Dangerous goods transportation vehicle state monitoring management method and system

Abstract

The embodiment of the invention provides a dangerous goods transportation vehicle state monitoring management method and system. The method comprises the steps that vehicle state information of a dangerous goods transportation vehicle is acquired; performing data preprocessing on the vehicle state information to obtain first data; performing anomaly detection on the first data, and determining a vehicle state grade according to an anomaly detection result; and under the condition that the vehicle state grade reaches a preset grade, carrying out risk alarm. According to the invention, the problem that the monitoring precision of the dangerous goods transportation vehicle is low under the condition of a single sensor is solved, and the effect of improving the monitoring precision of the dangerous goods transportation vehicle is achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to a field, and in particular, to a method and system for monitoring and managing the status of vehicles transporting dangerous goods. Background Art

[0002] With the rapid development of industrial production, the demand for transporting hazardous materials continues to increase. The transportation of hazardous materials involves a variety of complex factors, including the vehicle's driving state, the physical parameters within the tank, and driver behavior. These factors are directly related to the safety of the transportation process. Especially for certain hazardous materials, such as flammable, explosive, toxic, or radioactive substances, the safety requirements during transportation are even more stringent. For example, vehicles transporting explosives on highways must not exceed a speed of 80 kilometers per hour, and parameters such as the tank's vibration amplitude and turning angle must not exceed certain thresholds. Otherwise, energy accumulation (such as static electricity and the conversion of kinetic energy of vibration) can easily occur, leading to danger.

[0003] Currently, existing hazardous materials transport vehicle status monitoring systems primarily rely on data from single sensors, such as speed or pressure sensors, to monitor the vehicle's operating status. While these systems can provide a certain level of vehicle status information, they overlook the interactions between other relevant parameters, such as vibration and temperature. This single-parameter monitoring approach fails to fully reflect the vehicle's actual operating status and can easily lead to misjudgments or missed detections. Summary of the Invention

[0004] The embodiments of the present invention provide a method and system for monitoring and managing the status of a dangerous goods transport vehicle, so as to at least solve the problem of low monitoring accuracy of dangerous goods transport vehicles in the related art.

[0005] According to one embodiment of the present invention, a method for monitoring and managing the status of a dangerous goods transport vehicle is provided, comprising: Obtaining vehicle status information of a dangerous goods transport vehicle, wherein the vehicle status information includes vehicle position information, vehicle motion information, tank parameter information, and driving behavior information, wherein the tank parameter information includes at least tank pressure information and / or tank vibration information; Performing data preprocessing on the vehicle status information to obtain first data; performing anomaly detection on the first data, and determining a vehicle status level according to the anomaly detection result; When the vehicle status level reaches a preset level, a risk alarm is issued.

[0006] In an exemplary embodiment, performing anomaly detection on the first data includes: Calculating a driving behavior score based on the number of sudden accelerations and sudden brakings within a preset time period included in the driving behavior information; When the driving behavior score is greater than the first threshold, it is determined that the driving behavior is abnormal.

[0007] In an exemplary embodiment, performing anomaly detection on the first data includes: Calculating a driving behavior score based on the number of sudden accelerations and sudden brakings within a preset time period included in the driving behavior information; When the driving behavior score is greater than the first threshold, it is determined that the driving behavior is abnormal.

[0008] In an exemplary embodiment, performing anomaly detection on the first data includes: determining the vibration energy density according to the vibration frequency contained in the tank vibration information; determining the dynamic pressure change information of the tank according to the tank pressure information; determining a tank risk value based on the vibration energy density and the tank dynamic pressure change information; When the tank risk value is greater than the second threshold, it is determined that the tank is abnormal.

[0009] In an exemplary embodiment, after performing data preprocessing on the vehicle status information to obtain first data, the method further includes: Constructing the first data into a first matrix, and obtaining a matrix feature of the first matrix; Determining a combined eigenvalue based on the matrix characteristics and a preset characteristic relationship; constructing a combined characteristic matrix based on the combined eigenvalues; Calculate the correlation value of the combined feature matrix, and determine that the first data is abnormal if the correlation value does not meet the range condition.

[0010] In an exemplary embodiment, the performing data preprocessing on the vehicle status information to obtain the first data includes: filtering the vehicle status information; Supplement missing values ​​in the filtering results; The missing value supplementation processing result is standardized to obtain the first data.

[0011] According to another embodiment of the present invention, a system for monitoring and managing the status of dangerous goods transport vehicles is provided, comprising: An information collection module is used to obtain vehicle status information of a dangerous goods transport vehicle, wherein the vehicle status information includes vehicle position information, vehicle motion information, tank parameter information, and driving behavior information, wherein the tank parameter information includes at least tank pressure information and / or tank vibration information; a preprocessing module, configured to perform data preprocessing on the vehicle status information to obtain first data; an anomaly detection module, configured to perform anomaly detection on the first data and determine a vehicle status level based on the anomaly detection result; The alarm module is used to issue a risk alarm when the vehicle status level reaches a preset level.

[0012] In an exemplary embodiment, performing anomaly detection on the first data includes: Calculating a driving behavior score based on the number of sudden accelerations and sudden brakings within a preset time period included in the driving behavior information; When the driving behavior score is greater than the first threshold, it is determined that the driving behavior is abnormal.

[0013] In an exemplary embodiment, performing anomaly detection on the first data includes: determining the vibration energy density according to the vibration frequency contained in the tank vibration information; determining the dynamic pressure change information of the tank according to the tank pressure information; determining a tank risk value based on the vibration energy density and the tank dynamic pressure change information; When the tank risk value is greater than the second threshold, it is determined that the tank is abnormal.

[0014] According to yet another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when run.

[0015] According to another embodiment of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any one of the above method embodiments.

[0016] Through the present invention, since multiple dimensions of dangerous goods transport vehicles are comprehensively monitored and the transport status of the vehicles is judged from multiple dimensions, the problem of low monitoring accuracy of dangerous goods transport vehicles under a single sensor can be solved, thereby achieving the effect of improving the monitoring accuracy of dangerous goods transport vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of a method according to an embodiment of the present invention; Figure 2 is a structural block diagram of a device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0019] Hereinafter, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified with "first," "second," etc., may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0020] In addition, in this application, directional terms such as "up", "down", "left", and "right" may be defined including but not limited to the orientation relative to the schematic placement of the components in the drawings. It should be understood that these directional terms may be relative concepts. They are used for relative descriptions and clarifications, and they may change accordingly according to changes in the orientation of the components in the drawings.

[0021] In this application, unless otherwise specified or limited, the term "connection" should be understood broadly. For example, "connection" can mean fixed connection, detachable connection, or integration; it can mean direct connection or indirect connection through an intermediate medium. In addition, the term "coupling" can refer to the manner in which electrical connection is achieved for signal transmission.

[0022] As used herein, "about," "substantially," or "approximately" includes the stated value and an average value that is within an acceptable range of deviation from the particular value as determined by one of ordinary skill in the art taking into account the measurements in question and the errors associated with the measurement of the particular quantity (i.e., the limitations of the measurement system).

[0023] Example 1 In this embodiment, a method for monitoring and managing the status of a dangerous goods transport vehicle is provided. Figure 1 FIG. 1 is a flow chart of a method for monitoring and managing the status of a dangerous goods transport vehicle according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps: Step S11, obtaining vehicle status information of a dangerous goods transport vehicle, wherein the vehicle status information includes vehicle position information, vehicle motion information, tank parameter information, and driving behavior information, wherein the tank parameter information includes at least tank pressure information and / or tank vibration information; In this embodiment, for the transportation of dangerous goods, the tank parameters and driving behavior during the transportation process will directly determine the safety of vehicle transportation. In particular, for some special dangerous goods, the vehicle speed must not exceed a certain speed (for example, the speed of vehicles transporting explosives on highways must not exceed 80 kilometers per hour), and the tank vibration amplitude and turning angle (for example, vehicles transporting explosives must ensure smooth driving and avoid sharp turns, that is, the turning angle must not be less than 15°) must not exceed a certain threshold. Otherwise, energy (for example, static electricity) is easily accumulated, leading to danger. Therefore, how to monitor the vehicle's driving behavior and tank parameters is an important guarantee for ensuring the safe transportation of such products.

[0024] Among them, vehicle position information and vehicle motion information can be obtained and collected through speed sensors or GPS modules / Beidou modules. In particular, for acceleration and other data, an inertial measurement unit (IMU) can be additionally equipped for auxiliary detection; the vibration amplitude of the tank can be collected through a three-axis vibration sensor (±5g range) or an ICP-type acceleration sensor (frequency response range 0.5~2000Hz); the pressure inside the tank can be monitored in real time through a pressure sensor (0-10MPa range); the temperature can be monitored through a temperature sensor; the liquid level can be monitored through a liquid level sensor (or an ultrasonic / radar level gauge); the turning angle can be monitored through a gyroscope or steering sensor; driving behavior information includes continuous driving time, number of sharp turns / sudden brakes, fatigue driving, etc., and driving behavior information can be monitored through the driver monitoring system (DMS).

[0025] It should be noted that the aforementioned vehicle-mounted module needs to comply with the JT / T 794-2019 standard. The communication protocol for data transmission can be uploaded to the cloud platform through the MQTT protocol, with a transmission frequency ≥ 1 time / second (key parameters such as pressure require a higher frequency). At the same time, a local SD card is used for offline caching to prevent signal loss, support breakpoint resumption, and sensor data needs to be encrypted (such as AES-256) and support blockchain evidence storage (such as Hyperledger Fabric) to avoid data tampering.

[0026] Step S12, performing data preprocessing on the vehicle status information to obtain first data; In this embodiment, since the formats and sources of the data are different, the data need to be pre-processed to facilitate subsequent analysis.

[0027] The performing data preprocessing on the vehicle status information to obtain the first data includes: Step S121, filtering the vehicle status information; Step S122, performing missing value supplementation processing on the filtering result; Step S123: standardize the missing value supplementation processing result to obtain the first data.

[0028] In this embodiment, the purpose of filtering is to remove noise and outliers in the data to ensure the quality and reliability of the data. Specifically, it includes checking whether there are duplicate records in the data to remove duplicate data (specifically including rule base filtering, such as tank pressure > design pressure upper limit (such as LNG tanker design pressure 1.6MPa, threshold set to 1.5MPa), lateral acceleration > 0.5g (sharp turn physical limit) and other data for elimination), identifying and correcting erroneous data according to business logic and data range (for example, if the reading of a sensor is obviously beyond the reasonable range, it can be marked as an error and corrected), using filtering technology (such as low-pass filter, specifically Butterworth filter (cut-off frequency 10Hz, order = 4)) to remove high-frequency noise in the data, etc.; the purpose of missing value filling processing is to fill the missing values ​​in the data to ensure the integrity of the data, specifically including using interpolation methods (such as linear interpolation) or statistical methods (such as mean, median) to fill Missing values ​​are detected and then anomaly detection is used to determine whether the relevant data is correct. Specifically, in the case of a brief interruption of the GPS signal, random data loss (<5%) will occur. In this case, linear interpolation can be used to fill in the missing values ​​based on the time series. For equipment failures such as vibration sensors, continuous data will appear. In this case, the mean value of similar vehicles will be used to fill in the missing values ​​based on the data clustering results. For situations such as power failure of the tank pressure sensor, non-random data will appear. In this case, such data will be directly marked as a special value (such as -999) and equipment maintenance will be triggered, and no data filling will be performed. The purpose of standardization is to convert the data into a unified format, eliminate the dimensional differences between different features, and improve the stability and accuracy of the model. Specifically, Z-Score standardization or Min-Max standardization can be used for standardization, which is not limited here.

[0029] The method of performing abnormality detection on the preprocessing result may be to perform the following steps after performing data preprocessing on the vehicle status information to obtain the first data: Step S124: constructing the first data into a first matrix, and obtaining matrix characteristics of the first matrix; Step S125, determining a combined eigenvalue based on the matrix characteristics and a preset characteristic relationship; Step S126, constructing a combined characteristic matrix based on the combined eigenvalues; Step S127 , calculating the correlation value of the combined feature matrix, and determining that the first data is abnormal if the correlation value does not meet a range condition.

[0030] In this embodiment, the first data is subjected to abnormality detection in a matrix manner, which can effectively determine the relationship between matrix elements and accurately determine whether the first data is normal based on the data relationship.

[0031] The pre-processed vehicle status information (first data) is constructed into a matrix format, where each row represents a time point or sample, and each column represents a feature (such as vehicle speed, pressure, vibration, temperature, etc.).

[0032] Assume we have the following preprocessed data: The first matrix X constructed is: Then, based on the matrix X, the statistics (such as mean, standard deviation, maximum value, etc.) and mathematical properties (such as determinant, eigenvalue, etc.) of the matrix are calculated. Then, the time domain characteristics are calculated by calculating the mean, variance, peak value, etc. to determine the pressure stability; the frequency domain characteristics are calculated by assigning the FFT main frequency component (0-50Hz) to determine the abnormal vibration mode; the eigenvalues ​​of the covariance matrix are calculated by calculating the spectral radius of the covariance matrix between each sensor to determine the characteristics of multi-sensor linkage. The aforementioned matrix characteristics include statistics, mathematical properties, pressure stability, etc.; then, according to business needs and domain knowledge, a combined feature relationship is constructed, such as the product of velocity v and pressure P in the tank. , the ratio of vibration lateral acceleration vi to temperature T , pressure variance in the tank The difference from the temperature T , dynamic pressure Coupling coefficient with the vibration main frequency amplitude A The corresponding combined eigenvalues ​​are then calculated. SP represents the dynamic energy transfer of the fluid within the tank, reflecting the coupled effect of vehicle motion on the tank's internal pressure. This is because at high speeds, liquid sloshing or gas compression can cause pressure fluctuations, and the product SP can amplify these dynamic effects. Under normal circumstances, SP is <50 (m / s⋅kPa). If SP > 80, there is a risk, and the vehicle speed should be reduced. Similarly, since lower temperatures reduce the yield strength of the material, vibration is more likely to induce microcracks. Therefore, VT can quantify the risk of "vibration-temperature" synergistic damage, that is, the risk of brittleness of metal tanks in high vibration and low temperature environments. Generally, when VT > 0.1 (g / °C), the probability of tank weld rupture increases threefold. When PT is negative, it means that the pressure is dropping faster than the temperature, while conversely, it indicates that an exothermic reaction may have occurred. This can be used to determine whether a leak or abnormal reaction has occurred. For example, for compressed hydrogen, the normal VT range is -20 to 20 kPa / °C. If VT < −50, a leak may be present. Used to indicate the risk of fluid sloshing and structural vibration resonance, for example, when When it is >0.3, the probability of cracks in the stress concentration area of ​​the tank increases, and so on; then the above combined features are used to construct the corresponding combined feature matrix Y according to the time series, and the Pearson correlation coefficient is used to measure the linear correlation between the features. If the absolute value of the correlation value exceeds a certain threshold (such as 0.9), the data is considered to be abnormal.

[0033] Step S13, performing anomaly detection on the first data, and determining a vehicle status level according to the anomaly detection result; Step S14: When the vehicle status level reaches a preset level, a risk alarm is issued.

[0034] In this embodiment, when all data are normal, the final risk level is determined based on the data value and the corresponding weighted value. Specifically: (Formula 1) Where S is the risk level, Sp is the speed risk value (0 if the speed is within the normal range, otherwise 1). is the amplitude risk value, is the turning angle risk value, T is the temperature risk value, and w is the weight corresponding to the first data. Generally, S<1, at this time, there is no abnormal trigger, and the combined feature correlation value is within the preset range, and the risk level is determined to be normal. When a single feature is abnormal (such as a temporary temperature exceedance) or a composite feature is slightly abnormal, at this time 1≤S<2, it is necessary to pay attention and prompt the driver through the instrument panel; when two or more basic features are abnormal, or one key composite feature is abnormal (such as α>0.3), at this time 2≤S<3, it is necessary to limit the speed to 60km / h and report to the background dispatch center for maintenance in a specific area. When multiple systems are coordinated abnormal (such as pressure, vibration, and temperature are abnormal at the same time), S≥3, the vehicle is prohibited from driving and emergency leakage disposal is initiated, and so on.

[0035] Through the above steps, by comprehensively monitoring multiple dimensions of dangerous goods transport vehicles and judging the transport status of the vehicles from multiple dimensions, the problem of low monitoring accuracy of dangerous goods transport vehicles under a single sensor is solved, and the monitoring accuracy of dangerous goods transport vehicles is improved.

[0036] Example 2 The difference from Example 1 is that we notice that the probability of abnormal situations is mainly related to sudden acceleration and braking of the vehicle. Therefore, to simplify the abnormality detection calculation, the first data can be subjected to abnormality detection in the following manner: Step S131, calculating a driving behavior score based on the number of sudden accelerations and sudden brakings within a preset time period included in the driving behavior information; Step S132: When the driving behavior score is greater than the first threshold, it is determined that the driving behavior is abnormal.

[0037] In this embodiment, referring to the SAE J2944 standard and ECE R13 braking regulations, we set acceleration > 0.3g (approximately 2.94 m / s²) and duration ≥ 1 second as the sudden acceleration determination condition, and deceleration < -0.4g (approximately -3.92 m / s²) as the sudden braking determination condition. The preset time period is a specific time window (for example, detection is performed every 10 minutes to align with the driver's fatigue cycle). The driving behavior score Q is then calculated according to the following formula: (Formula 2) Where N1 is the number of sudden accelerations, N2 is the number of sudden brakes, N3 is the number of consecutive sudden accelerations / brakes, and p is the corresponding weight. Generally, p1 is 0.2, p2 is 0.5, and p3 is 0.3. The sudden pressure rise caused by sudden braking is more dangerous, so its weight is higher than other parameters.

[0038] Example 3 The difference from Examples 1 and 2 is that we noticed that regardless of sudden braking or acceleration, the ultimate cause of the risk change is the change in energy in the tank (such as the conversion of static electricity and oscillation kinetic energy). Therefore, the risk situation can be judged from the perspective of energy density change. Specifically, the following method can be used to judge: Step S133, determining the vibration energy density according to the vibration frequency included in the tank vibration information; Step S134, determining the dynamic pressure change information of the tank according to the tank pressure information; Step S135, determining a tank risk value based on the vibration energy density and the tank dynamic pressure change information; Step S136: When the tank risk value is greater than a second threshold, determine that the tank is abnormal.

[0039] In this embodiment, the vibration frequency is obtained from the tank vibration sensor. (unit: Hz), then based on the vibration frequency Determine the angular frequency ( ), and then the vibration energy density E is calculated by the following formula: (Formula 3) At the same time, the dynamic pressure changes can be determined by the aforementioned sensors : (Formula 4) Where, is the pressure value at time point i, is the average pressure, and N is the number of data points.

[0040] Then calculate the risk value according to the corresponding weight q: ,When the risk value is greater than a certain threshold, it is determined to be abnormal.

[0041] Through the description of the above embodiments, those skilled in the art will clearly understand that the methods according to the above embodiments can be implemented using software plus the necessary general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, or optical disk) and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0042] This embodiment also provides a dangerous goods transport vehicle status monitoring and management system, which is used to implement the above-mentioned embodiments and preferred embodiments. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0043] Figure 2 FIG. 1 is a structural block diagram of a state monitoring and management system for dangerous goods transport vehicles according to an embodiment of the present invention. Figure 2 As shown, the system includes: An information collection module 21 is configured to obtain vehicle status information of a dangerous goods transport vehicle, wherein the vehicle status information includes vehicle position information, vehicle motion information, tank parameter information, and driving behavior information, wherein the tank parameter information includes at least tank pressure information and / or tank vibration information; A preprocessing module 22 is used to perform data preprocessing on the vehicle status information to obtain first data; an abnormality detection module 23, configured to perform abnormality detection on the first data and determine a vehicle status level according to the abnormality detection result; The alarm module 24 is used to issue a risk alarm when the vehicle status level reaches a preset level.

[0044] In an optional embodiment, performing anomaly detection on the first data includes: Calculating a driving behavior score based on the number of sudden accelerations and sudden brakings within a preset time period included in the driving behavior information; When the driving behavior score is greater than the first threshold, it is determined that the driving behavior is abnormal.

[0045] In an optional embodiment, performing anomaly detection on the first data includes: determining the vibration energy density according to the vibration frequency contained in the tank vibration information; determining the dynamic pressure change information of the tank according to the tank pressure information; determining a tank risk value based on the vibration energy density and the tank dynamic pressure change information; When the tank risk value is greater than the second threshold, it is determined that the tank is abnormal.

[0046] It should be noted that the above modules can be implemented through software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0047] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when running.

[0048] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0049] An embodiment of the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0050] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0051] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0052] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0053] Units described as separate components may or may not be physically separate, and components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0054] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0055] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions for enabling a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0056] The above content is only a specific embodiment of this application, but the scope of protection of this application is not limited to this. Any changes or replacements within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for monitoring and managing the status of a dangerous goods transport vehicle, characterized in that: include: Obtaining vehicle status information of a dangerous goods transport vehicle, wherein the vehicle status information includes vehicle position information, vehicle motion information, tank parameter information, and driving behavior information, wherein the tank parameter information includes at least tank pressure information and / or tank vibration information; Performing data preprocessing on the vehicle status information to obtain first data; performing anomaly detection on the first data, and determining a vehicle status level according to the anomaly detection result; When the vehicle status level reaches a preset level, a risk alarm is issued.

2. The method according to claim 1, characterized in that The performing anomaly detection on the first data includes: Calculating a driving behavior score based on the number of sudden accelerations and sudden brakings within a preset time period included in the driving behavior information; When the driving behavior score is greater than the first threshold, it is determined that the driving behavior is abnormal.

3. The method according to claim 1, characterized in that The performing anomaly detection on the first data includes: determining the vibration energy density according to the vibration frequency contained in the tank vibration information; determining the dynamic pressure change information of the tank according to the tank pressure information; determining a tank risk value based on the vibration energy density and the tank dynamic pressure change information; When the tank risk value is greater than the second threshold, it is determined that the tank is abnormal.

4. The method according to claim 1, wherein After pre-processing the vehicle status information to obtain first data, the method further includes: Constructing the first data into a first matrix, and obtaining a matrix feature of the first matrix; Determining a combined eigenvalue based on the matrix characteristics and a preset characteristic relationship; constructing a combined characteristic matrix based on the combined eigenvalues; Calculate the correlation value of the combined feature matrix, and determine that the first data is abnormal if the correlation value does not meet the range condition.

5. The method according to claim 1, wherein The performing data preprocessing on the vehicle status information to obtain first data includes: filtering the vehicle status information; Supplement missing values ​​in the filtering results; The missing value supplementation processing result is standardized to obtain the first data.

6. A dangerous goods transport vehicle status monitoring and management system, characterized in that: include: An information collection module is used to obtain vehicle status information of a dangerous goods transport vehicle, wherein the vehicle status information includes vehicle position information, vehicle motion information, tank parameter information, and driving behavior information, wherein the tank parameter information includes at least tank pressure information and / or tank vibration information; a preprocessing module, configured to perform data preprocessing on the vehicle status information to obtain first data; an anomaly detection module, configured to perform anomaly detection on the first data and determine a vehicle status level based on the anomaly detection result; The alarm module is used to issue a risk alarm when the vehicle status level reaches a preset level.

7. The system according to claim 6, characterized in that The performing anomaly detection on the first data includes: Calculating a driving behavior score based on the number of sudden accelerations and sudden brakings within a preset time period included in the driving behavior information; When the driving behavior score is greater than the first threshold, it is determined that the driving behavior is abnormal.

8. The system according to claim 6, wherein: The performing anomaly detection on the first data includes: determining the vibration energy density according to the vibration frequency contained in the tank vibration information; determining the dynamic pressure change information of the tank according to the tank pressure information; determining a tank risk value based on the vibration energy density and the tank dynamic pressure change information; When the tank risk value is greater than the second threshold, it is determined that the tank is abnormal.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 5 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 5.

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