A pre-warning method and system of a front-view all-in-one machine controller and an intelligent terminal

Abstract

The application relates to a pre-warning method and system of a front-view all-in-one machine controller and an intelligent terminal, and relates to the technical field of automobile control, which comprises the following steps: receiving real-time detection information of various sensors and vehicle information; calling sensor type information and detection data information based on the real-time detection information; generating sensor reference data information based on the sensor type information; when the detection data information is inconsistent with the sensor reference data information, generating detection data deviation information based on the detection data information and the sensor reference data information; generating conversion data information based on the detection data deviation information and the sensor type information; generating comprehensive pre-warning information based on the conversion data information and the vehicle information, and outputting the comprehensive pre-warning information. The application has the effect of improving the pre-warning accuracy rate in a complex scene.

Description

Technical Field

[0001] This invention relates to the field of automotive control technology, and in particular to a warning method, system, and intelligent terminal for a forward-looking integrated controller. Background Technology

[0002] Vehicle control refers to the process of adjusting and managing the operating status and various functions of a vehicle, primarily achieved through electronic control units (ECUs). Vehicle control mainly includes powertrain control, chassis control, body control, driver assistance control, and control for new energy vehicles.

[0003] Currently, in driver assistance control, in order to improve driving safety and comfort, various sensors (such as cameras, radar, lidar, etc.) installed on the vehicle are generally used to perceive the environmental information around the vehicle in real time, and independent controllers are used to analyze and process the information detected by the sensors on the vehicle, thereby providing assistance and support to the driver.

[0004] Currently, independent controllers are generally used to analyze and process information detected by sensors on vehicles. In complex scenarios, these independent controllers rely on a single sensor, making it inconvenient to coordinate with each other. This results in limited coverage, susceptibility to environmental interference, difficulty in achieving omnidirectional risk perception, and consequently, reduced warning accuracy. Summary of the Invention

[0005] To improve the accuracy of early warning in complex scenarios, this invention provides an early warning method, system, and intelligent terminal for a forward-looking integrated controller.

[0006] In a first aspect, the present invention provides an early warning method for a forward-looking integrated camera controller, employing the following technical solution:

[0007] A warning method for a forward-looking integrated machine controller, comprising:

[0008] Receive real-time detection information from various sensors and vehicle information;

[0009] Sensor type information and detection data are retrieved based on real-time detection information;

[0010] Generate sensor reference data information based on sensor type information;

[0011] When the detection data information is inconsistent with the sensor reference data information, detection data deviation information is generated based on the detection data information and the sensor reference data information.

[0012] Transformation data information is generated based on the detection data deviation information and sensor type information;

[0013] Comprehensive early warning information is generated based on the converted data and vehicle information, and then output as comprehensive early warning information.

[0014] Optionally, methods for generating transformed data information include:

[0015] Determine if the sensor type information is the preset camera type information;

[0016] If so, the detection source information will be retrieved based on the real-time detection information;

[0017] Source and usage information is generated based on the source information and the deviation information of the detection data.

[0018] Based on the correspondence between source and usage information and preset usage conversion control information, usage conversion control information corresponding to the source and usage information is obtained;

[0019] Based on the application conversion control information, the detection data deviation information is converted and adjusted to form converted data information;

[0020] If not, then based on the correspondence between sensor type information and preset single-type conversion control information, single-type conversion control information corresponding to sensor type information is obtained;

[0021] Based on single-type conversion control information, the deviation information of the detection data is converted and adjusted to form converted data information.

[0022] Optionally, methods for generating source and usage information include:

[0023] Based on the correspondence between the detection source information and the preset source reference usage information, the source reference usage information corresponding to the detection source information is obtained;

[0024] Retrieve values ​​for the reference use based on the source reference use information;

[0025] Determine whether the base application value is only one;

[0026] If so, the source base usage information will be used as the source usage information;

[0027] If not, then based on the deviation information of the detection data, a deviation image is generated to predict its intended use.

[0028] Based on the consistency between the predicted use information from the deviation image and the source baseline use information, the selected use information is obtained and used as the source use information.

[0029] Optional methods for generating bias image prediction information include:

[0030] Retrieve deviation image information based on deviation information in the detection data;

[0031] Retrieve information on the type of deviation image and the numerical value of each deviation based on the deviation image information;

[0032] Determine whether the deviation value is only one;

[0033] If so, then based on the correspondence between the deviation image type information and the preset image type estimated use information, the image type estimated use information corresponding to the deviation image type information is obtained, and the image type estimated use information is used as the deviation image estimated use information.

[0034] If not, the image type reference deviation range corresponding to the deviation image type information is obtained based on the correspondence between the deviation image type information and the preset image type reference deviation range.

[0035] Extract single-category deviation values ​​from deviation image information based on deviation image category information;

[0036] Based on the falling within the deviation range of a single type of deviation value and the baseline deviation range of an image type, information on the selected deviation type is generated;

[0037] Based on the correspondence between the selected deviation type information and the preset selected type estimated use information, the selected type estimated use information corresponding to the selected deviation type information is obtained, and the selected type estimated use information is used as the deviation image estimated use information.

[0038] Optionally, methods for generating deviation type information include:

[0039] Determine whether the single-category deviation value falls within the image category baseline deviation range;

[0040] If so, the deviation image type information corresponding to the single type deviation value that falls within the image type baseline deviation range will be used as the initial selected type information.

[0041] Based on the initial selected category information and vehicle information, estimated category information is generated, and the estimated category information is used as the selection deviation category information.

[0042] If not, calculate the deviation between the single-category deviation value and the image category baseline deviation range and use it as the single-category deviation outlier.

[0043] The outliers of a single type are sorted from smallest to largest, and the type of deviation image corresponding to the top-ranked outlier is used as the selected type of deviation information.

[0044] Optional methods for generating the estimated category information include:

[0045] Retrieve the numerical values ​​of each category based on the initially selected category information;

[0046] Determine whether the value for each category is only one;

[0047] If so, the initially selected category information will be used as the estimated category information;

[0048] If not, then retrieve vehicle type information and vehicle brand information based on vehicle information;

[0049] Based on the correspondence between vehicle brand information and preset brand influence type information, brand influence type information corresponding to vehicle brand information is obtained.

[0050] Based on the correspondence between vehicle type information and preset type impact type information, type impact type information corresponding to vehicle type information is obtained;

[0051] Based on the overlap between brand influence category information and type influence category information, overlapping category information is selected;

[0052] The estimated category information is obtained based on the correlation between overlapping category information and initially selected category information.

[0053] Optional methods for generating comprehensive early warning information include:

[0054] Based on the converted data information, retrieve the converted source values, source sensor information, and source data information;

[0055] Based on the correspondence between source sensor information and preset sensor warning type information, sensor warning type information corresponding to the source sensor information is obtained;

[0056] Generate single-source early warning information based on source warning type information and source data information;

[0057] Determine if the source value for the conversion is only one;

[0058] If so, then the single-source early warning information will be treated as the comprehensive early warning information;

[0059] If not, then type-based impact information is generated based on sensor warning type information and vehicle information;

[0060] The source data information is adjusted based on the category benchmark impact information to form data adjustment information;

[0061] Multi-source early warning information is generated based on source early warning type information and data adjustment information, and multi-source early warning information is used as comprehensive early warning information.

[0062] Optionally, methods for generating category benchmark impact information include:

[0063] Vehicle deviation information is obtained based on the deviation between vehicle information and preset vehicle baseline information;

[0064] Based on the correspondence between vehicle deviation information and preset vehicle deviation impact type information, vehicle deviation impact type information corresponding to vehicle deviation information is obtained.

[0065] Based on the consistency between sensor warning type information and vehicle deviation impact type information, consistent type information is obtained;

[0066] Based on the correspondence between consistent category information and preset consistent category influence information, consistent category influence information corresponding to consistent category information is obtained, and consistent category influence information is used as category benchmark influence information.

[0067] Secondly, the present invention provides an early warning system for a forward-looking integrated camera controller, which adopts the following technical solution:

[0068] A warning system for a forward-looking integrated camera controller includes:

[0069] The acquisition module is used to acquire real-time detection information and vehicle information;

[0070] A memory for storing a warning method for a forward-looking integrated machine controller as described in any one of the first aspects;

[0071] The processor loads and executes programs from memory.

[0072] Thirdly, the present invention provides a smart terminal, which adopts the following technical solution:

[0073] A smart terminal includes a memory and a processor, wherein the memory stores a computer program and is capable of being loaded and executed by the processor as described in any one of the first aspects, a warning method for a forward-looking integrated machine controller.

[0074] In summary, the present invention has at least one of the following beneficial technical effects:

[0075] 1. By receiving real-time detection information and vehicle information, and retrieving sensor type information and detection data information, sensor reference data information is generated based on sensor type information. When the detection data information is inconsistent with the sensor reference data information, detection data deviation information is generated based on the detection data information and the sensor reference data information. Then, conversion data information is generated based on the detection data deviation information and the sensor type information. Finally, comprehensive warning information is generated based on the conversion data information and the vehicle information and output. Thus, warnings are issued based on multiple sensors, improving the accuracy of warnings in complex scenarios.

[0076] 2. By determining whether the sensor type information is the preset camera type information, if it is camera type information, the source information is retrieved through real-time detection information and generated with the detection data deviation information to form source application information. The application conversion control information is obtained through the source application information and the detection data deviation information is converted and adjusted to form conversion data information. If it is not camera type information, the single type conversion control information is obtained through the sensor type information and the detection data deviation information is converted and adjusted to form conversion data information, thereby improving the accuracy of the acquired conversion data information.

[0077] 3. By detecting source information, the source reference usage information is obtained, and the reference usage value is retrieved from the source reference usage information. When there is only one reference usage value, the source reference usage information is used as the source usage information. When there is more than one, deviation image is generated by detecting data deviation information to estimate usage information. Then, the consistency between the deviation image estimated usage information and the source reference usage information is used to obtain the selected usage information, and the selected usage information is used as the source usage information, thereby improving the accuracy of the obtained source usage information. Attached Figure Description

[0078] Figure 1 This is a flowchart of the early warning method of the front-view all-in-one machine controller according to an embodiment of this application;

[0079] Figure 2 This is a flowchart of the method for generating converted data information according to an embodiment of this application;

[0080] Figure 3 This is a flowchart illustrating the method for generating source and usage information according to an embodiment of this application;

[0081] Figure 4 This is a flowchart of a method for generating deviation image prediction application information according to an embodiment of this application;

[0082] Figure 5 This is a flowchart of the method for generating information on the selection of deviation types according to an embodiment of this application;

[0083] Figure 6 This is a flowchart of the method for generating estimated category information according to an embodiment of this application;

[0084] Figure 7 This is a flowchart of the method for generating comprehensive early warning information according to an embodiment of this application;

[0085] Figure 8 This is a flowchart of a method for generating category benchmark influence information according to an embodiment of this application. Detailed Implementation

[0086] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0087] A warning method for a forward-looking integrated controller acquires and receives real-time detection information and vehicle information from various sensors. When the detection data information is inconsistent with the sensor reference data information, the method analyzes the inconsistent data information simultaneously to generate and output comprehensive warning information. This enables comprehensive warning based on the data obtained from various sensors, improving the accuracy of warnings in complex scenarios.

[0088] Reference Figure 1 This invention discloses a warning method for a forward-looking integrated machine controller, comprising:

[0089] Step S100: Receive real-time detection information from each sensor and vehicle information.

[0090] Real-time detection information refers to the detection information collected in real time by various sensors mounted on the vehicle. This information is transmitted after being collected by each sensor in real time. Vehicle information refers to basic parameter information such as the vehicle's brand, model, and type. This vehicle information is obtained by querying the vehicle management system.

[0091] Step S101: Retrieve sensor type information and detection data information based on real-time detection information.

[0092] Sensor type information refers to the type of sensor, including forward-facing cameras, forward-facing millimeter-wave radar, DMS cameras, and corner radars. Detection data information refers to the data obtained after detection by each sensor. Real-time detection information includes both sensor type information and detection data, which can be retrieved for subsequent use.

[0093] Step S102: Generate sensor reference data information based on sensor type information.

[0094] Among them, sensor reference data information refers to the reference data information corresponding to different sensors during normal detection. Sensor reference data information is obtained by inputting sensor type information into a preset sensor type database, facilitating subsequent use. The sensor type database stores a lookup table of different sensor type information and their corresponding sensor reference data information, and is obtained through pre-input.

[0095] Step S103: When the detection data information is inconsistent with the sensor reference data information, detection data deviation information is generated based on the detection data information and the sensor reference data information.

[0096] Among them, the detection data deviation information refers to the data information corresponding to the deviation of the sensor detection data. When the detection data information is inconsistent with the sensor reference data information, it indicates that the sensor has detected an abnormality in the vehicle. Therefore, the deviation between the data information and the sensor reference data information is analyzed, and the data corresponding to the deviation is used as the detection data deviation information for subsequent use.

[0097] Step S104: Generate conversion data information based on the detection data deviation information and sensor type information.

[0098] The conversion of data information refers to transforming data into the same data type. This is achieved by converting the detection data deviation information based on the sensor type information, thus generating converted data information for later use. The specific steps for generating the converted data information are detailed in steps S200 to S206.

[0099] Step S105: Generate comprehensive early warning information based on the converted data information and vehicle information, and output the comprehensive early warning information.

[0100] The comprehensive early warning information refers to the early warning information generated from abnormal data collected by various sensors. By analyzing the transformed data and vehicle information, comprehensive early warning information is generated and output, thereby enabling simultaneous comprehensive early warning based on data from various sensors and improving the accuracy of early warnings in complex scenarios. The specific steps for generating comprehensive early warning information are described in steps S700 to S707.

[0101] exist Figure 1 In step S104, to further ensure the rationality of the converted data information, it is necessary to perform further separate analysis and calculation on the converted data information, specifically through... Figure 2 The steps shown are explained in detail.

[0102] Reference Figure 2 The method for generating transformed data information includes the following steps:

[0103] Step S200: Determine whether the sensor type information is the preset camera type information. If yes, proceed to step S201; if no, proceed to step S205.

[0104] The camera type information refers to the type information corresponding to a camera when the sensor is used. This information is obtained through pre-input. By determining whether the sensor type information matches the preset camera type information, it can be determined whether direct conversion can be performed based on the sensor type.

[0105] Step S201: Retrieve detection source information based on real-time detection information.

[0106] Among them, the detection source information refers to the source information of the detection data, and real-time detection information includes the detection source information.

[0107] When the sensor type information is the preset camera type information, it means that it is not possible to directly convert based on the sensor type. Therefore, the detection source information is retrieved through real-time detection information for convenient subsequent use.

[0108] Step S202: Generate source and purpose information based on the detection source information and the deviation information of the detection data.

[0109] The source and usage information refers to the usage information corresponding to the data source. This information is generated by analyzing the deviation information between the detection source information and the detection data, facilitating subsequent use. The specific steps for generating the source and usage information are described in steps S300 to S305.

[0110] Step S203: Based on the correspondence between source usage information and preset usage conversion control information, obtain usage conversion control information corresponding to the source usage information.

[0111] Among them, the application conversion control information refers to the control information used when converting deviation data according to its application. Different source application information corresponds to different application conversion control information. The application conversion control information is obtained by querying a preset source application database for matching, which facilitates subsequent use. The source application database pre-stores a lookup table of different source application information and their corresponding application conversion control information, and is obtained after pre-input.

[0112] Step S204: Based on the application conversion control information, the detection data deviation information is converted and adjusted to form converted data information.

[0113] Specifically, by converting and adjusting the data type corresponding to the deviation information of the detection data through the application conversion control information, the conversion data information is formed, thereby improving the accuracy of the acquired conversion data information.

[0114] Step S205: Based on the correspondence between sensor type information and preset single-type conversion control information, obtain single-type conversion control information corresponding to the sensor type information.

[0115] When the sensor type information is the preset camera type information, it means that the conversion cannot be directly performed based on the sensor type. Therefore, the single-type conversion control information is obtained by querying the preset sensor type database through the sensor type information. The sensor type database also stores a table of different sensor type information and their corresponding single-type conversion control information. The sensor type database is obtained after pre-input.

[0116] Step S206: Based on the single-type conversion control information, the detection data deviation information is converted and adjusted to form converted data information.

[0117] In this process, the data type corresponding to the deviation information of the detection data is converted and adjusted by single-type conversion control information to form converted data information, thereby improving the accuracy of the acquired converted data information.

[0118] exist Figure 2 In step S202, to further ensure the rationality of the source and usage information, it is necessary to perform further separate analysis and calculation on the source and usage information, specifically through... Figure 3 The steps shown are explained in detail.

[0119] Reference Figure 3 The method for generating source and purpose information includes the following steps:

[0120] Step S300: Based on the correspondence between the detected source information and the preset source reference usage information, obtain the source reference usage information corresponding to the detected source information.

[0121] Among them, the source reference usage information refers to the reference usage information corresponding to the source of the test data. Different test source information corresponds to different source reference usage information. The source reference usage information is obtained by inputting the test source information into a preset source reference usage database, which facilitates subsequent use. The source reference usage database pre-stores a lookup table of different test source information and their corresponding source reference usage information, and is obtained after pre-input.

[0122] Step S301: Retrieve the reference usage values ​​based on the source reference usage information.

[0123] Among them, the reference use value refers to the reference use value corresponding to the source of the measured data. By counting the number of reference use information corresponding to the source, the counting result is retrieved as the reference use value for convenient subsequent use.

[0124] Step S302: Determine whether the reference application value is only one. If yes, proceed to step S303; if no, proceed to step S304.

[0125] Specifically, by determining whether there is only one reference use value, it can be determined whether further selection and analysis of the source reference use information is needed.

[0126] Step S303: Use the source reference usage information as the source usage information.

[0127] When there is only one reference use value, it means that there is no need to further select and analyze the source reference use information. Therefore, the source reference use information is used as the source use information to improve the accuracy of the obtained source use information.

[0128] Step S304: Generate deviation image prediction information based on the deviation information of the detection data.

[0129] The deviation image prediction application information refers to the application information obtained by predicting the application based on the deviation image corresponding to the data deviation. This information is generated by analyzing the deviation information of the detection data, facilitating subsequent use. The specific steps for generating the deviation image prediction application information are described in steps S400 to S407.

[0130] Step S305: Based on the consistency between the predicted usage information and the source baseline usage information, select usage information is obtained and used as the source usage information.

[0131] The selected application information refers to the application information obtained by selecting the baseline application based on the application predicted by the deviation image. By analyzing the consistency between the application information predicted by the deviation image and the source baseline application information, the applications whose application information is consistent with the source baseline application information are used as the selected application information, and then the selected application information is used as the source application information, thereby improving the accuracy of the obtained source application information.

[0132] exist Figure 3 In step S304, to further ensure the rationality of the predicted use information of the deviation image, it is necessary to perform further separate analysis and calculation on the predicted use information of the deviation image. Specifically, this is done through... Figure 4 The steps shown are explained in detail.

[0133] Reference Figure 4 The method for generating deviation image prediction application information includes the following steps:

[0134] Step S400: Retrieve deviation image information based on detection data deviation information.

[0135] The detection data deviation information includes deviation image information, which refers to the image information corresponding to the deviation data. The deviation image information is retrieved using the detection data deviation information for convenient subsequent use.

[0136] Step S401: Retrieve the deviation image type information and the number of deviation values ​​based on the deviation image information.

[0137] The deviation image information includes deviation image type information and deviation count. Deviation image type information refers to the category to which the image corresponding to the deviation data belongs, including lane line deviation types, traffic sign deviation types, driver status deviation types, external vehicle deviation types, and external pedestrian deviation types, etc. The deviation count refers to the numerical value corresponding to the deviation image type information. The deviation image type information is retrieved from the deviation image information, and the retrieved deviation image type information is counted. The count result is used as the deviation count for subsequent use.

[0138] Step S402: Determine whether there is only one deviation value. If yes, proceed to step S403; if no, proceed to step S404.

[0139] In this process, by determining whether there is only one deviation value, it can be determined whether the application can be predicted directly based on the type of deviation image.

[0140] Step S403: Based on the correspondence between the deviation image type information and the preset image type estimated use information, obtain the image type estimated use information corresponding to the deviation image type information, and use the image type estimated use information as the deviation image estimated use information.

[0141] Among them, the image type predicted use information refers to the use information obtained after predicting the use based on the image type with deviation. Different image type deviations correspond to different image type predicted use information. When the deviation value is only one, it means that the use can be directly predicted based on the image type with deviation. Therefore, by inputting the image type deviation information into the preset image type predicted use database, the image type predicted use information is obtained through matching, and this image type predicted use information is used as the image type predicted use information with deviation, thereby improving the accuracy of the obtained image type predicted use information with deviation.

[0142] The image type prediction application database pre-stores a table that compares information on different biased image types with their corresponding image type prediction application information. The image type prediction application database is obtained after pre-input.

[0143] Step S404: Based on the correspondence between the deviation image type information and the preset image type benchmark deviation interval, obtain the image type benchmark deviation interval corresponding to the deviation image type information.

[0144] The image type baseline deviation range refers to the allowable deviation area for a given image type under normal circumstances. Different image type deviations correspond to different baseline deviation ranges. When there is more than one deviation value, it means that the application cannot be directly estimated based on the image type. Therefore, the image type deviation information is input into a preset image type baseline deviation database to match and obtain the image type baseline deviation range, facilitating subsequent use. The image type baseline deviation database pre-stores a lookup table of different image type deviations and their corresponding baseline deviation ranges, which is obtained after pre-input.

[0145] Step S405: Retrieve single-type deviation values ​​from the deviation image information based on the deviation image type information.

[0146] Among them, the single-category deviation value refers to the deviation value corresponding to the deviation image of a single category. The single-category deviation value is obtained by retrieving the deviation image corresponding to the category information of the deviation image from the deviation image information and calculating the area of ​​the deviation image, which is convenient for subsequent use.

[0147] Step S406: Generate information on the selected deviation type based on whether the single-type deviation value falls within the reference deviation range of the image type.

[0148] The selection of deviation type information refers to the type information obtained after selecting deviation types. This information is generated by analyzing the occurrence of individual deviation values ​​within the image type baseline deviation range, facilitating subsequent use. The specific steps for generating the selection of deviation type information are described in steps S500 to S504.

[0149] Step S407: Based on the correspondence between the selected deviation type information and the preset selected type estimated use information, obtain the selected type estimated use information corresponding to the selected deviation type information, and use the selected type estimated use information as the deviation image estimated use information.

[0150] The selected category predicted use information refers to the use information obtained after estimating the use based on the selected category. Different selection deviation category information corresponds to different selected category predicted use information. By inputting the selection deviation category information into a preset selected category predicted use database, the selected category predicted use information is obtained through matching. This selected category predicted use information is then used as the deviation image predicted use information, thereby improving the accuracy of the obtained deviation image predicted use information. The selected category predicted use database pre-stores a lookup table of different selection deviation category information and their corresponding selected category predicted use information. The selected category predicted use database is obtained through pre-input.

[0151] exist Figure 4 In step S406, to further ensure the rationality of the selected deviation type information, it is necessary to perform further separate analysis and calculation on the selected deviation type information. Specifically, this is done through... Figure 5 The steps shown are explained in detail.

[0152] Reference Figure 5 The method for generating information on the types of deviations includes the following steps:

[0153] Step S500: Determine whether the single-type deviation value falls within the image type baseline deviation range. If yes, proceed to step S501; if no, proceed to step S503.

[0154] The selection method is determined by checking whether the deviation value of a single type falls within the reference deviation range of the image type.

[0155] Step S501: Use the deviation image type information corresponding to the single type deviation value that falls within the image type baseline deviation range as the initial selected type information.

[0156] Specifically, when a single-type deviation value falls within the image type baseline deviation range, the deviation image type information corresponding to the single-type deviation value falling within the image type baseline deviation range is selected and defined as the initial selected type information for convenient subsequent use.

[0157] Step S502: Generate estimated category information based on the initial selected category information and vehicle information, and use the estimated category information as the selection deviation category information.

[0158] Among them, the estimated category information refers to the category information obtained after estimating the initially selected categories. By analyzing the initially selected category information and vehicle information, the estimated category information is generated and used as the selection deviation category information to improve the accuracy of the obtained selection deviation category information.

[0159] Step S503: Calculate the deviation value between the single-type deviation value and the image type baseline deviation interval and use it as the single-type deviation outlier value.

[0160] Specifically, when a single-category deviation value falls within the image category baseline deviation range, the deviation between the single-category deviation value and the image category baseline deviation range is calculated, and the calculated deviation value is used as a single-category deviation outlier for convenient subsequent use.

[0161] Step S504: Sort the single-type deviation outliers from smallest to largest, and use the deviation image type information corresponding to the single-type deviation outlier ranked first as the selected deviation type information.

[0162] Specifically, by sorting single-type deviation outliers from smallest to largest, and selecting the deviation image type information corresponding to the top-ranked single-type deviation outlier as the selected deviation type information, the accuracy of the obtained selected deviation type information is improved.

[0163] exist Figure 5 In step S502, to further ensure the rationality of the estimated category information, it is necessary to perform further separate analysis and calculation on the estimated category information, specifically through... Figure 6 The steps shown are explained in detail.

[0164] Reference Figure 6 The method for generating the estimated category information includes the following steps:

[0165] Step S600: Retrieve the number of categories based on the initially selected category information.

[0166] The "number of categories" refers to the number of categories in the initially selected category information. The number of categories in the initially selected category information is counted, and the count result is retrieved as the number of categories for convenient use later.

[0167] Step S601: Determine whether the value of the category is only one. If yes, proceed to step S602; if no, proceed to step S603.

[0168] Specifically, by determining whether there is only one category, it can be determined whether further analysis and selection of the initially selected category information is needed.

[0169] Step S602: Use the initially selected category information as the estimated category information.

[0170] When there is only one category, it means that there is no need to further analyze and select the initial category information. Therefore, the initial category information is used as the estimated category information to improve the accuracy of the obtained estimated category information.

[0171] Step S603: Retrieve vehicle type information and vehicle brand information based on vehicle information.

[0172] The vehicle type information refers to the vehicle model, including sedans, SUVs, and MPVs. The vehicle brand information refers to the brand, including Toyota, Volkswagen, and BMW. This information, including vehicle type and brand, is retrieved for convenient subsequent use.

[0173] Step S604: Based on the correspondence between vehicle brand information and preset brand influence type information, obtain brand influence type information corresponding to the vehicle brand information.

[0174] Among them, brand influence category information refers to the category information corresponding to the impact of vehicle brand on the type of image deviation. Different vehicle brand information corresponds to different brand influence category information. Brand influence category information is obtained by inputting vehicle brand information into a preset brand influence category database for convenient subsequent use. The brand influence category database pre-stores a lookup table of different vehicle brand information and their corresponding brand influence category information, which is obtained after pre-input.

[0175] Step S605: Based on the correspondence between vehicle type information and preset type influence type information, obtain type influence type information corresponding to vehicle type information.

[0176] Among them, the type influence category information refers to the category information corresponding to the influence of vehicle type on the type of image deviation. Different vehicle type information corresponds to different type influence category information. The type influence category information is obtained by inputting vehicle type information into a preset type influence category database for convenient subsequent use. The type influence category database pre-stores a lookup table of different vehicle type information and their corresponding type influence category information, and is obtained after pre-input.

[0177] Step S606: Select overlapping category information based on the overlap between brand influence category information and type influence category information.

[0178] Among them, overlapping category information refers to the impact category information caused by the overlap between vehicle type and vehicle brand. By analyzing the overlap between brand impact category information and type impact category information, overlapping categories are selected as overlapping category information for convenient subsequent use.

[0179] Step S607: Obtain the estimated category information based on the correlation between overlapping category information and initially selected category information.

[0180] Specifically, by inputting overlapping category information into a pre-defined associated category database, associated category information is obtained through matching. The consistency between the associated category information and the initially selected category information is analyzed, and the initially selected category information that matches the associated category information is used as the estimated category information, thus improving the accuracy of the obtained estimated category information. The associated category database pre-stores a lookup table of different overlapping category information and their corresponding associated category information, and is obtained after pre-input.

[0181] exist Figure 1 In step S105, to further ensure the rationality of the comprehensive early warning information, it is necessary to perform further separate analysis and calculation on the comprehensive early warning information. Specifically, this is done through... Figure 7 The steps shown are explained in detail.

[0182] Reference Figure 7 The method for generating comprehensive early warning information includes the following steps:

[0183] Step S700: Retrieve the source values, source sensor information, and source data information based on the conversion data information.

[0184] Here, "source values" refers to the numerical values ​​corresponding to the source of the converted data, "source sensor information" refers to the sensor corresponding to the source of the converted data, and "source data information" refers to the data information corresponding to a single source after conversion. The conversion data information includes the source values, source sensor information, and source data information. These information can be retrieved through the conversion data information for convenient subsequent use.

[0185] Step S701: Based on the correspondence between the source sensor information and the preset sensor warning type information, obtain the sensor warning type information corresponding to the source sensor information.

[0186] The sensor warning type information refers to the warning type information corresponding to the sensor-based warning. Different source sensor information corresponds to different sensor warning type information. The sensor warning type information is obtained by inputting the source sensor information into a preset sensor warning type database, facilitating subsequent use. The sensor warning type database pre-stores different source sensor information and their corresponding sensor warning type information, and is obtained after pre-input.

[0187] Step S702: Generate single-source early warning information based on source early warning type information and source data information.

[0188] Among them, single-source early warning information refers to early warning information that provides an early warning for deviation data from a single source. By combining the source early warning type information with the source data information, early warning information containing specific deviation data and early warning type is formed and used as single-source early warning information for convenient subsequent use.

[0189] Step S703: Determine whether the conversion source value is only one. If yes, proceed to step S704; if no, proceed to step S705.

[0190] Specifically, by determining whether the conversion source value is only one, it can be determined whether further analysis of the single-source warning information is needed.

[0191] Step S704: Treat single-source early warning information as comprehensive early warning information.

[0192] When there is only one source value, it means that no further analysis of the single-source warning information is needed, so the single-source warning information is regarded as the comprehensive warning information.

[0193] Step S705: Generate category benchmark impact information based on sensor warning category information and vehicle information.

[0194] Among them, the category baseline impact information refers to the baseline impact information generated by the data type conversion required for the warning category. When there is only one conversion source value, it means that further analysis of the single-source warning information is not required. Therefore, the category baseline impact information is generated by analyzing the sensor warning category information and vehicle information for convenient subsequent use. The specific method for generating the category baseline impact information is described in steps S800 to S803.

[0195] Step S706: Adjust the source data information based on the category benchmark impact information to form data adjustment information.

[0196] Specifically, by adjusting the data type corresponding to the source data information based on the category benchmark impact information, data adjustment information is formed, which is convenient for subsequent use.

[0197] Step S707: Generate multi-source early warning information based on source early warning type information and data adjustment information, and use the multi-source early warning information as comprehensive early warning information.

[0198] In this process, by combining source warning type information with data adjustment information, warning information containing specific deviation data and warning types is formed and used as multi-source warning information. Then, the multi-source warning information is used as comprehensive warning information to improve the accuracy of the acquired comprehensive warning information.

[0199] exist Figure 7 In step S705, to further ensure the rationality of the category benchmark impact information, it is necessary to perform further separate analysis and calculation on the category benchmark impact information. Specifically, this is done through... Figure 8 The steps shown are explained in detail.

[0200] Reference Figure 8 The method for generating category benchmark impact information includes the following steps:

[0201] Step S800: Obtain vehicle deviation information based on the deviation between vehicle information and preset vehicle reference information.

[0202] Among them, vehicle baseline information refers to the technical parameters of the vehicle under normal conditions, including the engine's output power and displacement. This information is obtained through pre-input. Vehicle deviation information refers to the deviation information corresponding to deviations in the vehicle's technical parameters. This is achieved by analyzing the deviation between the vehicle information and the preset vehicle baseline information, and using the resulting deviation as vehicle deviation information for subsequent use.

[0203] Step S801: Based on the correspondence between vehicle deviation information and preset vehicle deviation influence type information, obtain vehicle deviation influence type information corresponding to the vehicle deviation information.

[0204] Among them, vehicle deviation impact type information refers to the type of impact information when a deviation in a vehicle's technical parameters requires a warning. Different vehicle deviation information corresponds to different vehicle deviation impact type information. The vehicle deviation impact type information is obtained by inputting the vehicle deviation information into a preset vehicle deviation impact type database, facilitating subsequent use. The vehicle deviation impact type database stores a lookup table of different vehicle deviation information and their corresponding vehicle deviation impact type information, which is obtained after pre-input.

[0205] Step S802: Based on the consistency between the sensor warning type information and the vehicle deviation impact type information, obtain consistent type information.

[0206] In this process, the consistency between sensor warning type information and vehicle deviation impact type information is analyzed, and the types of sensor warning type information and vehicle deviation impact type information are regarded as consistent type information, which facilitates subsequent use.

[0207] Step S803: Based on the correspondence between consistent category information and preset consistent category influence information, obtain consistent category influence information corresponding to consistent category information, and use consistent category influence information as category benchmark influence information.

[0208] Among them, consistent category impact information refers to the impact information of consistent categories on the required data types. Different consistent category information corresponds to different consistent category impact information. Consistent category impact information is obtained by inputting consistent category information into a pre-set consistent category impact database and then matching it. This consistent category impact information is used as the category baseline impact information, thereby improving the accuracy of the obtained category baseline impact information. The consistent category impact database pre-stores a lookup table of different consistent category information and their corresponding consistent category impact information, and is obtained through pre-input.

[0209] Based on the same inventive concept, embodiments of the present invention provide an early warning system for a forward-looking integrated camera controller, comprising:

[0210] The acquisition module is used to acquire real-time detection information and vehicle information;

[0211] A memory for storing a warning method for a front-view integrated controller as described above;

[0212] The processor loads and executes programs from memory.

[0213] Based on the same inventive concept, embodiments of the present invention provide a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor as described above for a pre-warning method of a front-view all-in-one controller.

[0214] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above 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. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0215] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A warning method for a forward-looking integrated machine controller, characterized in that, include: Receive real-time detection information from various sensors and vehicle information; Sensor type information and detection data are retrieved based on real-time detection information; Generate sensor reference data information based on sensor type information; When the detection data information is inconsistent with the sensor reference data information, detection data deviation information is generated based on the detection data information and the sensor reference data information. Converted data information is generated based on the detection data deviation information and sensor type information; Based on the converted data and vehicle information, a comprehensive early warning message is generated and output. The methods for generating comprehensive early warning information include: Based on the converted data information, retrieve the converted source values, source sensor information, and source data information; Based on the correspondence between source sensor information and preset sensor warning type information, sensor warning type information corresponding to the source sensor information is obtained; Generate single-source early warning information based on source warning type information and source data information; Determine if the source value for the conversion is only one; If so, then the single-source early warning information will be treated as the comprehensive early warning information; If not, then type-based impact information is generated based on sensor warning type information and vehicle information; The source data information is adjusted based on the category benchmark impact information to form data adjustment information; Multi-source early warning information is generated based on source early warning type information and data adjustment information, and multi-source early warning information is used as comprehensive early warning information.

2. The early warning method for a forward-looking integrated machine controller according to claim 1, characterized in that, Methods for generating transformed data information include: Determine if the sensor type information is the preset camera type information; If so, the detection source information will be retrieved based on the real-time detection information; Source and usage information is generated based on the source information and the deviation information of the detection data. Based on the correspondence between source and usage information and preset usage conversion control information, usage conversion control information corresponding to the source and usage information is obtained; Based on the application conversion control information, the detection data deviation information is converted and adjusted to form converted data information; If not, then based on the correspondence between sensor type information and preset single-type conversion control information, single-type conversion control information corresponding to sensor type information is obtained; Based on single-type conversion control information, the deviation information of the detection data is converted and adjusted to form converted data information.

3. A warning method for a forward-looking integrated machine controller based on claim 2, characterized in that, Methods for generating source and usage information include: Based on the correspondence between the detection source information and the preset source reference usage information, the source reference usage information corresponding to the detection source information is obtained; Retrieve values ​​for the reference use based on the source reference use information; Determine whether the base application value is only one; If so, the source base usage information will be used as the source usage information; If not, then based on the deviation information of the detection data, a deviation image is generated to predict its intended use. Based on the consistency between the predicted use information from the deviation image and the source baseline use information, the selected use information is obtained and used as the source use information.

4. A warning method for a forward-looking integrated machine controller based on claim 3, characterized in that, Methods for generating deviation image prediction application information include: Retrieve deviation image information based on deviation information in the detection data; Retrieve information on the type of deviation image and the numerical value of each deviation based on the deviation image information; Determine whether the deviation value is only one; If so, then based on the correspondence between the deviation image type information and the preset image type estimated use information, the image type estimated use information corresponding to the deviation image type information is obtained, and the image type estimated use information is used as the deviation image estimated use information. If not, the image type reference deviation range corresponding to the deviation image type information is obtained based on the correspondence between the deviation image type information and the preset image type reference deviation range. Extract single-category deviation values ​​from deviation image information based on deviation image category information; Based on the falling within the deviation range of a single type of deviation value and the baseline deviation range of an image type, information on the selected deviation type is generated; Based on the correspondence between the selected deviation type information and the preset selected type estimated use information, the selected type estimated use information corresponding to the selected deviation type information is obtained, and the selected type estimated use information is used as the deviation image estimated use information.

5. A warning method for a forward-looking integrated machine controller based on claim 4, characterized in that, Methods for generating information on the types of deviations include: Determine whether the single-category deviation value falls within the image category baseline deviation range; If so, the deviation image type information corresponding to the single type deviation value that falls within the image type baseline deviation range will be used as the initial selected type information. Based on the initial selected category information and vehicle information, estimated category information is generated, and the estimated category information is used as the selection deviation category information. If not, calculate the deviation between the single-category deviation value and the image category baseline deviation range and use it as the single-category deviation outlier. The outliers of a single type are sorted from smallest to largest, and the type of deviation image corresponding to the top-ranked outlier is used as the selected type of deviation information.

6. A warning method for a forward-looking integrated machine controller based on claim 5, characterized in that, Methods for generating predicted category information include: Retrieve the numerical values ​​of each category based on the initially selected category information; Determine whether the value for each category is only one; If so, the initially selected category information will be used as the estimated category information; If not, then retrieve vehicle type information and vehicle brand information based on vehicle information; Based on the correspondence between vehicle brand information and preset brand influence type information, brand influence type information corresponding to vehicle brand information is obtained. Based on the correspondence between vehicle type information and preset type impact type information, type impact type information corresponding to vehicle type information is obtained; Based on the overlap between brand influence category information and type influence category information, overlapping category information is selected; The estimated category information is obtained based on the correlation between overlapping category information and initially selected category information.

7. A warning method for a forward-looking integrated machine controller based on claim 1, characterized in that, Methods for generating category benchmark impact information include: Vehicle deviation information is obtained based on the deviation between vehicle information and preset vehicle baseline information; Based on the correspondence between vehicle deviation information and preset vehicle deviation impact type information, vehicle deviation impact type information corresponding to vehicle deviation information is obtained. Based on the consistency between sensor warning type information and vehicle deviation impact type information, consistent type information is obtained; Based on the correspondence between consistent category information and preset consistent category influence information, consistent category influence information corresponding to consistent category information is obtained, and consistent category influence information is used as category benchmark influence information.

8. A warning system for a forward-looking integrated machine controller, characterized in that, include: The acquisition module is used to acquire real-time detection information and vehicle information; A memory for storing a warning method for a forward-looking integrated machine controller as described in any one of claims 1 to 7; The processor loads and executes programs from memory.

9. A smart terminal, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program and can be loaded and executed by the processor, as described in any one of claims 1 to 7, a warning method for a forward-looking integrated machine controller.

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