Safety air bag generator detection early warning system and automobile

Abstract

The invention relates to a safety air bag generator detecting and early warning system and an automobile, and the safety air bag generator detecting and early warning system comprises an internal detecting module which is used for obtaining working condition characteristic data in a shell of a safety air bag generator; the cloud platform is used for receiving and storing the working condition feature data, and processing the working condition feature data by adopting a prediction model based on the working condition feature data to obtain a predicted failure risk level of the airbag generator; and obtaining early warning information based on the predicted failure risk level of the airbag generator, and sending the early warning information to the terminal. Working condition characteristic data in a shell of the safety airbag generator is obtained through the internal detection module and sent to the cloud platform, the cloud platform judges the failure risk of the safety airbag generator based on a detection result and sends the failure risk to the early warning module for early warning, and a user is reminded to conduct maintenance and troubleshooting in time. Therefore, effective protection is provided for the safety air bag generator when an accident occurs, and the safety of passengers is protected.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of automobiles, in particular to a safety air bag inflator detection and early warning system and an automobile. BACKGROUND

[0002] A traditional safety air bag system (Supplemental Restraint System, SRS) significantly improves the passive safety of a vehicle. The system mainly includes different types of safety air bags such as DAB / PAB / KAB / CAB / SAB, and the core component is a gas inflator (Inflator), which is a component that generates a large amount of gas in an instant through chemical reaction or physical release of gas, and quickly generates gas to fill the air bag in a collision.

[0003] With the increase of the service life, there may be problems such as leakage of the gas generating agent of the safety air bag inflator and loss of activity, which leads to failure of the safety air bag inflator. At present, there is a lack of effective means to judge the health state of the safety air bag inflator, and when the safety air bag inflator fails, the safety air bag inflator cannot be maintained in time, which is not conducive to protecting the safety of passengers in an accident. SUMMARY

[0004] Therefore, it is necessary to provide a safety air bag inflator detection and early warning system and an automobile in view of the problem that there is a lack of effective means to judge the health state of the safety air bag inflator, and when the safety air bag inflator fails, the safety air bag inflator cannot be maintained in time, which is not conducive to protecting the safety of passengers in an accident.

[0005] The application provides a safety air bag inflator detection and early warning system, which comprises an internal detection module, an internal detection module, which is used for acquiring working condition characteristic data inside a shell of a safety air bag inflator; a cloud platform, which is in signal connection with the internal detection module, is used for receiving and storing the working condition characteristic data, processing the working condition characteristic data based on the working condition characteristic data by using a prediction model to obtain a prediction failure risk level of the safety air bag inflator, obtaining early warning information based on the prediction failure risk level of the safety air bag inflator, and sending the early warning information to a terminal.

[0006] According to one embodiment of the application, the system further comprises an edge computing module, which is in communication connection with the internal detection module and the cloud platform respectively, is used for receiving the working condition characteristic data, and performing abnormal analysis on the working condition characteristic data to obtain optimized working condition characteristic data.

[0007] According to one embodiment of the application, the edge computing module is further used for encrypting the working condition characteristic data according to a preset algorithm after the optimized working condition characteristic data is obtained.

[0008] According to one embodiment of the present application, the working condition characteristic data comprises at least one of gas pressure, internal temperature and gas activity in the shell.

[0009] According to one embodiment of the present application, the internal detection module comprises: a pressure sensor for detecting gas pressure in the shell; a gas sensor for detecting gas production drug decomposition product concentration in the shell; and a transmitter electrically connected with the pressure sensor and the gas sensor, the transmitter being used for transmitting detection data of the pressure sensor and the gas sensor; a fixing seat fixedly arranged in the shell, the pressure sensor, the gas sensor and the transmitter being integrally packaged in the fixing seat.

[0010] According to one embodiment of the present application, the internal detection module further comprises a storage battery and a vibration power generation unit, the storage battery and the vibration power generation unit being fixed to the fixing seat, the storage battery being electrically connected with the pressure sensor, the gas sensor, the transmitter and the vibration power generation unit respectively; and / or, the internal detection module being electrically connected with a vehicle clock spring.

[0011] According to one embodiment of the present application, the terminal comprises a vehicle-mounted display module and a user terminal.

[0012] According to one embodiment of the present application, the sending of the early warning information to the terminal comprises: determining that the automobile is in a starting state, and sending early warning information to the vehicle-mounted display module; determining that the automobile is in a non-starting state, and sending early warning information to the user terminal.

[0013] According to one embodiment of the present application, the system further comprises an environment temperature detection module, the environment temperature detection module being signal connected with the edge computing module, and being used for acquiring environment temperature data of an environment where the airbag generator is located, the edge computing module being used for receiving the working condition characteristic data and the environment temperature data, and screening pressure data in the working condition characteristic data based on the environment temperature data.

[0014] The present application further provides an automobile applied to the airbag generator detection and early warning system in the above-mentioned embodiments, the automobile comprising an airbag generator and an internal detection module.

[0015] The airbag inflator detection and early warning system and the automobile have the advantages that the working condition characteristic data inside the airbag inflator shell is acquired through the internal detection module, the working condition characteristic data is sent to the cloud platform, the cloud platform can judge the failure risk of the airbag inflator based on the detection result of the internal detection module, and sends the failure risk to the early warning module for early warning, so that the user is reminded to maintain in time and eliminate the fault, thereby ensuring that the airbag inflator provides effective protection when an accident occurs and protects the safety of the passengers. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 FIG. 1 is a structural schematic diagram of a columnar inflator in an embodiment of the present application.

[0017] Figure 2 FIG. 2 is a structural schematic diagram of a tubular inflator in another embodiment of the present application.

[0018] Figure 3 FIG. 3 is a working process schematic diagram of the airbag inflator detection and early warning system in an embodiment of the present application.

[0019] Figure 4 FIG. 4 is a prediction model training process schematic diagram in an embodiment of the present application.

[0020] Figure 5 FIG. 5 is a working process schematic diagram of the airbag inflator detection and early warning system in another embodiment of the present application.

[0021] REFERENCE SIGNS:

[0022] 1, upper shell; 2, bursting disc; 3, internal detection module; 4, fixing seat; 5, expanding explosive; 6, gas generating explosive; 7, ignition tube; 8, short circuit ring; 9, lower shell; 10, inner shell; 11, shell; 12, diffuser. DETAILED DESCRIPTION

[0023] In order to make the above objectives, characteristics and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application are described in detail below in conjunction with the drawings. In the following description, a large number of specific details are set forth in order to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the spirit of the present application, so the present application is not limited by the specific embodiments disclosed below.

[0024] In the description of the application, it should be understood that, if there are these terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like, these terms indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the purpose of facilitating the description of the application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the application.

[0025] In addition, if there are these terms "first", "second", these terms are only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first", "second" can explicitly or implicitly include at least one of the features. In the description of the application, if the term "multiple" appears, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise explicitly specified and limited.

[0026] In this application, unless otherwise explicitly specified and limited, if there are terms such as "mounting", "connecting", "connecting", "fixing" and the like, these terms should be broadly understood. For example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements, unless otherwise explicitly limited. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0027] In this application, unless otherwise explicitly specified and limited, if there are similar descriptions such as "first feature on or under second feature", the meaning can be that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. Moreover, the first feature "above", "above" and "above" of the second feature can be that the first feature is directly above or obliquely above the second feature, or only indicates that the horizontal height of the first feature is higher than that of the second feature. The first feature "below", "below" and "below" of the second feature can be that the first feature is directly below or obliquely below the second feature, or only indicates that the horizontal height of the first feature is less than that of the second feature.

[0028] It is to be noted that when an element such as a layer, film, region, or substrate is referred to as being "on" another element, it can be directly on the other element or intervening elements can also be present. In addition, it is to be understood that when a layer is referred to as being "connected", "coupled", or "adjacent" to another element, it can be directly connected, coupled, or adjacent to the other element, or intervening elements can also be present. As used herein, the term "vertical", "horizontal", "upper", "lower", "left", "right", and the like are merely used for the purpose of illustration and do not indicate an absolute orientation.

[0029] The airbag inflator detection and early warning system provided by an embodiment of the present application can realize health state detection of the airbag inflator. For ease of understanding, the structure of the airbag inflator is briefly introduced below.

[0030] The airbag inflator is the core power component of the SRS and needs to rapidly produce sufficient gas to fill the airbag through chemical reaction or gas release at the moment of collision. The airbag inflator is mainly divided into two types, i.e., a columnar inflator and a tubular inflator, corresponding to airbags installed at different positions.

[0031] In combination with Figure 1 and Figure 2 , the airbag inflator includes a shell 11, an expanding fuel chamber 5, a rupture disc 2, an ignition tube 7, and a short circuit ring 8.

[0032] As shown in Figure 1 , the shell 11 of the columnar inflator includes an upper shell 1, a lower shell 9, and an inner shell 10. The upper shell 1 is connected to the lower shell 9. The lower shell 9 is provided with an interface at the bottom, which is in communication with the airbag. After gas is generated, the gas can rapidly flow into the airbag through the interface. The inner shell 10 is located between the upper shell 1 and the lower shell 9.

[0033] As shown in Figure 2 , the tubular inflator adopts an integrated shell 11. One end of the shell 11 is provided with a diffuser 12. The diffuser 12 is used to uniformly distribute the gas generated by the gas generating agent 6, so as to avoid excessive local airflow from damaging the airbag.

[0034] The expanding fuel chamber 5 is arranged in the shell 11. The expanding fuel chamber 5 is filled with expanding fuel 5 inside and gas generating agent 6 outside. The main component of the gas generating agent 6 is sodium azide (NaN3) or guanidine nitrate (GuHN), which can generate a large amount of N2 through combustion or decomposition reaction.

[0035] The rupture disc 2 is arranged in the inner shell 10 of the columnar inflator and the expanding fuel chamber 5 of the tubular inflator. The rupture disc 2 is a pressure-triggered sealing component, which is closed in normal state. When the internal pressure reaches a preset threshold, the rupture disc 2 breaks.

[0036] The ignition tube 7 is a collision trigger component. After receiving an electrical signal of a vehicle collision sensor, an internal ignition powder is combusted to generate a high-temperature flame to ignite the expanding powder 5, thereby starting a gas production process. The short-circuit ring 8 is a safety protection component. When the vehicle is not started or collision is not triggered, the short-circuit ring 8 shorts the pin of the ignition tube 7, thereby preventing static electricity or a false trigger electrical signal from causing the ignition tube 7 to start unexpectedly.

[0037] In combination Figure 3 The airbag inflator early warning system of the embodiment includes: an internal detection module 3 configured to acquire working condition characteristic data inside a shell 11 of the airbag inflator; a cloud platform connected to the internal detection module 3 and configured to receive and store the working condition characteristic data, process the working condition characteristic data based on the working condition characteristic data using a prediction model, and obtain a predicted failure risk level of the airbag inflator; obtain early warning information based on the predicted failure risk level of the airbag inflator, and send the early warning information to a terminal.

[0038] The working condition characteristic data can be the pressure, internal temperature, and concentration of decomposition products (e.g., N2) of the gas generating agent 6 inside the shell 11 of the airbag inflator, and can reflect the current state of the airbag inflator. For example, when the gas generating agent 6 decomposes, it will cause the pressure inside the shell 11 of the airbag inflator to change. When the pressure inside the shell 11 of the airbag inflator exceeds a first threshold value, it indicates that the decomposition rate of the gas generating agent 6 is high, and the airbag inflator cannot meet the protection requirements in emergency situations. Alternatively, when the pressure inside the shell 11 of the airbag inflator decreases at a rate exceeding a second threshold value, it indicates that the airbag inflator has a leak, and the airbag inflator also cannot meet the protection requirements in emergency situations. When the working condition characteristic data is the concentration of the decomposition products of the gas generating agent 6, it indicates that the decomposition rate of the gas generating agent 6 is high, and the airbag inflator cannot meet the protection requirements in emergency situations.

[0039] For example, the input data of the prediction model is the working condition characteristic data such as the pressure, internal temperature, and concentration of the decomposition products of the gas generating agent 6, and the output data is the early warning information. After the prediction model receives the working condition characteristic data through the input layer, it processes the working condition characteristic data through the processing layer to obtain the activity degree of the gas generating agent 6. Based on the activity degree of the gas generating agent 6 and the risk level threshold value, the prediction model determines the predicted failure risk level of the airbag inflator and outputs the predicted failure risk level through the output layer. The prediction model can be an LSTM prediction model.

[0040] The cloud platform determines the early warning information corresponding to the predicted failure risk level according to the mapping relationship between the determined failure risk level and the early warning information. Exemplarily, when the failure risk level is level one, the early warning information is a red early warning identifier, prompting that the airbag inflator is invalid and needs to be replaced; when the failure risk level is level two, the early warning information is a yellow early warning identifier, prompting that the airbag inflator has a failure risk; and when the failure risk level is level three, the early warning information is a green early warning identifier, prompting that the airbag inflator is valid. Through the explicit mapping rule of the risk level and the early warning information, the cloud platform can quickly and accurately convert the abstract failure risk level into an intuitive visual early warning identifier, which not only facilitates subsequent accurate push of alarm information, but also enables users to quickly understand the health status of the airbag inflator without professional knowledge to determine whether to take measures such as replacement and repair; at the same time, the standardized early warning mapping relationship avoids early warning confusion caused by inconsistent judgment standards, ensures consistency of early warning information output by different terminals, improves the reliability and user trust of the early warning system, and provides clear guidance for airbag maintenance and safety protection.

[0041] Optionally, the early warning information further includes a real-time pressure curve and a real-time internal temperature curve, wherein the cloud platform can generate the real-time pressure curve based on the pressure data inside the airbag inflator shell 11 and the collection time in the working condition characteristic data, and generate the real-time internal temperature curve based on the internal temperature data inside the airbag inflator shell 11 and the collection time in the working condition characteristic data. The real-time pressure curve and the real-time internal temperature curve can intuitively show the change rule of the pressure and temperature inside the airbag inflator shell with time, and the user can trace the fluctuation process of the pressure and temperature through the curve to more accurately judge the internal state change trend of the inflator; at the same time, the curve form of data analysis also provides more abundant reference basis for subsequent maintenance, further improves the practicality and readability of the early warning information, and is beneficial to the user to more comprehensively master the health status of the airbag inflator, which makes up for the deficiency of the traditional single dimension of early warning information relying on text or identifier.

[0042] In combination with Figure 4 Exemplarily, the training process of the prediction model is as follows:

[0043] Step 201: Obtain a training data set; wherein the training data set includes a plurality of training sample data; each training sample data includes working condition sample data and failure risk level standard data.

[0044] When obtaining the training data set, the following methods can be used but are not limited to:

[0045] First, obtain the working condition characteristic data inside the shell 11 of the airbag inflator, and obtain the target prediction model applicable to the airbag inflator.

[0046] Then, based on the target prediction model, the working condition characteristic data inside the shell 11 of the airbag generator is processed, and the failure risk level of the airbag generator in the processing process is obtained.

[0047] Finally, based on the working condition characteristic data inside the shell 11 of the airbag generator and the failure risk level, a training data set is obtained.

[0048] Step 202: Select target training sample data from the training data set.

[0049] Step 203: Input the working condition sample data in the target training sample data into the initial prediction model, so that the initial prediction model receives the working condition sample data through the input layer, and obtains the predicted failure risk level of the airbag generator through the processing layer. After processing the working condition sample data, the predicted failure risk level of the airbag generator is output through the output layer.

[0050] Step 204: Update the parameters of the initial prediction model based on the prediction error between the predicted failure risk level of the airbag generator and the failure risk level standard data in the target training sample data.

[0051] Step 205: Determine whether the iteration training termination condition is met; if yes, execute step 206; if no, return to step 202; wherein the iteration training termination condition is that the iteration number is not less than the number threshold, or the prediction error is not higher than the error threshold.

[0052] Step 206: Obtain the prediction model based on the parameters of the initial prediction model updated in the last iteration training operation.

[0053] In combination Figure 5 In some embodiments, the airbag generator detection and warning system further comprises an edge computing module, which is in communication connection with the internal detection module 3 and the cloud platform respectively, and is used for receiving working condition characteristic data and performing abnormal analysis on the working condition characteristic data to obtain optimized working condition characteristic data.

[0054] For example, the edge computing module can use a Tiny Machine Learning (TinyML) model to analyze the working condition characteristic data according to a preset step length to obtain the working condition characteristic data with abnormalities as the optimized working condition characteristic data. For example, when the gas pressure value or the internal temperature value suddenly changes, the gas pressure value or the internal temperature value is determined as the abnormal working condition characteristic data, or when the decomposition product concentration of the gas generating agent 6 is greater than a preset value, the decomposition product concentration of the gas generating agent 6 is determined as the abnormal working condition characteristic data, wherein the preset step length is 100ms / once.

[0055] By identifying abnormal conditions such as gas pressure mutation, internal temperature mutation, and decomposition product concentration of gas generating agent exceeding a preset value through the edge computing module, the optimized working condition characteristic data is screened out; this process not only realizes the local preliminary processing of data, avoids the problem of excessive bandwidth occupation and increased transmission delay caused by directly transmitting massive raw data to the cloud, but also quickly captures potential abnormalities of the safety airbag inflator, lays a foundation for the subsequent cloud platform to further analyze and timely respond to the early warning system, and improves the response efficiency of the entire detection and early warning system to safety hazards and the economy of data processing.

[0056] In some embodiments, the airbag inflator detection and early warning system further comprises an ambient temperature detection module, which is in signal connection with the edge computing module, and is used to obtain ambient temperature data of the environment where the airbag inflator is located. The edge computing module is used to receive the working condition characteristic data and the ambient temperature data, and to filter the pressure data in the working condition characteristic data based on the ambient temperature data.

[0057] In specific implementation, based on the determined mapping relationship between the ambient temperature data and the pressure value, it is determined whether the pressure value collected by the internal detection module 3 is a pressure abnormal value affected by the change of the ambient temperature data. If yes, the pressure value is deleted; if not, the pressure value is retained.

[0058] In this embodiment, the ambient temperature detection module can obtain the temperature data of the environment where the airbag inflator is located. After the edge computing module receives the data and the working condition characteristic data, according to the preset mapping relationship between the ambient temperature and the pressure value, the pressure abnormal value affected by the change of the ambient temperature is filtered out and deleted, and the pressure data that truly reflects the internal state of the generator is retained; the interference of the ambient temperature fluctuation on the pressure detection result can be effectively eliminated, the misjudgment of the generator due to environmental factors is avoided, the accuracy of the pressure data in the working condition characteristic data is improved, the reliable data for the subsequent cloud platform to predict the failure risk and the early warning system to accurately issue warnings is provided, and the reliability of the entire detection and early warning system for judging the health status of the airbag inflator is further enhanced.

[0059] In some embodiments, the edge computing module is further used to encrypt the working condition characteristic data according to a preset algorithm after obtaining the optimized working condition characteristic data.

[0060] For example, the edge computing module uses the AES-128 (Advanced Encryption Standard, 128-bit key) symmetric encryption algorithm to encrypt the optimized working condition characteristic data, which can ensure data confidentiality and prevent CAN bus sniffing or man-in-the-middle attacks.

[0061] In some embodiments, the internal detection module 3 comprises a pressure sensor, a gas sensor, a transmitter and a fixed seat 4. The pressure sensor is used to detect the gas pressure in the shell 11. The gas sensor is used to detect the concentration of the decomposition products of the gas-producing drug 6 in the shell 11. The transmitter is electrically connected with the pressure sensor and the gas sensor, and the transmitter is used to send the detection data of the pressure sensor and the gas sensor. The fixed seat 4 is fixedly arranged in the shell 11, and the pressure sensor, the gas sensor and the transmitter are integrally packaged in the fixed seat 4.

[0062] The internal detection module 3 of the embodiment ensures the stable installation of each component inside the generator, avoids affecting the detection accuracy due to loose structure, and can adapt to the closed space environment of the generator. It provides reliable data support for subsequent edge computing module, cloud platform data analysis and early warning, effectively solves the problem that the traditional safety airbag cannot monitor the internal state of the generator, and lays a hardware foundation for safety airbag health state monitoring.

[0063] In some embodiments, the internal detection module 3 further comprises a storage battery and a vibration power generation unit, and the storage battery and the vibration power generation unit are fixed to the fixed seat 4. The storage battery is electrically connected with the pressure sensor, the gas sensor, the transmitter and the vibration power generation unit respectively.

[0064] In the embodiment, the vibration power generation unit can be an electromagnetic vibration power generation unit, a piezoelectric vibration power generation unit or an electrostatic vibration power generation unit, etc. The vibration power generation unit can supplement electric energy by using daily vibration of the vehicle; and the storage battery guarantees continuous operation when there is no external power supply. Therefore, real-time detection can be realized when the automobile is not started and power supply by the power battery is not available.

[0065] In some embodiments, the internal detection module 3 is electrically connected with a vehicle clock spring, and a power supply line of the internal detection module 3 is arranged in parallel with or separately from an airbag point explosion line.

[0066] The vehicle clock spring itself is a mature component for transmitting electrical signals in the vehicle, and no additional power supply line needs to be added on other structures of the vehicle when the module is connected, thereby avoiding damage to the original circuit layout of the vehicle and being conducive to reducing production cost.

[0067] Meanwhile, when the vehicle clock spring is combined with the storage battery and the vibration power generation unit, even if the storage battery is discharged or the vibration power generation unit does not generate electric energy temporarily, the clock spring can continuously supply power to the components such as the pressure sensor, the gas sensor and the transmitter, so as to ensure that the module can stably collect and transmit the internal working condition data of the generator and avoid monitoring failure caused by power supply interruption, thereby laying a power supply foundation for continuous realization of subsequent data processing and early warning functions.

[0068] In some embodiments, the sensors of the internal detection module 3 are connected through I 2C (Inter-Integrated Circuit, Inter-Integrated Circuit) / SPI (Serial Peripheral Interface, Serial Peripheral Interface) interface, real-time acquisition of pressure, temperature, chemical substances and other data, providing raw information for subsequent analysis. The I2C / SPI interface adapts to the low-power and high-reliability data acquisition requirements of the sensor, ensuring real-time and accurate acquisition of raw data, laying a foundation for subsequent analysis. The data preprocessed by the edge computing module is quickly and low-delay transmitted to the vehicle-mounted 5G gateway through ultra-reliable and low-delay communication. The combination of ultra-reliable and low-delay communication and the 5G gateway greatly reduces the data transmission delay, avoids the delay of early warning caused by time delay, and adapts to the high requirements of real-time safety airbag monitoring. The vehicle-mounted 5G gateway forwards the data to the cloud platform through the MQTT (Message Queuing Telemetry Transport, Message Queuing Telemetry Transport) protocol; the characteristics of light weight and low bandwidth occupation of the MQTT protocol reduce the data transmission cost, while ensuring stable interaction between the vehicle-mounted gateway and the cloud, avoiding data loss caused by network fluctuations. The instructions of the cloud platform are returned through the 5G network, and the terminal executes OTA update, alarm, etc. The instructions of the cloud platform are returned to the terminal, which not only solves the problem of no real-time data feedback of traditional safety airbags, but also improves the system operation and risk response efficiency, and can fully enhance the practicality and reliability of the safety airbag generator detection and early warning system.

[0069] In some embodiments, the terminal includes a vehicle-mounted display module and a user terminal.

[0070] In this embodiment, the terminal includes a vehicle-mounted display module (such as a central control screen) and a user terminal (such as a mobile phone APP), which can realize multi-channel transmission and presentation of early warning information, adapt to vehicle interaction and daily use habits, improve information reach efficiency, and thus be conducive to ensuring the reliability of safety airbag use.

[0071] In some embodiments, sending the early warning information to the terminal includes:

[0072] Determining that the car is in a started state, and sending the early warning information to the vehicle-mounted display module;

[0073] Determining that the car is in an unstarted state, and sending the early warning information to the user terminal.

[0074] When the car is in a started state, the user is usually in a driving or riding scene, and at this time the early warning information is sent to the vehicle-mounted display module, which can present the safety airbag generator state in real time through an intuitive visual form. The user does not need to perform additional operations on the device, and can quickly obtain key information during driving, avoiding early warning delay caused by not checking the mobile phone.

[0075] When the automobile is in an unstarted state, for example, parked in a garage, the user is away from the vehicle, the cloud platform can ensure that the user can know the safety hazard in time even if not at the vehicle side by pushing the early warning information to the user terminal. For example, after the vehicle is parked for a long time, the gas generating agent 6 appears slow leakage, the mobile phone APP can actively push the early warning information to avoid the user finding the problem when using the vehicle next time, and ensure the safety prediction before traveling.

[0076] The application further provides an automobile applied to the airbag inflator detection and early warning system, and the automobile comprises the airbag inflator and the internal detection module 3.

[0077] The technical features of the above embodiments can be combined arbitrarily, and to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, it should be considered that the combinations are within the scope of the present application.

[0078] The above embodiments only express several implementation manners of the present application, the description is more specific and detailed, but it should not be understood as a limitation on the patent application scope. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the protection scope of the present application. Therefore, the patent protection scope of the present application should be subject to the appended claims.

Claims

1. An airbag inflator detection warning system, characterized by, The application relates to a safety airbag generator detection and early warning system. The system comprises: an internal detection module for acquiring working condition characteristic data inside a shell of a safety airbag generator; a cloud platform in signal connection with the internal detection module, for receiving and storing the working condition characteristic data, processing the working condition characteristic data based on the working condition characteristic data by using a prediction model, and obtaining a prediction failure risk level of the safety airbag generator; 2. The airbag inflator tamper-evidencing system of claim 1, wherein based on the prediction failure risk level of the safety airbag generator, obtaining early warning information and sending the early warning information to a terminal. The system further comprises:

3. The airbag inflator tamper-evidencing system of claim 2, wherein an edge computing module in communication connection with the internal detection module and the cloud platform, for receiving the working condition characteristic data, and performing abnormality analysis on the working condition characteristic data to obtain optimized working condition characteristic data.

4. The airbag inflator tamper-evidencing system of claim 1, wherein The edge computing module is further used for encrypting the working condition characteristic data according to a preset algorithm after the optimized working condition characteristic data is obtained.

5. The airbag inflator tamper-evidencing system according to any one of claims 1 to 4, wherein The working condition characteristic data comprises at least one of gas pressure, internal temperature and gas activity in the shell. The internal detection module comprises: a pressure sensor for detecting the gas pressure in the shell; a gas sensor for detecting the concentration of gas production drug decomposition products in the shell; and a transmitter in electric connection with the pressure sensor and the gas sensor, the transmitter being used for sending detection data of the pressure sensor and the gas sensor; 6. The airbag inflator tamper-evidencing system of claim 5, wherein, a fixing seat fixedly arranged in the shell, the pressure sensor, the gas sensor and the transmitter being integrally packaged in the fixing seat. The internal detection module further comprises a storage battery and a vibration power generation unit, the storage battery and the vibration power generation unit being fixed to the fixing seat, the storage battery being in electric connection with the pressure sensor, the gas sensor, the transmitter and the vibration power generation unit respectively; and / or 7. The airbag inflator tamper-evidencing system according to any one of claims 1 to 4, wherein the internal detection module is in electric connection with a vehicle clock spring.

8. The airbag inflator tamper-evidencing system of claim 7, wherein, The terminal comprises a vehicle-mounted display module and a user terminal. The sending of the early warning information to the terminal comprises: determining that the automobile is in a starting state, and sending early warning information to the vehicle-mounted display module; 9. The airbag inflator tamper-evidencing system of claim 2 or 3, wherein, determining that the automobile is in a non-starting state, and sending early warning information to the user terminal. The system further comprises:

10. An automobile characterized by comprising: an environment temperature detection module in signal connection with the edge computing module, for acquiring environment temperature data of an environment where the safety airbag generator is located, and the edge computing module is used for receiving the working condition characteristic data and the environment temperature data, and screening pressure data in the working condition characteristic data based on the environment temperature data. The application is applied to the safety airbag generator detection and early warning system according to any one of claims 1 to 9, and the automobile comprises a safety airbag generator and an internal detection module.

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