Early warning method and system of foresight all-in-one machine controller and intelligent terminal

Abstract

The invention relates to an early warning method and system of a foresight all-in-one machine controller and an intelligent terminal, and relates to the technical field of automobile control, and the method comprises the steps: receiving real-time detection information of each sensor 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, detection data deviation information is generated 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; and generating comprehensive early warning information based on the converted data information and the vehicle information, and outputting the comprehensive early warning information. The method has the effect of improving the early warning accuracy in a complex scene.

Description

Technical Field

[0001] The present invention relates to the field of automobile control technology, and in particular to an early warning method, system and intelligent terminal for a front-view integrated machine controller. Background Art

[0002] Vehicle control refers to the process of regulating and managing a vehicle's operating status and various functions, primarily through electronic control units. This includes powertrain control, chassis control, body control, driver assistance control, and new energy vehicle control.

[0003] Currently, in driving assistance control, in order to improve driving safety and comfort, various sensors installed on the vehicle (such as cameras, radars, lidars, etc.) 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] At present, independent controllers are generally used to analyze and process the information detected by sensors on the vehicle. When in complex scenarios, each independent controller relies on a single sensor, which makes it inconvenient to coordinate. As a result, the coverage range is limited and susceptible to environmental interference, making it difficult to achieve omnidirectional risk perception, which in turn leads to reduced warning accuracy. Summary of the Invention

[0005] In order to improve the warning accuracy in complex scenarios, the present invention provides a warning method, system and intelligent terminal for a forward-looking integrated machine controller.

[0006] In a first aspect, the present invention provides an early warning method for a front-view integrated machine controller, which adopts the following technical solution: A warning method for a front-view integrated machine controller, comprising: Receive real-time detection information and vehicle information from each sensor; Retrieve sensor type information and detection data information based on real-time detection information; generating sensor benchmark data information based on the 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; generating conversion data information based on the detection data deviation information and the sensor type information; Generate comprehensive warning information based on the converted data information and vehicle information, and output the comprehensive warning information.

[0007] Optionally, the method for generating the conversion data information includes: Determine whether the sensor type information is the preset camera type information; If yes, retrieve the detection source information based on the real-time detection information; Generate source usage information based on detection source information and detection data deviation information; Based on the correspondence between the source usage information and the preset usage conversion control information, the usage conversion control information corresponding to the source usage information is obtained; converting and adjusting the detection data deviation information based on the purpose conversion control information to form conversion data information; If not, obtaining the single-type conversion control information corresponding to the sensor type information based on the correspondence between the sensor type information and the preset single-type conversion control information; The detection data deviation information is converted and adjusted based on the single-type conversion control information to form conversion data information.

[0008] Optional methods for generating source use 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 the benchmark usage value based on the source benchmark usage information; Determine whether the number of base usages is only one; If yes, the source base use information is used as the source use information; If not, generating deviation image estimated usage information based on the detection data deviation information; Based on the consistency between the estimated usage information of the deviation image and the source benchmark usage information, the selected usage information is obtained, and the selected usage information is used as the source usage information.

[0009] Optionally, a method for generating the deviation image estimated usage information includes: Retrieving deviation image information based on detection data deviation information; Retrieving deviation image type information and deviation value based on deviation image information; Determine whether the deviation value is only one; If yes, then based on the correspondence between the deviation image type information and the preset image type estimated usage information, obtain the image type estimated usage information corresponding to the deviation image type information, and use the image type estimated usage information as the deviation image estimated usage information; If not, then based on the correspondence between the deviation image type information and the preset image type reference deviation interval, the image type reference deviation interval corresponding to the deviation image type information is obtained; Retrieving a single-category deviation value from the deviation image information based on the deviation image category information; Based on whether the single category deviation value falls within the image category benchmark deviation range, the selected deviation category information is generated; Based on the correspondence between the selected deviation type information and the preset selected type estimated usage information, the selected type estimated usage information corresponding to the selected deviation type information is obtained, and the selected type estimated usage information is used as the deviation image estimated usage information.

[0010] Optionally, a method for generating selected deviation type information includes: Determining whether the single category deviation value falls within the image category benchmark deviation interval; If yes, the deviation image category information corresponding to the single category deviation value falling within the image category reference deviation interval is used as the initial selected category information; Generate estimated category information based on the initial selected category information and the vehicle information, and use the estimated category information as the selected deviation category information; If not, the deviation between the single category deviation value and the image category benchmark deviation interval is calculated and used as the single category deviation outlier; The single-category deviation outliers are sorted from small to large, and the deviation image category information corresponding to the single-category deviation outlier that ranks first is used as the selected deviation category information.

[0011] Optionally, methods for generating estimated category information include: Retrieve the number of categories based on the initially selected category information; Determine whether the number of types is only one; If yes, the initially selected category information is used as the estimated category information; If not, retrieve vehicle type information and vehicle brand information based on the vehicle information; Based on the correspondence between the vehicle brand information and the preset brand influence category information, the brand influence category information corresponding to the vehicle brand information is obtained; Based on the correspondence between the vehicle type information and the preset type impact category information, the type impact category information corresponding to the vehicle type information is obtained; Selecting overlapping category information based on the overlap between brand influence category information and type influence category information; Estimated category information is obtained based on the correlation between the overlapping category information and the initially selected category information.

[0012] Optional methods for generating comprehensive warning information include: Retrieve conversion source value, source sensor information and source data information based on conversion data information; Based on the correspondence between the source sensor information and the preset sensor warning type information, the sensor warning type information corresponding to the source sensor information is obtained; Generate single-source warning information based on source warning type information and source data information; Determine whether the conversion source has only one value; If yes, the single-source warning information will be used as comprehensive warning information; If not, generating category benchmark impact information based on the sensor warning category information and vehicle information; Adjusting source data information based on category benchmark impact information to form data adjustment information; Multi-source warning information is generated based on source warning type information and data adjustment information, and the multi-source warning information is used as comprehensive warning information.

[0013] Optionally, a method for generating category baseline impact information includes: Obtaining vehicle deviation information based on a deviation between the vehicle information and preset vehicle reference information; Based on the correspondence between the vehicle deviation information and the preset vehicle deviation impact type information, the vehicle deviation impact type information corresponding to the vehicle deviation information is obtained; Based on the consistency between the sensor warning type information and the vehicle deviation impact type information, consistent type information is obtained; Based on the correspondence between the consistent category information and the preset consistent category impact information, consistent category impact information corresponding to the consistent category information is obtained, and the consistent category impact information is used as category benchmark impact information.

[0014] In a second aspect, the present invention provides an early warning system for a front-view integrated machine controller, which adopts the following technical solutions: A warning system for a front-view integrated machine controller, comprising: Acquisition module, used to obtain real-time detection information and vehicle information; A memory, configured to store an early warning method for a forward-looking integrated machine controller according to any one of the first aspects; The processor loads and executes the program in the memory.

[0015] In a third aspect, the present invention provides an intelligent terminal, which adopts the following technical solution: An intelligent terminal includes a memory and a processor, wherein the memory stores a computer program and can be loaded by the processor to execute an early warning method for a forward-looking integrated machine controller as described in any one of the first aspects.

[0016] In summary, the present invention includes at least one of the following beneficial technical effects: 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 the 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. Conversion data information is then generated based on the detection data deviation information and sensor type information. Comprehensive warning information is generated and output based on the conversion data information and vehicle information. This allows warnings to be issued based on multiple sensors, improving the accuracy of warnings in complex scenarios. 2. Determine whether the sensor type information is the preset camera type information. If it is the camera type information, retrieve the detection source information through the real-time detection information and generate the source use information by combining it with the detection data deviation information. Query the source use information to obtain the use conversion control information and convert and adjust the detection data deviation information to form the converted data information. If it is not the camera type information, query the sensor type information to obtain the single type conversion control information and convert and adjust the detection data deviation information to form the converted data information, thereby improving the accuracy of the obtained converted data information. 3. Obtain source benchmark usage information by detecting source information query, and retrieve benchmark usage individual value through source benchmark usage information. When the benchmark usage individual value is only one, use source benchmark usage information as source usage information. When it is more than one, generate deviation image estimated usage information by detecting data deviation information, and then obtain selected usage information by checking the consistency between deviation image estimated usage information and source benchmark usage information, and use selected usage information as source usage information, thereby improving the accuracy of the obtained source usage information. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of the early warning method of the front-view integrated machine controller according to an embodiment of the present application; Figure 2 is a flow chart of a method for generating conversion data information according to an embodiment of the present application; Figure 3 This is a flow chart of a method for generating source usage information according to an embodiment of the present application; Figure 4 This is a flow chart of a method for generating deviation image estimated usage information according to an embodiment of the present application; Figure 5 This is a flow chart of a method for generating selected deviation type information according to an embodiment of the present application; Figure 6 is a flow chart of a method for generating estimated category information according to an embodiment of the present application; Figure 7 This is a flow chart of a method for generating comprehensive warning information according to an embodiment of the present application; Figure 8 This is a flow chart of a method for generating category benchmark impact information according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] The present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0019] A warning method for a forward-looking integrated machine 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 simultaneously analyzes the inconsistent data information to generate and output comprehensive warning information. This method can simultaneously provide comprehensive warnings for the data obtained by various sensors, thereby improving the warning accuracy in complex scenarios.

[0020] Reference Figure 1 The embodiment of the present invention discloses an early warning method for a front-view integrated machine controller, which includes: Step S100: receiving real-time detection information from each sensor and vehicle information.

[0021] Real-time detection information refers to the detection information collected in real time by various sensors installed on the vehicle. Real-time detection information is collected and transmitted in real time by various sensors. Vehicle information refers to basic parameters such as the vehicle's brand, model, and type. Vehicle information is obtained by querying the vehicle management system.

[0022] Step S101: Retrieve sensor type information and detection data information based on real-time detection information.

[0023] Sensor type information refers to the type of sensor, including forward-facing cameras, forward-facing millimeter-wave radars, DMS cameras, and corner radars. Detection data refers to the data obtained by each sensor. Real-time detection information includes both sensor type information and detection data. These information can be retrieved through real-time detection information for later use.

[0024] Step S102: Generate sensor reference data information based on the sensor type information.

[0025] Sensor baseline data refers to the baseline data corresponding to different sensors during normal testing. Sensor type information is entered into a pre-set sensor type database and then matched to obtain the sensor baseline data, facilitating subsequent use. The sensor type database stores a comparison table of different sensor type information and corresponding sensor baseline data. The sensor type database is obtained through pre-entry.

[0026] Step S103: 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.

[0027] 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 means 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.

[0028] Step S104: generating conversion data information based on the detection data deviation information and the sensor type information.

[0029] The converted data information refers to data information converted to the same data type. By converting the detected data deviation information based on the sensor type information, the converted data information is generated to facilitate subsequent use. The specific steps for generating the converted data information are shown in steps S200 to S206.

[0030] Step S105: Generate comprehensive warning information based on the converted data information and vehicle information, and output the comprehensive warning information.

[0031] Comprehensive warning information refers to warning information about abnormal data collected by various sensors. By analyzing the converted data and vehicle information, comprehensive warning information is generated and output. This allows for comprehensive warnings based on the data collected by various sensors, improving the accuracy of warnings in complex scenarios. For the specific steps for generating comprehensive warning information, refer to steps S700 to S707.

[0032] exist Figure 1 In step S104, in order to further ensure the rationality of the converted data information, it is necessary to further analyze and calculate the converted data information. Figure 2 The steps shown are explained in detail.

[0033] Reference Figure 2 , the method for generating conversion data information includes the following steps: Step S200: Determine whether the sensor type information is the preset camera type information. If yes, execute step S201; if not, execute step S205.

[0034] The camera type information refers to the type information corresponding to the sensor when it is a camera, and the camera type information is obtained by pre-input. By determining whether the sensor type information is the preset camera type information, it is determined whether direct conversion can be performed based on the sensor type.

[0035] Step S201: Retrieve detection source information based on real-time detection information.

[0036] Among them, the detection source information refers to the source information of the detection data, and the real-time detection information includes the detection source information.

[0037] When the sensor type information is the preset camera type information, it means that direct conversion based on the sensor type is not possible at this time. Therefore, the detection source information is retrieved through real-time detection information to facilitate subsequent use.

[0038] Step S202: Generate source usage information based on the detection source information and the detection data deviation information.

[0039] The source usage information refers to the usage information corresponding to the data source. By analyzing the detection source information and the detection data deviation information, the source usage information is generated to facilitate subsequent use. The specific steps for generating the source usage information are shown in steps S300 to S305.

[0040] Step S203: Based on the correspondence between the source usage information and the preset usage conversion control information, the usage conversion control information corresponding to the source usage information is obtained.

[0041] The purpose conversion control information refers to the control information used to convert deviation data based on its intended use. Different source purpose information corresponds to different purpose conversion control information. The purpose conversion control information queries a preset source purpose database for matching and obtains the purpose conversion control information for subsequent use. The source purpose database pre-stores a comparison table of different source purpose information and corresponding purpose conversion control information, and the source purpose database is obtained through pre-input.

[0042] Step S204: converting and adjusting the detection data deviation information based on the usage conversion control information to form converted data information.

[0043] The data type corresponding to the detection data deviation information is converted and adjusted through the use conversion control information, thereby forming conversion data information, thereby improving the accuracy of the obtained conversion data information.

[0044] Step S205: Based on the correspondence between the sensor type information and the preset single-type conversion control information, single-type conversion control information corresponding to the sensor type information is obtained.

[0045] Among them, when the sensor type information is the preset camera type information, it means that direct conversion cannot be performed directly according to the sensor type at this time. Therefore, the preset sensor type database is queried through the sensor type information and then matched to obtain the single type conversion control information. The sensor type database also stores a comparison table of different sensor type information and corresponding single type conversion control information. The sensor type database is obtained after pre-input.

[0046] Step S206: converting and adjusting the detection data deviation information based on the single-type conversion control information to form converted data information.

[0047] The data type corresponding to the detection data deviation information is converted and adjusted through the single-type conversion control information, thereby forming conversion data information, thereby improving the accuracy of the obtained conversion data information.

[0048] exist Figure 2 In step S202, in order to further ensure the rationality of the source use information, it is necessary to further analyze and calculate the source use information. Figure 3 The steps shown are explained in detail.

[0049] Reference Figure 3 , the method for generating source use information includes the following steps: Step S300: 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.

[0050] The source benchmark usage information refers to the benchmark usage information corresponding to the source of the test data. Different test source information corresponds to different source benchmark usage information. By inputting the test source information into a pre-set source benchmark usage database, the source benchmark usage information is matched and obtained, facilitating subsequent use. The source benchmark usage database pre-stores a comparison table of different test source information and corresponding source benchmark usage information, and the source benchmark usage database is obtained after pre-input.

[0051] Step S301: Retrieve the baseline usage value based on the source baseline usage information.

[0052] Among them, the benchmark usage value refers to the benchmark usage value corresponding to the source of the measured data. The number corresponding to the source benchmark usage information is counted, and the counting result is retrieved as the benchmark usage value for subsequent use.

[0053] Step S302: Determine whether the number of reference usage values ​​is only one. If yes, proceed to step S303; if no, proceed to step S304.

[0054] Among them, by determining whether the number of benchmark usage values ​​is only one, it is determined whether further selection and analysis of the source benchmark usage information is required.

[0055] Step S303: Using the source reference usage information as the source usage information.

[0056] Among them, when the value of the benchmark use is only one, it means that there is no need to further select and analyze the source benchmark use information at this time, so the source benchmark use information is used as the source use information, thereby improving the accuracy of the obtained source use information.

[0057] Step S304: generating deviation image estimated usage information based on the detection data deviation information.

[0058] The estimated usage information for the deviation image refers to the usage information obtained by estimating the usage based on the deviation image corresponding to the data deviation. This information is generated by analyzing the detection data deviation information to facilitate subsequent use. The specific steps for generating the estimated usage information for the deviation image are described in steps S400 to S407.

[0059] Step S305: The estimated usage information based on the deviation image is consistent with the source reference usage information to obtain selected usage information, and the selected usage information is used as the source usage information.

[0060] Selected use information refers to use information obtained by selecting a baseline use based on the use estimated by the deviation image. By analyzing the consistency between the estimated use information from the deviation image and the source baseline use information, and selecting uses where the estimated use information from the deviation image and the source baseline use information are consistent as selected use information, and then using the selected use information as the source use information, the accuracy of the obtained source use information can be improved.

[0061] exist Figure 3 In step S304, in order to further ensure the rationality of the deviation image prediction usage information, it is necessary to further analyze and calculate the deviation image prediction usage information. Figure 4 The steps shown are explained in detail.

[0062] Reference Figure 4 ,The method for generating the estimated usage information of the deviation image includes the following steps: Step S400: Retrieving deviation image information based on the detection data deviation information.

[0063] The detection data deviation information includes deviation image information, which refers to image information corresponding to the deviation data. The deviation image information can be retrieved through the detection data deviation information for easy subsequent use.

[0064] Step S401: Retrieve deviation image type information and deviation value based on deviation image information.

[0065] Deviation image information includes deviation image category information and deviation count values. Deviation image category information refers to the category information of the image corresponding to the deviation data, including lane deviation category, traffic sign deviation category, driver status deviation category, external vehicle deviation category, and external pedestrian deviation category. The deviation count value refers to the number of deviation image category information. Deviation image category information is retrieved using the deviation image information, and the retrieved deviation image category information is counted. The count result is used as the deviation count value for subsequent use.

[0066] Step S402: Determine whether the deviation value is only one. If yes, proceed to step S403; if no, proceed to step S404.

[0067] Here, by determining whether the number of deviation values ​​is only one, it is determined whether the usage can be directly estimated based on the type of the deviation image.

[0068] Step S403: Based on the correspondence between the deviation image category information and the preset image category estimated usage information, the image category estimated usage information corresponding to the deviation image category information is obtained, and the image category estimated usage information is used as the deviation image estimated usage information.

[0069] The image type estimated usage information refers to usage information obtained by estimating usage based on the deviation image type. Different deviation image type information corresponds to different image type estimated usage information. When the deviation value is only one, it means that the usage can be directly estimated based on the deviation image type. Therefore, the image type estimated usage information is obtained by inputting the deviation image type information into a preset image type estimated usage database and matching it. The image type estimated usage information is then used as the deviation image estimated usage information, thereby improving the accuracy of the obtained deviation image estimated usage information.

[0070] The image type estimated usage database pre-stores a comparison table of different deviation image type information and corresponding image type estimated usage information, and the image type estimated usage database is obtained through pre-input.

[0071] Step S404: obtaining an image type reference deviation interval corresponding to the deviation image type information based on a correspondence between the deviation image type information and a preset image type reference deviation interval.

[0072] The image type benchmark deviation range refers to the deviation range within which the deviation image type's deviation area is normally allowed to exist. Different deviation image type information corresponds to different image type benchmark deviation ranges. When the deviation value is more than one, it indicates that the intended use cannot be directly estimated based on the deviation image type. Therefore, the image type benchmark deviation range is obtained by inputting the deviation image type information into a preset image type benchmark deviation database and then matching it, thereby facilitating subsequent use. The image type benchmark deviation database pre-stores a comparison table of different deviation image type information and the corresponding image type benchmark deviation ranges. The image type benchmark deviation database is obtained after pre-input.

[0073] Step S405: Retrieving a single-category deviation value from the deviation image information based on the deviation image category information.

[0074] Among them, the single-category deviation value refers to the deviation value corresponding to a single type of deviation image. The single-category deviation value is obtained by retrieving the deviation image corresponding to the deviation image type information from the deviation image information and calculating the area where the deviation image exists, which is convenient for subsequent use.

[0075] Step S406 : generating selected deviation category information based on whether the single category deviation value falls within the image category reference deviation interval.

[0076] The selected deviation type information refers to the type information obtained after selecting the deviation type. By analyzing whether the single-type deviation value falls within the image type benchmark deviation range, the selected deviation type information is generated for subsequent use. The specific steps for generating the selected deviation type information are described in steps S500 to S504.

[0077] Step S407: Based on the correspondence between the selected deviation type information and the preset selected type estimated usage information, the selected type estimated usage information corresponding to the selected deviation type information is obtained, and the selected type estimated usage information is used as the deviation image estimated usage information.

[0078] The selected category estimated use information refers to the use information obtained after estimating the use based on the selected category. Different selected deviation category information corresponds to different selected category estimated use information. The selected category estimated use information is obtained by inputting the selected deviation category information into a preset selected category estimated use database and then matching it. The selected category estimated use information is used as the deviation image estimated use information, thereby improving the accuracy of the obtained deviation image estimated use information. The selected category estimated use database pre-stores a comparison table of different selected deviation category information and the corresponding selected category estimated use information. The selected category estimated use database is obtained after pre-input.

[0079] exist Figure 4 In step S406, in order to further ensure the rationality of the selected deviation type information, it is necessary to further analyze and calculate the selected deviation type information. Figure 5 The steps shown are explained in detail.

[0080] Reference Figure 5 , the method for generating the selected deviation type information includes the following steps: Step S500: Determine whether the single category deviation value falls within the image category reference deviation range. If yes, execute step S501; if not, execute step S503.

[0081] The selection method is determined by determining whether the single category deviation value falls within the image category reference deviation interval.

[0082] Step S501 : using the deviation image category information corresponding to the single category deviation value falling within the image category reference deviation interval as the initially selected category information.

[0083] Among them, when the single category deviation value falls into the image category benchmark deviation interval, the deviation image category information corresponding to the single category deviation value falling into the image category benchmark deviation interval is selected and defined as the initial selected category information for subsequent use.

[0084] Step S502: generating estimated category information based on the initially selected category information and the vehicle information, and using the estimated category information as selected deviation category information.

[0085] Among them, the estimated category information refers to the category information obtained after estimating the initially selected category. By analyzing the initially selected category information and the vehicle information, the estimated category information is generated, and the estimated category information is used as the selected deviation category information to improve the accuracy of the obtained selected deviation category information.

[0086] Step S503: Calculate the deviation between the single-category deviation value and the image category reference deviation interval and use it as the single-category deviation abnormal value.

[0087] Among them, when the single-category deviation value falls into the image category benchmark deviation interval, the deviation value between the single-category deviation value and the image category benchmark deviation interval is calculated, and the calculated deviation value is used as the single-category deviation anomaly value for subsequent use.

[0088] Step S504: sorting the single-category deviation outliers from small to large, and taking the deviation image category information corresponding to the first-ranked single-category deviation outlier as the selected deviation category information.

[0089] Among them, by sorting the single-category deviation outliers from small to large, and selecting the deviation image category information corresponding to the single-category deviation outlier ranked first and using it as the selected deviation category information, the accuracy of the obtained selected deviation category information is improved.

[0090] exist Figure 5 In step S502, in order to further ensure the rationality of the estimated category information, it is necessary to further analyze and calculate the estimated category information. Figure 6 The steps shown are explained in detail.

[0091] Reference Figure 6 ,The method for generating estimated category information includes the following steps: Step S600: Retrieve a category value based on the initially selected category information.

[0092] The category number value refers to the number value corresponding to the category in the initially selected category information. The number of categories in the initially selected category information is counted, and the counting result is retrieved as the category number value for convenience of subsequent use.

[0093] Step S601: Determine whether the number of categories is only one. If yes, proceed to step S602; if no, proceed to step S603.

[0094] Here, by judging whether the number of category values ​​is only one, it is judged whether further analysis and selection of the initially selected category information is required.

[0095] Step S602: Using the initially selected category information as estimated category information.

[0096] Among them, when the number of categories is only one, it means that there is no need to further analyze and select the initially selected category information at this time, so the initially selected category information is used as the estimated category information, thereby improving the accuracy of the obtained estimated category information.

[0097] Step S603: Retrieve vehicle type information and vehicle brand information based on the vehicle information.

[0098] Vehicle type information refers to the vehicle model, including sedan, SUV, and MPV types. Vehicle brand information refers to the vehicle brand, including Toyota, Volkswagen, and BMW. Vehicle information includes both vehicle type and brand information. Retrieving vehicle type and brand information through vehicle information facilitates subsequent use.

[0099] Step S604: Based on the correspondence between the vehicle brand information and the preset brand influence category information, brand influence category information corresponding to the vehicle brand information is obtained.

[0100] 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 and then matching it, facilitating subsequent use. The brand influence category database pre-stores a comparison table of different vehicle brand information and corresponding brand influence category information. The brand influence category database is obtained through pre-input.

[0101] Step S605: Based on the correspondence between the vehicle type information and the preset type impact category information, the type impact category information corresponding to the vehicle type information is obtained.

[0102] The type impact category information refers to the category information corresponding to the impact of vehicle type on the type of image deviation. Different vehicle types correspond to different type impact category information. The type impact category information is obtained by inputting the vehicle type information into a pre-set type impact category database and then matching it, facilitating subsequent use. The type impact category database pre-stores a comparison table of different vehicle types and corresponding type impact category information. The type impact category database is obtained through pre-input.

[0103] Step S606: Selecting overlapping category information based on the overlap between the brand influence category information and the type influence category information.

[0104] Among them, the overlapping category information refers to the impact category information caused by the overlap of vehicle type and vehicle brand. By analyzing the overlap of brand impact category information and type impact category information, the overlapping categories are selected as the overlapping category information for convenience in subsequent use.

[0105] Step S607: Obtaining estimated category information based on the correlation between the overlapping category information and the initially selected category information.

[0106] The associated category information is obtained by inputting the overlapping category information into a preset associated category database and then matching it. The consistency between the associated category information and the initially selected category information is analyzed, and the initially selected category information that is consistent with the associated category information is used as the estimated category information, thereby improving the accuracy of the obtained estimated category information. The associated category database pre-stores a comparison table of different overlapping category information and corresponding associated category information, and the associated category database is obtained after pre-input.

[0107] exist Figure 1 In step S105, in order to further ensure the rationality of the comprehensive warning information, it is necessary to further analyze and calculate the comprehensive warning information. Figure 7 The steps shown are explained in detail.

[0108] Reference Figure 7 ,The method for generating comprehensive early warning information includes the following steps: Step S700: Retrieve conversion source values, source sensor information, and source data information based on the conversion data information.

[0109] The conversion source value refers to the value corresponding to the source of the converted data, the source sensor information refers to the sensor corresponding to the source of the converted data, and the source data information refers to the data information corresponding to a single source after conversion. The conversion data information includes the conversion source value, source sensor information, and source data information. The conversion data information allows you to retrieve the conversion source value, source sensor information, and source data information for subsequent use.

[0110] Step S701: Based on the correspondence between the source sensor information and the preset sensor warning type information, the sensor warning type information corresponding to the source sensor information is obtained.

[0111] Sensor warning type information refers to the warning type information corresponding to a sensor warning. Different source sensor information corresponds to different sensor warning types. The sensor warning type information is obtained by inputting the source sensor information into a pre-set sensor warning type database and then matching it, facilitating subsequent use. The sensor warning type database pre-stores different source sensor information and corresponding sensor warning type information. The sensor warning type database is obtained through pre-input.

[0112] Step S702: Generate single-source warning information based on the source warning type information and the source data information.

[0113] Among them, single-source warning information refers to warning information that warns of deviation data from a single source. By combining the source warning type information with the source data information, warning information containing specific deviation data and warning types is formed and used as single-source warning information, which is convenient for subsequent use.

[0114] Step S703: Determine whether the conversion source value is only one. If yes, execute step S704; if no, execute step S705.

[0115] Among them, by judging whether there is only one conversion source value, it is determined whether further analysis of the single-source warning information is needed.

[0116] Step S704: Use the single-source warning information as comprehensive warning information.

[0117] Among them, when the conversion source value is only one, it means that there is no need to further analyze the single-source warning information at this time, so the single-source warning information is used as comprehensive warning information.

[0118] Step S705: Generate category benchmark impact information based on the sensor warning category information and vehicle information.

[0119] The category baseline impact information refers to the baseline impact information generated by the data type conversion required for the warning type. When there is only one conversion source value, further analysis of the single-source warning information is not required. Therefore, the category baseline impact information is generated by analyzing the sensor warning type information and vehicle information for subsequent use. The specific method for generating the category baseline impact information is described in steps S800 to S803.

[0120] Step S706: adjusting the source data information based on the category benchmark impact information to form data adjustment information.

[0121] Among them, the data type corresponding to the source data information is adjusted by the type benchmark impact information, thereby forming data adjustment information to facilitate subsequent use.

[0122] Step S707: Generate multi-source warning information based on the source warning type information and the data adjustment information, and use the multi-source warning information as comprehensive warning information.

[0123] Among them, by combining the source warning type information with the data adjustment information, warning information containing specific deviation data and warning types is formed and used as multi-source warning information, and then the multi-source warning information is used as comprehensive warning information to improve the accuracy of the obtained comprehensive warning information.

[0124] exist Figure 7 In step S705, in order to further ensure the rationality of the category-based impact information, it is necessary to further analyze and calculate the category-based impact information separately. Figure 8 The steps shown are explained in detail.

[0125] Reference Figure 8 ,The method for generating category benchmark impact information includes the following steps: Step S800: Obtaining vehicle deviation information based on a deviation between the vehicle information and preset vehicle reference information.

[0126] Vehicle baseline information refers to the vehicle's technical parameters under normal conditions, including the vehicle's engine output power and displacement. This information is obtained through pre-input. Vehicle deviation information refers to deviations in the vehicle's technical parameters. The deviation between the vehicle information and the preset vehicle baseline information is analyzed and used as vehicle deviation information for subsequent use.

[0127] Step S801: Based on the correspondence between the vehicle deviation information and the preset vehicle deviation impact type information, vehicle deviation impact type information corresponding to the vehicle deviation information is obtained.

[0128] Vehicle deviation impact category information refers to the type of impact caused by a vehicle's technical parameter deviation requiring a warning. Different vehicle deviation information corresponds to different types of vehicle deviation impact information. The vehicle deviation impact category information is obtained by inputting the vehicle deviation information into a pre-set vehicle deviation impact category database and then matching it, facilitating subsequent use. The vehicle deviation impact category database stores a comparison table of different vehicle deviation information and corresponding vehicle deviation impact category information. Vehicle deviation impact category information is obtained through pre-input.

[0129] Step S802: Based on the consistency between the sensor warning type information and the vehicle deviation impact type information, consistent type information is obtained.

[0130] Among them, by analyzing the consistency between the sensor warning type information and the vehicle deviation impact type information, the types of the sensor warning type information and the vehicle deviation impact type information are regarded as consistent type information, so as to facilitate subsequent use.

[0131] Step S803: Based on the correspondence between the consistent category information and the preset consistent category impact information, consistent category impact information corresponding to the consistent category information is obtained, and the consistent category impact information is used as category benchmark impact information.

[0132] Consistent category impact information refers to the impact of a consistent category on the required data type. Different consistent category information corresponds to different consistent category impact information. Consistent category impact information is obtained by inputting the consistent category information into a preset consistent category impact database and then matching it. The consistent category impact information is then used as category baseline impact information, thereby improving the accuracy of the obtained category baseline impact information. The consistent category impact database pre-stores a comparison table of different consistent category information and corresponding consistent category impact information. The consistent category impact database is obtained through pre-input.

[0133] Based on the same inventive concept, an embodiment of the present invention provides an early warning system for a front-view integrated machine controller, comprising: Acquisition module, used to obtain real-time detection information and vehicle information; A memory for storing the above-mentioned early warning method of the forward-looking integrated machine controller; The processor loads and executes the program in the memory.

[0134] Based on the same inventive concept, an embodiment of the present invention provides an intelligent terminal including a memory and a processor, wherein the memory stores a computer program and can be loaded and executed by the processor as the above-mentioned warning method of a forward-looking integrated machine controller.

[0135] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the division of the above-mentioned functional modules is only used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working processes of the above-mentioned systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0136] 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 embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart 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 front-view integrated machine controller, characterized in that: include: Receive real-time detection information and vehicle information from each sensor; Retrieve sensor type information and detection data information based on real-time detection information; generating sensor benchmark data information based on the 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; generating conversion data information based on the detection data deviation information and the sensor type information; Generate comprehensive warning information based on the converted data information and vehicle information, and output the comprehensive warning information.

2. A warning method for a front-view integrated machine controller according to claim 1, characterized in that: The method for generating the conversion data information includes: Determine whether the sensor type information is the preset camera type information; If yes, retrieve the detection source information based on the real-time detection information; Generate source usage information based on detection source information and detection data deviation information; Based on the correspondence between the source usage information and the preset usage conversion control information, the usage conversion control information corresponding to the source usage information is obtained; converting and adjusting the detection data deviation information based on the purpose conversion control information to form conversion data information; If not, obtaining the single-type conversion control information corresponding to the sensor type information based on the correspondence between the sensor type information and the preset single-type conversion control information; The detection data deviation information is converted and adjusted based on the single-type conversion control information to form conversion data information.

3. The early warning method of the front-view integrated machine controller according to claim 2 is characterized in that: Methods for generating source use 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 the benchmark usage value based on the source benchmark usage information; Determine whether the number of base usages is only one; If yes, the source base use information is used as the source use information; If not, generating deviation image estimated usage information based on the detection data deviation information; Based on the consistency between the estimated usage information of the deviation image and the source benchmark usage information, the selected usage information is obtained, and the selected usage information is used as the source usage information.

4. The early warning method of the front-view integrated machine controller according to claim 3 is characterized in that: The method for generating the deviation image estimated usage information includes: Retrieving deviation image information based on detection data deviation information; Retrieving deviation image type information and deviation value based on deviation image information; Determine whether the deviation value is only one; If yes, then based on the correspondence between the deviation image type information and the preset image type estimated usage information, obtain the image type estimated usage information corresponding to the deviation image type information, and use the image type estimated usage information as the deviation image estimated usage information; If not, then based on the correspondence between the deviation image type information and the preset image type reference deviation interval, the image type reference deviation interval corresponding to the deviation image type information is obtained; Retrieving a single-category deviation value from the deviation image information based on the deviation image category information; Based on whether the single category deviation value falls within the image category benchmark deviation range, the selected deviation category information is generated; Based on the correspondence between the selected deviation type information and the preset selected type estimated usage information, the selected type estimated usage information corresponding to the selected deviation type information is obtained, and the selected type estimated usage information is used as the deviation image estimated usage information.

5. The early warning method of the front-view integrated machine controller according to claim 4 is characterized in that: The method for generating the selected deviation type information includes: Determining whether the single category deviation value falls within the image category benchmark deviation interval; If yes, the deviation image category information corresponding to the single category deviation value falling within the image category reference deviation interval is used as the initial selected category information; Generate estimated category information based on the initial selected category information and the vehicle information, and use the estimated category information as the selected deviation category information; If not, the deviation between the single category deviation value and the image category benchmark deviation interval is calculated and used as the single category deviation outlier; The single-category deviation outliers are sorted from small to large, and the deviation image category information corresponding to the single-category deviation outlier that ranks first is used as the selected deviation category information.

6. The early warning method of the front-view integrated machine controller according to claim 5, characterized in that: Methods for generating estimated species information include: Retrieve the number of categories based on the initially selected category information; Determine whether the number of types is only one; If yes, the initially selected category information is used as the estimated category information; If not, retrieve vehicle type information and vehicle brand information based on the vehicle information; Based on the correspondence between the vehicle brand information and the preset brand influence category information, the brand influence category information corresponding to the vehicle brand information is obtained; Based on the correspondence between the vehicle type information and the preset type impact category information, the type impact category information corresponding to the vehicle type information is obtained; Selecting overlapping category information based on the overlap between brand influence category information and type influence category information; Estimated category information is obtained based on the correlation between the overlapping category information and the initially selected category information.

7. The early warning method of the front-view integrated machine controller according to claim 1 is characterized in that: Methods for generating comprehensive early warning information include: Retrieve conversion source value, source sensor information and source data information based on conversion data information; Based on the correspondence between the source sensor information and the preset sensor warning type information, the sensor warning type information corresponding to the source sensor information is obtained; Generate single-source warning information based on source warning type information and source data information; Determine if there is only one value in the conversion source; If yes, the single-source warning information will be used as comprehensive warning information; If not, generating category benchmark impact information based on the sensor warning category information and vehicle information; Adjusting source data information based on category benchmark impact information to form data adjustment information; Multi-source warning information is generated based on source warning type information and data adjustment information, and the multi-source warning information is used as comprehensive warning information.

8. The early warning method of the front-view integrated machine controller according to claim 7, characterized in that: Methods for generating category-based impact information include: Obtaining vehicle deviation information based on a deviation between the vehicle information and preset vehicle reference information; Based on the correspondence between the vehicle deviation information and the preset vehicle deviation impact type information, the vehicle deviation impact type information corresponding to the vehicle deviation information is obtained; Based on the consistency between the sensor warning type information and the vehicle deviation impact type information, consistent type information is obtained; Based on the correspondence between the consistent category information and the preset consistent category impact information, consistent category impact information corresponding to the consistent category information is obtained, and the consistent category impact information is used as category benchmark impact information.

9. A warning system for a front-view integrated machine controller, characterized in that: include: Acquisition module, used to obtain real-time detection information and vehicle information; A memory, configured to store a warning method for a forward-looking integrated machine controller according to any one of claims 1 to 8; The processor loads and executes the program in the memory.

10. An intelligent terminal, characterized in that: The invention comprises a memory and a processor, wherein the memory stores a computer program and can be loaded by the processor to execute a warning method for a forward-looking integrated machine controller according to any one of claims 1 to 8.

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