Transportation risk event detection method and device, computer equipment, storage medium and program product

Abstract

The invention relates to a transportation risk event detection method and device, computer equipment, a storage medium and a program product. The method comprises the steps of decrypting an encrypted data packet sent by a communication unit to obtain decrypted data, performing feature extraction on the decrypted data to obtain key features, obtaining a risk event according to the key features and a decision tree model, and sending alarm information to the communication unit according to the risk event. By adopting the method, the risk event in the transportation process can be accurately detected.

Description

Technical Field

[0001] The present application relates to the field of emergency transportation technology, and in particular to a transportation risk event detection method, device, computer equipment, storage medium and program product. Background Art

[0002] In the current emergency transportation field, traditional risk monitoring methods mainly rely on manual reporting by drivers. Although this method can achieve timely discovery and reporting of risks to a certain extent, its efficiency and accuracy are restricted by many factors. Specifically, when a vehicle breaks down, encounters a traffic jam, or has an accident, the driver needs to report to the dispatch center by phone or other means of communication.

[0003] However, this reporting method has obvious lags and uncertainties. In an emergency, reporting may not be possible immediately, or information transmission may be delayed due to limited communication conditions, which in turn affects the accuracy and completeness of the information and makes it impossible to accurately monitor the current risk situation. Summary of the invention

[0004] Based on this, it is necessary to provide a transportation risk event detection method, device, computer equipment, storage medium and program product that can accurately detect transportation risk events in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for detecting a transportation risk event, comprising:

[0006] Decrypting the encrypted data packet sent by the communication unit to obtain decrypted data; the decrypted data includes sensor data, image data and positioning data;

[0007] Extract features from the decrypted data to obtain key features;

[0008] According to the key features and the decision tree model, risk events are obtained, and according to the risk events, alarm information is sent to the communication unit; the alarm information includes the risk level corresponding to the risk event.

[0009] In one embodiment, the step of obtaining risk events according to key features and a decision tree model includes:

[0010] All key features are weighted averaged and fused to obtain risk features;

[0011] The risk features are normalized and input into the decision tree model to obtain predicted events.

[0012] In one embodiment, the process of establishing a decision tree model includes:

[0013] Decrypting the historical data packets sent by the communication unit to obtain historical data;

[0014] Add data annotations to historical data. Data annotations are used to characterize historical risk events corresponding to historical data.

[0015] Based on historical data and historical risk events, decision rules are obtained, and a decision tree model is established based on the decision rules.

[0016] In one embodiment, the decision rule includes a decision threshold corresponding to a historical risk event; and the step of sending an alarm message to a communication unit according to the risk event includes:

[0017] Determine the risk level corresponding to the risk event based on the decision threshold;

[0018] Based on the risk level, an alert message is sent to the communication unit.

[0019] In one embodiment, the step of sending an alarm message to a communication unit based on the risk level includes:

[0020] When the risk level is the first level, an alarm message and an alarm voice are sent to the communication unit, and emergency information is reported;

[0021] When the risk level is the second level, an alarm message is sent to the communication unit, and the vehicle is identified in the map based on the positioning data; the risk level corresponding to the second level is less than the risk level corresponding to the first level.

[0022] In one embodiment, the method further comprises:

[0023] When the risk level is the third level, a prompt message is sent to the communication unit to remind the driver that there is a potential risk accident; the risk level corresponding to the third level is less than the risk level corresponding to the second level.

[0024] In a second aspect, the present application also provides a transport risk event detection device, comprising:

[0025] A data decryption module is used to decrypt the encrypted data packet sent by the communication unit to obtain decrypted data; the decrypted data includes sensor data, image data and positioning data;

[0026] A feature extraction module is used to extract features from decrypted data to obtain key features;

[0027] The risk detection module is used to obtain risk events based on key features and decision tree models, and send warning information to the communication unit based on the risk events; the warning information includes the risk level corresponding to the risk event.

[0028] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method steps of any one of the first aspects are implemented.

[0029] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements any one of the method steps in the first aspect when the computer program is executed by a processor.

[0030] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which implements any one of the method steps in the first aspect when executed by a processor.

[0031] The above-mentioned transportation risk event detection method, device, computer equipment, storage medium and program product can ensure the comprehensiveness and accuracy of key features by extracting features from the decrypted sensor data, image data and positioning data, and process the key features through a decision tree model to the communication unit, which can ensure the accuracy of risk event detection, thereby sending alarm information to the communication unit in time to ensure the safety of the transportation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 This is an application environment diagram of a transportation risk event detection method in an embodiment;

[0034] Figure 2 A schematic diagram of a flow chart of a method for detecting a transportation risk event in an embodiment;

[0035] Figure 3 A schematic diagram of a flow chart of a method for detecting a transportation risk event in another embodiment;

[0036] Figure 4 is a structural block diagram of a transport risk event detection device in one embodiment;

[0037] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0039] The transportation risk event detection method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. The server 102 communicates with the communication unit 104 through the network. The server 102 is used to decrypt the encrypted data packet sent by the communication unit 104 to obtain decrypted data; the decrypted data includes sensor data, image data and positioning data, and feature extraction is performed on the decrypted data to obtain key features. According to the key features and the decision tree model, risk events are obtained, and according to the risk events, warning information is sent to the communication unit; the warning information includes the risk level corresponding to the risk event. The server 102 can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The communication unit 104 can be, but is not limited to, a vehicle-mounted communication unit.

[0040] In an exemplary embodiment, Figure 2 As shown, a method for detecting transportation risk events is provided. Figure 1 The server 102 in the example is used as an example to illustrate, including the following steps 202 to 206. Among them:

[0041] S202: Decrypt the encrypted data packet sent by the communication unit to obtain decrypted data; the decrypted data includes sensor data, image data and positioning data.

[0042] Optionally, during the transportation process, the communication unit packages the collected data to form an encrypted data packet, wherein the encrypted data packet includes sensor data, image data and positioning data. The encrypted data packet is transmitted to the server in real time through the communication network. After receiving the encrypted data packet, the server decrypts the encrypted data packet and performs data processing to ensure the accuracy and integrity of the decrypted data.

[0043] S204: Extract features from the decrypted data to obtain key features.

[0044] Optionally, by performing feature extraction on the decrypted data, key features in the data are obtained, such as temperature anomalies, speed changes, distance to the vehicle ahead, and road congestion, etc. By extracting multi-dimensional features, key features that can reflect transportation risks are obtained.

[0045] S206: Obtain risk events according to the key features and the decision tree model, and send warning information to the communication unit according to the risk events; the warning information includes the risk level corresponding to the risk event.

[0046] Optionally, the decision tree model is a tool for classification and decision-making based on data features. By learning and analyzing a large number of known risk events and their corresponding data features, a mapping relationship between features and risk events is established. By inputting the extracted key features into the decision tree model, the model can determine whether there is a risk event in the current transportation process and what kind of risk event it is. Then, according to the severity of the risk event, the corresponding risk level is determined and an alarm message is sent to the communication unit.

[0047] In the above-mentioned transportation risk event detection method, by extracting features from the decrypted sensor data, image data and positioning data, the comprehensiveness and accuracy of key features can be ensured. The key features are processed by the decision tree model to the communication unit to ensure the accuracy of risk event detection, thereby sending alarm information to the communication unit in a timely manner to ensure the safety of the transportation process.

[0048] In an exemplary embodiment, the step of obtaining risk events based on key features and a decision tree model includes: performing weighted average fusion processing on all key features to obtain risk features; normalizing the risk features and inputting the normalized risk features into the decision tree model to obtain predicted events.

[0049] Optionally, in order to comprehensively consider different types of key features, a weighted average method is used to assign a weight to each key feature according to its importance, and then a risk feature value that comprehensively reflects the transportation risk is obtained through weighted calculation. After that, the risk features are normalized, the data range is adjusted to between 0 and 1, and outliers are removed to ensure the accuracy of the risk features. After that, the normalized risk features are processed through a decision tree model, and the predicted events are output.

[0050] In this embodiment, through weighted average fusion processing, multiple key features of different types can be integrated into a risk feature value, which comprehensively reflects various risk factors in the transportation process. By processing the risk features through a decision tree model, the accuracy of predicted events can be improved.

[0051] In an exemplary embodiment, the process of establishing a decision tree model includes: decrypting historical data packets sent by a communication unit to obtain historical data; adding data annotations to the historical data, the data annotations are used to characterize historical risk events corresponding to the historical data; obtaining decision rules based on the historical data and historical risk events, and establishing a decision tree model based on the decision rules.

[0052] Optionally, by acquiring the historical data packets of the communication unit, the historical data packets are decrypted, and data tags are added to the decrypted historical data, wherein the data tags are used to characterize the historical risk events corresponding to the historical data, and the decision rules are determined according to the historical risk events to obtain the decision tree model. For example, the historical risk event is vehicle failure-engine temperature is too high, wherein the temperature is divided into three risk levels, namely, slight, moderate, and severe, according to the degree of temperature being too high. The risk event and the risk level of the risk event can be obtained through the decision tree model.

[0053] In this embodiment, by adding data annotations to historical data and establishing a decision tree model based on historical risk events represented by the data annotations, the accuracy of the decision tree model can be improved, thereby accurately predicting risk events in the transportation process.

[0054] In an exemplary embodiment, the decision rule includes a decision threshold corresponding to a historical risk event; the step of sending an alarm message to a communication unit according to the risk event includes: determining the risk level corresponding to the risk event according to the decision threshold; and sending an alarm message to the communication unit based on the risk level.

[0055] Optionally, in the process of establishing a decision tree model, by analyzing the annotated historical data, a corresponding decision threshold is determined for each possible historical risk event. After the existence of a certain risk event is detected through the decision tree model, its risk level is determined according to the decision threshold corresponding to the risk event. Furthermore, after determining the risk level, according to the severity of the risk level, an alarm message is sent to the communication unit to inform the transportation personnel of the risk events currently faced and the corresponding risk level, so that the transportation personnel can promptly understand the risk problems and their severity during the transportation process.

[0056] In this embodiment, the risk level is determined by setting different decision thresholds, and based on the risk level, an alarm message is sent to the communication unit, so that differentiated warnings can be provided for risk events of different severity, thereby improving the accuracy and completeness of risk event detection.

[0057] In an exemplary embodiment, the step of sending an alarm message to a communication unit based on the risk level includes: when the risk level is a first level, sending an alarm message and an alarm voice to the communication unit, and reporting emergency information; when the risk level is a second level, sending an alarm message to the communication unit, and identifying the vehicle in a map based on positioning data; the risk level corresponding to the second level is less than the risk level corresponding to the first level.

[0058] Optionally, in the case of the first level of risk, it means that there is a risk event with a high degree of risk. At this time, an alarm message is sent to the communication unit so that the transportation personnel can view the specific risk content and prompts on the device. At the same time, an alarm voice is sent to attract the attention of the transportation personnel in a more direct and stronger way to ensure that important risk prompts are not missed. In the case of the second level of risk, an alarm message is sent to the communication unit to inform the transportation personnel of the current risk situation. In addition, the location of the vehicle is marked on the map based on the positioning data, so that the transportation personnel and managers can intuitively understand the location of the vehicle and provide more comprehensive information support for subsequent decisions and actions.

[0059] In this embodiment, by taking different warning and handling measures for different risk levels, accurate management of transportation risks can be achieved, thereby effectively ensuring the safety of the transportation process.

[0060] In an exemplary embodiment, the method further includes: when the risk level is the third level, sending a prompt message to the communication unit to prompt the driver that there is a potential risk accident; the risk level corresponding to the third level is less than the risk level corresponding to the second level.

[0061] Optionally, when the risk level is the third level, it indicates that there is a potential risk in the transportation process at this time, but the risk level is relatively low. At this time, a prompt message is sent to the communication unit to inform the transportation personnel that there may be potential risks to avoid the potential risks from further developing into actual risk accidents.

[0062] In this embodiment, by taking different warning and handling measures for different risk levels, accurate management of transportation risks can be achieved, thereby effectively ensuring the safety of the transportation process.

[0063] In an exemplary embodiment, Figure 3 As shown, a method for detecting a transportation risk event is provided, the method comprising the following steps:

[0064] S302: Decrypt the encrypted data packet sent by the communication unit to obtain decrypted data; the decrypted data includes sensor data, image data and positioning data.

[0065] S304: Extract features from the decrypted data to obtain key features.

[0066] S306: Decrypt the historical data packets sent by the communication unit to obtain historical data; add data annotations to the historical data, where the data annotations are used to characterize the historical risk events corresponding to the historical data; obtain decision rules based on the historical data and historical risk events, and establish a decision tree model based on the decision rules.

[0067] S308: Perform weighted average fusion processing on all key features to obtain risk features; normalize the risk features and input the normalized risk features into the decision tree model to obtain predicted events.

[0068] S310: Determine the risk level corresponding to the risk event based on the decision threshold corresponding to the historical risk event.

[0069] S312: When the risk level is the first level, an alarm message and an alarm voice are sent to the communication unit, and emergency information is reported; when the risk level is the second level, an alarm message is sent to the communication unit, and the vehicle is marked on the map based on the positioning data; the risk level corresponding to the second level is less than the risk level corresponding to the first level.

[0070] S314: When the risk level is the third level, a prompt message is sent to the communication unit to prompt the driver that there is a potential risk accident; the risk level corresponding to the third level is lower than the risk level corresponding to the second level.

[0071] In this embodiment, by performing feature extraction on the decrypted sensor data, image data and positioning data, the comprehensiveness and accuracy of key features can be ensured. The key features are processed by a decision tree model to the communication unit to ensure the accuracy of risk event detection, thereby sending alarm information to the communication unit in a timely manner to ensure the safety of the transportation process.

[0072] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0073] Based on the same inventive concept, the embodiment of the present application also provides a transport risk event detection device for implementing the transport risk event detection method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more transport risk event detection device embodiments provided below can refer to the limitations of the transport risk event detection method above, and will not be repeated here.

[0074] In an exemplary embodiment, Figure 4 As shown, a transport risk event detection device is provided, comprising: a data decryption module 10, a feature extraction module 20 and a risk detection module 30, wherein:

[0075] The data decryption module 10 is used to decrypt the encrypted data packet sent by the communication unit to obtain decrypted data; the decrypted data includes sensor data, image data and positioning data.

[0076] The feature extraction module 20 is used to extract features from the decrypted data to obtain key features.

[0077] The risk detection module 30 is used to obtain risk events according to key features and a decision tree model, and send warning information to the communication unit according to the risk events; the warning information includes the risk level corresponding to the risk event.

[0078] In an exemplary embodiment, the risk detection module 30 is also used to perform weighted average fusion processing on all key features to obtain risk features; normalize the risk features, and input the normalized risk features into a decision tree model to obtain predicted events.

[0079] In an exemplary embodiment, the risk detection module 30 is also used to decrypt historical data packets sent by the communication unit to obtain historical data; add data annotations to the historical data, and the data annotations are used to characterize historical risk events corresponding to the historical data; obtain decision rules based on historical data and historical risk events, and establish a decision tree model based on the decision rules.

[0080] In an exemplary embodiment, the decision rule includes a decision threshold corresponding to a historical risk event; the risk detection module 30 is further used to determine the risk level corresponding to the risk event according to the decision threshold; and send an alarm message to the communication unit based on the risk level.

[0081] In an exemplary embodiment, the risk detection module 30 is also used to send an alarm message and an alarm voice to a communication unit and report emergency information when the risk level is the first level; when the risk level is the second level, send an alarm message to the communication unit and identify the vehicle in the map based on the positioning data; the risk level corresponding to the second level is less than the risk level corresponding to the first level.

[0082] In an exemplary embodiment, the risk detection module 30 is also used to send a prompt message to the communication unit to prompt the driver that there is a potential risk accident when the risk level is the third level; the risk level corresponding to the third level is less than the risk level corresponding to the second level.

[0083] Each module in the above-mentioned transport risk event detection device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0084] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store decrypted data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for detecting a transportation risk event is implemented.

[0085] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0086] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: decrypting an encrypted data packet sent by a communication unit to obtain decrypted data; the decrypted data includes sensor data, image data, and positioning data; performing feature extraction on the decrypted data to obtain key features; obtaining risk events based on the key features and a decision tree model, and sending alarm information to the communication unit based on the risk events; the alarm information includes the risk level corresponding to the risk event.

[0087] In one embodiment, the processor executes a computer program that involves obtaining risk events based on key features and a decision tree model, including: performing weighted average fusion processing on all key features to obtain risk features; normalizing the risk features, and inputting the normalized risk features into the decision tree model to obtain predicted events.

[0088] In one embodiment, when the processor executes a computer program, it decrypts historical data packets sent by the communication unit to obtain historical data; adds data annotations to the historical data, and the data annotations are used to characterize historical risk events corresponding to the historical data; obtains decision rules based on the historical data and historical risk events, and establishes a decision tree model based on the decision rules.

[0089] In one embodiment, the decision rules include a decision threshold corresponding to a historical risk event; when the processor executes a computer program, sending an alarm message to a communication unit based on the risk event includes: determining a risk level corresponding to the risk event based on the decision threshold; and sending an alarm message to the communication unit based on the risk level.

[0090] In one embodiment, the processor executes a computer program involving sending an alarm message to a communication unit based on the risk level, including: when the risk level is a first level, sending an alarm message and an alarm voice to the communication unit, and reporting emergency information; when the risk level is a second level, sending an alarm message to the communication unit, and identifying the vehicle in a map based on the positioning data; the risk level corresponding to the second level is less than the risk level corresponding to the first level.

[0091] In one embodiment, when the processor executes the computer program, the following steps are also implemented: when the risk level is the third level, a prompt message is sent to the communication unit to prompt the driver that there is a potential risk accident; the risk level corresponding to the third level is less than the risk level corresponding to the second level.

[0092] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: decrypting an encrypted data packet sent by a communication unit to obtain decrypted data; the decrypted data includes sensor data, image data, and positioning data; performing feature extraction on the decrypted data to obtain key features; obtaining risk events based on the key features and a decision tree model, and sending alarm information to the communication unit based on the risk events; the alarm information includes the risk level corresponding to the risk event.

[0093] In one embodiment, when a computer program is executed by a processor, the computer program involves obtaining risk events based on key features and a decision tree model, including: performing weighted average fusion processing on all key features to obtain risk features; normalizing the risk features, and inputting the normalized risk features into the decision tree model to obtain predicted events.

[0094] In one embodiment, when the computer program is executed by the processor, it involves decrypting the historical data packets sent by the communication unit to obtain historical data; adding data annotations to the historical data, the data annotations are used to characterize the historical risk events corresponding to the historical data; obtaining decision rules based on the historical data and historical risk events, and establishing a decision tree model based on the decision rules.

[0095] In one embodiment, the decision rules include a decision threshold corresponding to a historical risk event; when the computer program is executed by a processor, sending an alarm message to a communication unit based on the risk event includes: determining a risk level corresponding to the risk event based on the decision threshold; and sending an alarm message to the communication unit based on the risk level.

[0096] In one embodiment, when a computer program is executed by a processor, it involves sending an alarm message to a communication unit based on the risk level, including: when the risk level is a first level, sending an alarm message and an alarm voice to the communication unit, and reporting emergency information; when the risk level is a second level, sending an alarm message to the communication unit, and identifying the vehicle in a map based on the positioning data; the risk level corresponding to the second level is less than the risk level corresponding to the first level.

[0097] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the risk level is the third level, a prompt message is sent to the communication unit to prompt the driver that there is a potential risk accident; the risk level corresponding to the third level is less than the risk level corresponding to the second level.

[0098] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the following steps: decrypting an encrypted data packet sent by a communication unit to obtain decrypted data; the decrypted data includes sensor data, image data, and positioning data; performing feature extraction on the decrypted data to obtain key features; obtaining risk events based on the key features and a decision tree model, and sending alarm information to the communication unit based on the risk events; the alarm information includes the risk level corresponding to the risk event.

[0099] In one embodiment, when a computer program is executed by a processor, the computer program involves obtaining risk events based on key features and a decision tree model, including: performing weighted average fusion processing on all key features to obtain risk features; normalizing the risk features, and inputting the normalized risk features into the decision tree model to obtain predicted events.

[0100] In one embodiment, when the computer program is executed by the processor, it involves decrypting the historical data packets sent by the communication unit to obtain historical data; adding data annotations to the historical data, the data annotations are used to characterize the historical risk events corresponding to the historical data; obtaining decision rules based on the historical data and historical risk events, and establishing a decision tree model based on the decision rules.

[0101] In one embodiment, the decision rules include a decision threshold corresponding to a historical risk event; when the computer program is executed by a processor, sending an alarm message to a communication unit based on the risk event includes: determining a risk level corresponding to the risk event based on the decision threshold; and sending an alarm message to the communication unit based on the risk level.

[0102] In one embodiment, when a computer program is executed by a processor, it involves sending an alarm message to a communication unit based on the risk level, including: when the risk level is a first level, sending an alarm message and an alarm voice to the communication unit, and reporting emergency information; when the risk level is a second level, sending an alarm message to the communication unit, and identifying the vehicle in a map based on the positioning data; the risk level corresponding to the second level is less than the risk level corresponding to the first level.

[0103] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the risk level is the third level, a prompt message is sent to the communication unit to prompt the driver that there is a potential risk accident; the risk level corresponding to the third level is less than the risk level corresponding to the second level.

[0104] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0105] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0106] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for detecting transportation risk events, characterized in that: Applied to servers; The server interacts with a communication unit of a vehicle; the method comprising: Decrypting the encrypted data packet sent by the communication unit to obtain decrypted data; the decrypted data includes sensor data, image data and positioning data; Performing feature extraction on the decrypted data to obtain key features; According to the key features and the decision tree model, a risk event is acquired, and according to the risk event, an alarm message is sent to the communication unit; the alarm message includes a risk level corresponding to the risk event.

2. The method according to claim 1, characterized in that The obtaining of risk events according to the key features and the decision tree model includes: All key features are weighted averaged and fused to obtain risk features; The risk features are normalized and input into a decision tree model to obtain predicted events.

3. The method according to claim 1, characterized in that The process of establishing the decision tree model includes: Decrypting the historical data packets sent by the communication unit to obtain historical data; Adding data annotations to the historical data, where the data annotations are used to characterize historical risk events corresponding to the historical data; According to the historical data and the historical risk events, decision rules are obtained, and according to the decision rules, a decision tree model is established.

4. The method according to claim 3, characterized in that: The decision rule includes a decision threshold corresponding to the historical risk event; The sending of warning information to the communication unit according to the risk event includes: Determining the risk level corresponding to the risk event according to the decision threshold; Based on the risk level, an alert message is sent to the communication unit.

5. The method according to claim 4, characterized in that The sending of warning information to the communication unit based on the risk level includes: When the risk level is the first level, sending warning information and warning voice to the communication unit, and reporting emergency information; When the risk level is the second level, an alarm message is sent to the communication unit, and the vehicle is marked on a map based on the positioning data; the risk level corresponding to the second level is less than the risk level corresponding to the first level.

6. The method according to claim 5, characterized in that The method further comprises: When the risk level is the third level, a prompt message is sent to the communication unit to prompt the driver that there is a potential risk accident; the risk level corresponding to the third level is less than the risk level corresponding to the second level.

7. A transportation risk event detection device, characterized in that: The device comprises: A data decryption module, used to decrypt the encrypted data packet sent by the communication unit to obtain decrypted data; the decrypted data includes sensor data, image data and positioning data; A feature extraction module, used to extract features from the decrypted data to obtain key features; The risk detection module is used to obtain risk events according to the key features and the decision tree model, and send warning information to the communication unit according to the risk events; the warning information includes the risk level corresponding to the risk event.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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