract key features of the real-time operation data and real-time positioning data of the vehicle to be detected with the same frequency to obtain key feature vectors of the real-time operation data and real-time positioning data of the vehicle to be detected; The key feature vectors of the real-time operation data and real-time positioning data of the vehicle to be detected are spliced ​​to obtain multimodal fusion data of the vehicle to be detected.

4. The vehicle illegal driving detection method according to claim 1, characterized in that: The method of analyzing the standardized time series data of the vehicle to be detected according to the preset idle speed determination rule to determine whether the vehicle to be detected has idle speed behavior includes: When it is detected that the engine speed of the vehicle to be detected is not zero and the vehicle speed is zero, determining whether the duration of the engine speed of the vehicle to be detected being not zero and the vehicle speed being zero exceeds a first preset time threshold; If the engine speed of the vehicle to be detected is not zero and the duration of the zero speed exceeds a first preset time threshold, analyzing the positioning coordinates and the engine continuous working time in the standardized time series data of the vehicle to be detected; If it is detected that the positioning coordinates of the vehicle to be detected do not change and the engine continues to work for a period of time exceeding a second preset time threshold, it is determined that the vehicle to be detected is idling.

5. The vehicle illegal driving detection method according to claim 1, characterized in that: The method of analyzing the standardized time series data of the vehicle to be detected according to the preset oil leakage extraction judgment rule to determine whether the vehicle to be detected has oil leakage extraction behavior includes: When it is detected that the parking time of the vehicle to be detected is greater than or equal to the third preset time threshold and the duration after starting is greater than or equal to the fourth preset time threshold, the fuel tank liquid level data within the fifth preset time threshold before the parking of the vehicle to be detected and the fuel tank liquid level data within the sixth preset time threshold after starting are sampled; Based on the sampled fuel tank level data within the fifth preset time threshold before the vehicle to be tested stops and the fuel tank level data within the sixth preset time threshold after starting, the configured vehicle model and fuel tank level data in the standardized time series data of the vehicle to be tested are analyzed to determine whether the vehicle to be tested has oil leakage.

6. The vehicle illegal driving detection method according to claim 5, characterized in that: The method of analyzing the configuration vehicle model and the fuel tank liquid level data in the standardized time series data of the vehicle to be detected based on the sampled fuel tank liquid level data within the fifth preset time threshold before the vehicle stops and the fuel tank liquid level data within the sixth preset time threshold after the vehicle starts, and determining whether the vehicle to be detected has oil leakage behavior, includes: Preset a first fuel tank liquid level drop rate corresponding to the configuration model of the vehicle to be detected, and calculate a second fuel tank liquid level drop rate between the fuel tank liquid level data within a fifth preset time threshold before the vehicle to be detected stops and the fuel tank liquid level data within a sixth preset time threshold after the vehicle to be detected starts; The drop rate difference between the second fuel tank liquid level drop rate and the first fuel tank liquid level drop rate is calculated. If the calculated drop rate difference exceeds the preset drop rate difference, it is determined that the vehicle to be detected has oil leakage behavior.

7. The vehicle illegal driving detection method according to claim 1, characterized in that: The method of confirming the violation of the vehicle to be detected that has been detected to have idling behavior or oil leakage behavior, and generating a vehicle violation briefing for the vehicle to be detected that has been confirmed to have violated the law, includes: Obtain historical operating data of the vehicle to be tested during the violation time period, and confirm the violation of the vehicle to be tested that is detected to have idling behavior or oil leakage behavior based on the acquired historical operating data, to obtain the vehicle to be tested that is confirmed to have violated the law; Configure the splicing rules of the vehicle violation briefing, and generate a vehicle violation briefing for the confirmed violating vehicles to be detected according to the configured splicing rules of the vehicle violation briefing.

8. A vehicle violation detection device, characterized in that: Including acquisition module, processing module, analysis module and confirmation module: The acquisition module is used to acquire real-time operation data of the vehicle to be detected and real-time positioning data of the vehicle to be detected; The processing module is used to perform data preprocessing on the acquired real-time operation data and real-time positioning data of the vehicle to be detected, so as to obtain standardized time series data of the vehicle to be detected; The analysis module is used to analyze the standardized time series data of the vehicle to be detected according to the preset idle speed determination rules and oil leakage determination rules, and determine whether the vehicle to be detected has idle speed behavior or oil leakage behavior; The confirmation module is used to confirm the violation of the vehicle to be detected in which idling behavior or oil leakage behavior is detected, and generate a vehicle violation briefing for the vehicle to be detected that is confirmed to have violated the law.

9. A vehicle violation detection device, characterized in that: comprising a memory and a processor, wherein: The memory is used to store computer programs; The processor is used to read the computer program in the memory and execute the steps of a vehicle illegal driving detection method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A readable computer program is stored thereon, and when the program is executed by a processor, the steps of a vehicle illegal driving detection method as described in any one of claims 1 to 7 are implemented.

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