Intelligent cockpit linkage control method and system for child pickup scene

Abstract

The application discloses an intelligent cockpit linkage control method for a child pick-up scene and relates to the technical field of intelligent control of automobiles. The method comprises the following steps: analyzing positioning information at a preset detection time point, fusing pressure sensing and infrared image recognition of a child state, combining time and position characteristics to determine a pick-up scene type; based on the scene type and a historical child portrait, matching target playing resources and pushing, and generating linkage control reminding information according to the cockpit locking state. The application can accurately determine whether the vehicle is in a child pick-up scene, and based on the scene recognition information, the multimedia terminal playing control and the intelligent linkage of the cockpit equipment are carried out, the riding safety is improved, and the cockpit intelligent control in the child pick-up scene can be realized.

Description

Technical Field

[0001] This invention relates to the field of automotive intelligent control technology, and in particular to an intelligent cockpit linkage control method and system for scenarios involving picking up and dropping off children. Background Technology

[0002] With the deep integration of the automotive industry and artificial intelligence technology, the modern intelligent cockpit has gradually become the core interactive space of vehicles. Currently, mainstream intelligent vehicles are generally equipped with high-precision navigation and positioning systems, enabling users to save and plan routes to frequently visited locations such as home and school. However, existing intelligent cockpit systems cannot accurately identify and control high-frequency scenarios; especially in scenarios involving children picking up and dropping off at school, due to the strong time regularity, relatively fixed routes, and special requirements for safety and comfort, existing technologies often struggle to achieve fully automated and accurate responses. Therefore, existing technologies have the problem of being unable to achieve intelligent cockpit control in high-frequency scenarios. Summary of the Invention

[0003] This invention provides a smart cockpit linkage control method and system for child pick-up and drop-off scenarios, aiming to solve the problem that existing technologies cannot perform smart cockpit linkage control for high-frequency scenarios.

[0004] In a first aspect, embodiments of the present invention provide an intelligent cockpit linkage control method for child pick-up and drop-off scenarios, wherein the method is applied to a control terminal, the control terminal being communicatively connected with a positioning component, a pressure sensor array, an infrared camera, a multimedia terminal, and a cockpit linkage control component to achieve data information transmission, the pressure sensor array being disposed within the seat cushion of the vehicle seat, and the infrared camera being disposed on the side facing the seat, the method comprising: If the preset detection time point is reached, the positioning information collected by the positioning component is parsed according to the preset positioning parsing rules to obtain the corresponding positioning parsing information; The pressure sensing information and infrared image are fused and recognized according to a preset fusion recognition strategy to obtain the corresponding fusion recognition result; the pressure sensing information is collected by the pressure sensor array and the infrared image is collected by the infrared camera. The location parsing information and the fusion recognition result are identified according to the pick-up and drop-off scene recognition rules to obtain the corresponding scene recognition information; Based on the pre-stored historical child portraits and the scene recognition information, the corresponding target playback resources are matched and obtained from the pre-set resource library and pushed to the multimedia terminal; Based on the preset reminder strategy and the scene recognition information, a joint control reminder message corresponding to the lock status information is generated; the lock status information is collected by the cockpit joint control component.

[0005] Secondly, embodiments of the present invention also provide an intelligent cockpit linkage control system for child pick-up and drop-off scenarios, wherein the system is configured on a control terminal, the control terminal is communicatively connected with a positioning component, a pressure sensor array, an infrared camera, a multimedia terminal, and a cockpit linkage control component to achieve data information transmission, the pressure sensor array is disposed within the seat cushion of the vehicle seat, the infrared camera is disposed on the side facing the seat, and the system is used to execute the intelligent cockpit linkage control method for child pick-up and drop-off scenarios as described in the first aspect above, the system comprising: The positioning information parsing unit is used to parse the positioning information collected by the positioning component according to the preset positioning parsing rules when a preset detection time point is reached, so as to obtain the corresponding positioning parsing information. The fusion recognition unit is used to perform fusion recognition on pressure sensing information and infrared image according to a preset fusion recognition strategy to obtain the corresponding fusion recognition result; the pressure sensing information is collected by the pressure sensor array and the infrared image is collected by the infrared camera. The scene recognition information acquisition unit is used to identify the positioning parsing information and the fusion recognition result according to the pick-up and drop-off scene recognition rules to obtain the corresponding scene recognition information; The target playback resource push unit is used to match and obtain the corresponding target playback resource from the preset resource library based on the pre-stored historical child portrait and the scene recognition information, and push it to the multimedia terminal. The joint control reminder information generation unit is used to generate joint control reminder information corresponding to the lockout status information based on the preset reminder strategy and the scene recognition information; the lockout status information is collected by the cockpit joint control component.

[0006] Thirdly, embodiments of the present invention also provide a computer device, wherein the device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When the processor executes the program stored in the memory, it implements the steps of the intelligent cockpit linkage control method for child pick-up and drop-off scenarios described in the first aspect above.

[0007] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the intelligent cockpit linkage control method for child pick-up and drop-off scenarios as described in the first aspect above.

[0008] This invention provides an intelligent cockpit linkage control method for child pick-up and drop-off scenarios. The method includes: parsing positioning information at a preset detection time point, integrating pressure sensing and infrared image recognition to identify the child's state, and determining the pick-up and drop-off scenario type based on time and location features; matching and pushing target playback resources based on the scenario type and historical child profiles, and simultaneously generating linkage reminder information based on the cockpit lock status. This application can accurately determine whether the vehicle is in a child pick-up and drop-off scenario, and perform multimedia terminal playback control and intelligent linkage of cockpit equipment based on scenario recognition information, improving passenger safety and enabling intelligent cockpit control in child pick-up and drop-off scenarios. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A flowchart illustrating the intelligent cockpit linkage control method for child pick-up and drop-off scenarios provided in this embodiment of the invention; Figure 2 This is a schematic diagram illustrating an application scenario of the intelligent cockpit linkage control method for picking up and dropping off children provided in an embodiment of the present invention. Figure 3 This is a schematic block diagram of an intelligent cockpit linkage control system for child pick-up and drop-off scenarios provided in an embodiment of the present invention. Figure 4 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0013] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0014] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0015] This invention application provides an intelligent cockpit linkage control method for child pick-up and drop-off scenarios. This method is applied to a control terminal 10, which executes a stored software program to implement the aforementioned intelligent cockpit linkage control method for child pick-up and drop-off scenarios. Figure 2 As shown, the control terminal 10 communicates with the positioning component 20, pressure sensor array 30, infrared camera 40, multimedia terminal 50, and cockpit control component 60 to transmit data. The positioning component 20 can be integrated with the control terminal 10. The positioning component 20 can receive satellite signals and obtain positioning information. The positioning component 20 can be a Beidou positioning terminal or a GPS positioning terminal. The pressure sensor array 30 is installed inside the seat cushion of the vehicle seat 11, and the infrared camera 40 is installed on the side facing the seat 11. The control terminal 10 is a terminal component used for data analysis and component control, such as a processor (MCU) configured in the vehicle's infotainment system. The pressure sensor array 30 is used to sense the pressure generated by the person sitting above the seat 11, and the infrared camera 40 is used to collect infrared images of the person sitting above the seat. The multimedia terminal 50 is installed on the side facing the seat 11 and integrates a display screen, speaker, and microphone. The multimedia terminal 50 can be used to play audio / video to the person sitting above the seat 11 and can also acquire the voice of the person sitting above the seat 11 through the speaker, thereby enabling intelligent interaction. The cockpit control module 60 is a control component installed inside the vehicle for monitoring user control, door lock status, window status, and seat belt status. For example... Figure 1 As shown, the method includes steps S110 to S150.

[0016] S110. If the preset detection time point is reached, the positioning information collected by the positioning component is parsed according to the preset positioning parsing rules to obtain the corresponding positioning parsing information.

[0017] The method provided in this application is applied to a control terminal, which serves as the core processing unit of the intelligent cockpit system, responsible for coordinating the acquisition and processing of data from various sensors and the issuance of final control commands. At the physical architecture level, the control terminal establishes communication connections with positioning components, pressure sensor arrays, infrared cameras, multimedia terminals, and cockpit control components to achieve data transmission. Specifically, the control terminal can connect to the aforementioned components via an onboard bus (such as a CAN bus or onboard Ethernet).

[0018] The preset detection time point can refer to the time base for the system to trigger the scene recognition process. It can be determined by comparing the system clock with the default detection time point. For example, the detection time point can be set to a fixed time period every day except statutory holidays (such as 7:00-8:00 am and 4:00-5:30 pm). When the system clock reaches the preset detection time point, the control terminal starts the current detection process.

[0019] The positioning component collects real-time geographic location information of vehicles, which can then be used for subsequent analysis and judgment. Positioning resolution information is semantic information obtained by processing raw positioning data, used to characterize the vehicle's current geographic location or its association with specific locations. Pre-defined positioning resolution rules define how to convert latitude and longitude coordinates into business-meaningful location descriptions. The construction of these rules is completed during the system initialization phase: First, historical travel trajectory data of users is collected, extracting the coordinate ranges of frequently visited locations during child pick-up and drop-off (such as home and school locations), as well as vehicle movement positioning information during the vehicle's journey between home and school (this information is periodically acquired and recorded by the vehicle during its movement); finally, standardized data containing coordinate ranges and vehicle movement positioning information is generated as the corresponding historical pick-up and drop-off trajectory, and the corresponding positioning resolution rules are configured. This positioning resolution rule file can be manually updated and expanded by users through the vehicle's infotainment system or a mobile app.

[0020] During operation, the control terminal reads the location information uploaded by the positioning component, performs comparison and analysis using preset positioning parsing rules, and obtains the corresponding positioning parsing information.

[0021] In a specific embodiment, step S110 includes the following sub-steps: determining whether the location deviation between the target location corresponding to the location information and the historical pick-up and drop-off trajectory in the location parsing rule is within the deviation range in the location parsing rule; if the location deviation is within the deviation range, obtaining matching location parsing information; if the location deviation is not within the deviation range, comparing the location information with the historical pick-up and drop-off trajectory in the location parsing rule to obtain the corresponding trajectory overlap; determining whether the overlap is greater than the overlap threshold set in the location parsing rule to obtain whether the location parsing information is matched.

[0022] Position deviation is used to characterize the spatial deviation between the vehicle's target location and the pick-up / drop-off coordinate range in the historical pick-up / drop-off trajectory. Based on the target location corresponding to the positioning information, the latitude and longitude coordinates of the target location are determined. The target location is also the user's navigation endpoint. The planar geometric distance between this target location and each pick-up / drop-off coordinate range in the historical trajectory is calculated, and this distance is used as the position deviation. The deviation range in the preset positioning resolution rules is a tolerance interval comprehensively set based on the normal error fluctuation range of the Global Positioning System (GPS) or BeiDou Navigation Satellite System (BDS) during vehicle operation, as well as the space occupied by building areas. This deviation range can be set as a fixed distance threshold, such as 300 meters or 500 meters, or it can be dynamically adjusted according to the building area level (community size level or school level). For example, a smaller deviation range is set for pick-up / drop-off coordinate ranges corresponding to kindergarten areas, and a larger deviation range is set for pick-up / drop-off coordinate ranges corresponding to middle school areas. The calculated actual positioning deviation value is compared with the set deviation range to determine whether the target positioning coincides with the pick-up and drop-off coordinate range in the historical pick-up and drop-off trajectory in physical space.

[0023] When the location deviation between the target location and any pick-up / drop-off coordinate range is within the aforementioned deviation range, it means that the vehicle's destination is consistent with the home or school location in the historical pick-up / drop-off trajectory. Under this condition, it is determined that the road segment corresponding to the current location information matches the valid path of the pick-up / drop-off scenario, thereby generating matching location parsing information.

[0024] If the location deviation is determined to be outside the deviation range, it indicates that the vehicle's destination does not match the home or school location in the historical pick-up and drop-off trajectory. Further trajectory comparison analysis can then be performed. Specifically, based on the location information, a matching trajectory is extracted from the vehicle's mobile location information of the historical pick-up and drop-off trajectory. For example, if the location information is at point A on the route from home to school, the location coordinates between home and point A are extracted from the vehicle's mobile location information as the comparison trajectory. The overlap degree between the actual driving trajectory reflected by the location information and the comparison trajectory is then calculated. Trajectory overlap degree is a quantitative indicator that measures the degree of consistency between two paths in spatial direction. For example, the Fréchet Distance algorithm or the Hausdorff Distance algorithm can be used to calculate the geometric similarity between the sequence of location points within the current time window and the corresponding sequence of location coordinates in the comparison trajectory to obtain the overlap degree value. The value range of this trajectory overlap degree can be normalized to between 0 and 1; the closer the value is to 1, the more consistent the directions of the two trajectories are.

[0025] After obtaining the trajectory overlap, the system further determines whether this overlap exceeds the overlap threshold set in the positioning and analysis rules. The overlap threshold is the dividing line between valid pick-up / drop-off detours and non-pick-up / drop-off trips, and its setting is based on the statistical distribution of overlap in various detour scenarios in historical pick-up / drop-off behavior. For example, this overlap threshold can be set to values ​​such as 0.7 or 0.8, meaning that only when the similarity between the current trajectory and the historical trajectory exceeds this proportion is the vehicle considered to still be performing a pick-up / drop-off task. If the overlap exceeds this threshold, it is determined that although there is a positional deviation, the overall driving trend of the vehicle still conforms to the pick-up / drop-off pattern, thus obtaining matching positioning and analysis information; conversely, if the overlap does not exceed this threshold, it is determined that the vehicle has left the pick-up / drop-off scenario, generating mismatched positioning and analysis information. Through this hierarchical judgment logic, the system can accommodate the path uncertainty in actual driving to the greatest extent while ensuring recognition accuracy.

[0026] By employing the dual verification mechanism based on positioning deviation and trajectory overlap, the simple point-to-point location comparison is expanded into a comprehensive judgment logic that includes path trend analysis. This approach enables the positioning resolution process to adapt to complex road conditions such as GPS signal drift and road construction detours, transforming ambiguous real-time location data into a high-confidence scenario matching basis. This effectively solves the problem of misjudgment or omission of pick-up and drop-off scenarios caused by fluctuations in positioning data, improving the accuracy of scene recognition in complex travel environments for the intelligent cockpit.

[0027] S120. The pressure sensing information and infrared image are fused and recognized according to the preset fusion recognition strategy to obtain the corresponding fusion recognition result.

[0028] The pressure sensing information is collected by the pressure sensor array, and the infrared image is collected by the infrared camera. The pressure sensor array is installed inside the vehicle seat cushion and consists of multiple independent pressure-sensitive sensing units distributed along the surface of the cushion, interconnected by a flexible circuit board. It converts pressure deformation into electrical signals. The pressure sensing information is a raw data sequence reflecting the weight distribution and shape changes of the object supported by the seat, such as a matrix containing the coordinate positions of each sensing unit and their corresponding pressure values. The infrared camera is located on the side facing the seat and uses an uncooled infrared focal plane array detector to convert the infrared radiation energy emitted by the object into a visualized image signal. The infrared image is grayscale or pseudo-color image data reflecting the temperature field distribution in the seat area.

[0029] The fusion recognition strategy is a set of algorithmic logics pre-configured in the control terminal for fusing and analyzing pressure sensing information and infrared images to determine the member type. It includes feature items, recognition models, and judgment rules.

[0030] In a specific embodiment, step S120 includes the following sub-steps: extracting corresponding pressure sensing feature parameters and infrared image feature parameters from the pressure sensing information and the infrared image respectively according to the feature extraction rules in the fusion recognition strategy; inputting the pressure sensing feature parameters and infrared image feature parameters into the recognition model in the fusion recognition strategy to obtain the corresponding recognition probability and confidence level; judging the recognition probability and confidence level according to the judgment rules in the fusion recognition strategy to obtain the corresponding fusion recognition result.

[0031] During execution, the control terminal periodically reads the voltage signals reported by the pressure sensor array via the vehicle bus, and generates pressure sensing information through analog-to-digital conversion and noise reduction processing; simultaneously, it acquires real-time image frames output by the infrared camera through the video acquisition interface. After loading the fusion recognition strategy, pressure sensing feature parameters such as total pressure, pressure coverage area, and pressure center are extracted from the pressure sensing information, and infrared image feature parameters such as head-to-body ratio, shoulder width, and sitting height are extracted from the infrared image. The extracted multimodal features (including pressure sensing feature parameters and infrared image feature parameters) are input into the recognition model to analyze and obtain the recognition probability and confidence level. Before using the input recognition model, it can be trained using training data.

[0032] Specifically, the feature extraction rules include multiple feature terms; corresponding pressure sensing feature parameters are extracted from the pressure sensing information. The obtained pressure sensing feature parameters include the total pressure of the force-bearing area on the seat surface, the pressure coverage area, and the pressure center. Among them, the total pressure is the sum of the pressure values ​​sensed by all sensing units in the pressure sensor array, and the pressure coverage area is the coverage area of ​​all sensing units in the pressure sensor array whose pressure value is greater than the basic pressure value (e.g., 10N) (number of sensing units with pressure values ​​greater than the basic pressure value × unit coverage area). The coordinate position corresponding to the sensing unit with the highest pressure value in the pressure sensor array is obtained as the pressure center.

[0033] Infrared image feature parameters are extracted from the infrared image according to feature extraction rules. The pixel value of each pixel in the infrared image is strongly correlated with the temperature value. Based on the correlation between pixel value and temperature value, the region with a temperature resistivity greater than 35℃ is cropped from the infrared image as the target human image region. The target human image region is segmented and features are extracted to obtain the corresponding infrared image feature parameters. Specifically, according to the feature extraction rules, the widest position in the target human image region is obtained as the shoulder width, and the narrowest position (neck) in the target human image region is obtained for image segmentation. The upper part is used as the head region image, and the lower part is used as the body region image. The height of the body region image is obtained as the sitting height, and the ratio between the height of the head region image and the height of the body region image is obtained as the head-to-body ratio.

[0034] The pre-trained recognition model is used to perform probability analysis and confidence assessment on the pressure-sensing feature parameters and infrared image feature parameters extracted in the above steps. For example, a dual-stream convolutional neural network can be used as the recognition model to process the input pressure-sensing feature parameters and infrared image feature parameters separately, and feature fusion is performed in a fully connected layer to finally output the recognition probability and confidence score. The recognition model is not a simple logical judgment module, but a nonlinear mapping model learned based on historical sample data. The recognition model consists of an input layer, an intermediate layer, and an output layer. The input layer is used to input the fusion parameters, the intermediate layer is used to perform correlation analysis on the fusion parameters, and the output layer is used to output the recognition probability and confidence score. The recognition model receives the pressure-sensing feature parameters and infrared image feature parameters and outputs a probabilistic judgment about whether it is a child. The recognition probability represents the likelihood that the model will classify the current input as a child, and its value ranges from 0 to 1. The confidence score reflects the certainty or credibility of the model's judgment result and is used to measure the degree of matching between the model's input data and the prior distribution of the samples (prior distribution information obtained based on the training data).

[0035] The recognition probability and confidence level are judged according to the judgment rules. If the confidence level exceeds the confidence threshold (e.g., 85%) in the judgment rules, and the recognition probability exceeds the probability threshold (e.g., 80%) in the judgment rules, the fusion recognition result is a child. If the recognition probability does not exceed the probability threshold, the fusion recognition result is not a child; if the confidence level does not exceed the confidence threshold, the fusion recognition result is unrecognizable. The control terminal can periodically acquire the corresponding sensing values ​​and continuously output the fusion recognition result, such as setting the recognition period to 30 seconds.

[0036] S130. The positioning parsing information and the fusion recognition result are identified according to the pick-up and drop-off scene recognition rules to obtain the corresponding scene recognition information.

[0037] Scene recognition information is used to characterize the specific child pick-up and drop-off status of the vehicle, such as being on the way to or from school, or not in a pick-up / drop-off scenario. Pick-up / drop-off scene recognition rules are logical judgment criteria used to accurately determine the current scene by integrating current time, location parsing information, and fusion recognition results. The construction of these pick-up / drop-off scene recognition rules is completed during the configuration phase before the system goes live.

[0038] In a specific embodiment, step S130 includes the following sub-steps: performing probability analysis on the current time, the location parsing information, and the fusion recognition result according to the probability analysis model in the pick-up and drop-off scene recognition rules to obtain corresponding classification probability analysis information; and performing scene type parsing on the classification probability analysis information according to the scene parsing model in the pick-up and drop-off scene recognition rules to determine the corresponding scene type as scene recognition information.

[0039] The probabilistic analysis model is configured to comprehensively quantify and evaluate multi-dimensional spatiotemporal and state features. The current time, location parsing information, and fusion recognition results are used to construct the model's input feature vector, representing time features, geographical location features, and the child's state inside the vehicle, respectively. The model dynamically assigns values ​​to each feature dimension using preset weighting coefficients, calculating the probability distribution of the vehicle's current position in different preset pick-up and drop-off scenarios (such as dropping off / picking up school, or regular travel), thereby outputting the probability values ​​for each candidate scenario.

[0040] In practical implementation, the probability analysis model can adopt a multi-dimensional weighted scoring mechanism. The probability analysis model can use the school drop-off probability sub-model, school pick-up probability sub-model and general travel probability sub-model in the probability analysis model to analyze the current time, location parsing information and fusion recognition results, and obtain three sets of comprehensive probability scores as the corresponding classification probability analysis information.

[0041] The time matching degree, route matching degree, and passenger status matching degree are multiplied by their respective weight coefficients set in their respective sub-models and summed to obtain the corresponding comprehensive probability score. The current time is used to determine the corresponding time matching degree. If the current time is within the drop-off period, the drop-off time matching degree is set to 1, and the pick-up time matching degree is set to 0; if the current time is within the pick-up period, the pick-up time matching degree is set to 1, and the drop-off time matching degree is set to 0. If the current time deviates from the closest time period, the corresponding matching degree is obtained based on the deviation time. For example, if the current time is 8:12, the closest time period is the drop-off period of 7:00-8:00; if the deviation time is 12 minutes, the drop-off time matching degree is set to 1-r×t / t0, and the pick-up time matching degree is set to 0. Here, r is the matching adjustment coefficient (e.g., set to 0.5), t is the deviation time, and t0 is the deviation from the baseline time (e.g., set to 60 minutes). If the location parsing information matches, the corresponding path matching degree is determined to be 1; if the location parsing information does not match, the corresponding path matching degree is determined to be 0. If the fusion recognition result is a child, the occupant status matching degree is determined to be 1; if the fusion recognition result is not a child, the occupant status matching degree is determined to be 0; if the fusion recognition result is unrecognizable, the occupant status matching degree is determined to be 0.5.

[0042] The probability sub-model for transporting students to school can be represented as follows: Comprehensive probability score P = W1 × time matching degree + W2 × route matching degree + W3 × passenger status matching degree. W1, W2, and W3 are preset dynamic weight coefficients in the probability sub-model for transporting students to school. The system can automatically adjust them according to the user's historical travel frequency (for example, if the same scenario is triggered for 5 consecutive working days, the weight coefficient W1 for the corresponding period will gradually increase). The initial weight coefficients are W1 = 0.6, W2 = 0.2, and W3 = 0.2, respectively.

[0043] Based on the obtained classification probability analysis information, the corresponding scene type is analyzed to determine the scene type as the scene identification information. To ensure the stability of the identification results, if the comprehensive probability score of a certain scene type is consistently higher than a preset threshold (e.g., 80 points) and remains stable within a preset time window (e.g., 3 minutes), the scene analysis model determines that the scene type is valid. For example, when the probability analysis results show that the probability of the school delivery scene is 90 points and the state is stable, the scene analysis model outputs the school delivery scene as the scene identification information. This dual verification mechanism based on probability threshold and temporal stability can effectively filter out occasional false triggering signals, ensuring that the output scene identification information has high accuracy and robustness, thus providing a reliable logical starting point for subsequent cabin resource push and joint control reminders.

[0044] Through the above steps, the probabilistic analysis model's quantitative evaluation of multi-dimensional features and the threshold determination mechanism of the scene analysis model work together to achieve accurate capture of child pick-up and drop-off scenarios. This technical process, from multi-dimensional feature input to probability calculation and then to deterministic scene output, enables the system to maintain high-precision scene recognition capabilities even in complex urban traffic environments and changing user habits, laying a solid data foundation for the automated linkage control of intelligent cockpits.

[0045] S140. Based on the pre-stored historical child portraits and the scene recognition information, the corresponding target playback resources are matched and obtained from the preset resource library and pushed to the multimedia terminal.

[0046] Historical child profiles are digital records of children's long-term behavioral preferences and personalized characteristics in vehicles, used to achieve accurate content recommendations. The construction process is continuous during system operation: First, historical interaction data of children is collected, including the types of audio / video content played in the past, content tags (such as English, classical poetry, cartoons), playback duration, number of replays, temperature settings, and fan speed settings; second, the collected data is statistically analyzed to extract children's interest preference models, such as liking natural science audio, preferring English listening comprehension content, and liking specific styles of cartoons; finally, the above preference features are structured and stored to form a historical child profile containing fields such as age group, interest tags, frequently used content list, and air conditioning setting preferences. This profile is updated regularly as data accumulates, for example, by retraining the model weekly or monthly to adapt to changes in children's interests as they grow.

[0047] The pre-installed resource library is a database storing various multimedia materials for children. Its resources are indexed and categorized according to tags such as scene type, content category, and applicable age. The sources of these materials include, for example, content packages pre-installed on the in-vehicle system, resource streams synchronized from a cloud server, or user-imported personal collections via USB / Bluetooth.

[0048] During operation, the control terminal searches and matches resources in a pre-set resource library based on current scene recognition information (e.g., a school drop-off scenario) and historical child profiles (e.g., 8 years old, interested in English and science). This matching process might involve: first, filtering out a subset of resources associated with the school drop-off scenario (e.g., learning content, light music); second, further filtering from the resource subset based on interest tags in the historical child profile to find content that matches the child's preferences (e.g., English vocabulary audio, science stories); and finally, determining the target playback resource. If multiple candidate resources exist, the system can also sort or randomly select them based on playback duration, last playback time, etc. After determining the target playback resource, the control terminal generates a playback command and pushes it to the multimedia terminal. The multimedia terminal can be a rear-seat entertainment screen, a connected tablet, or a car audio system, etc., used to execute specific playback actions, thereby achieving personalized content delivery based on the specific scenario.

[0049] In a specific embodiment, the control terminal is communicatively connected to the temperature sensor 71 installed outside the vehicle and the air conditioner 70 installed inside the vehicle. The above execution steps also include the following methods: obtaining the adjustment temperature corresponding to the historical child portrait and temperature monitoring information according to a preset temperature adjustment strategy; the temperature monitoring information is collected by the temperature sensor; generating an adjustment command corresponding to the adjustment temperature and sending it to the air conditioner.

[0050] The pre-set temperature regulation strategy constructs a set of control logic for determining the target ambient temperature inside the vehicle. Its core mechanism lies in comprehensively calculating the real-time environmental conditions outside the vehicle and the long-term personalized preferences of the occupants. Temperature sensors located outside the vehicle collect temperature monitoring information. These sensors sense the meteorological parameters of the vehicle's environment in real time and upload data representing the ambient temperature to the control terminal. Historical child profiles are structured data records of child occupant characteristics accumulated over the long-term operation of the system. These records include preference fields directly related to temperature regulation, such as children's comfort feedback regarding the cabin temperature in different seasons and at different times.

[0051] Based on the aforementioned preset temperature regulation strategy, the control terminal performs correlation analysis between the real-time temperature monitoring information collected by the temperature sensor and the temperature preference data stored in the historical child profiles. Taking an outside ambient temperature of 35℃ as an example, if the historical child profile records show that the child prefers a cooler range of 24℃ to 26℃ in high-temperature environments, the temperature regulation strategy calculates the corresponding regulation temperature, such as T, based on the temperature difference between the inside and outside of the vehicle and the heat conduction model. m =Ts-k(T c -T s -T0), Tc is the temperature monitoring information (e.g., 35℃), T mTo adjust the temperature, k is the adjustment coefficient (e.g., set to 0.2), T s The system sets preferred temperatures for different temperature ranges (e.g., 25℃ corresponds to a cool range of 24℃ to 26℃) within the temperature preference data, with T0 as the base temperature parameter (e.g., set to 5℃). This calculation process is based on temperature monitoring information and the preference settings of historical child profiles. By combining real-time environmental perception with historical profile preferences, the system avoids blindly cooling or heating based on a single standard, thus achieving precise temperature control for specific children.

[0052] After determining the set temperature, the control terminal further converts this temperature parameter into an adjustment command that the vehicle's air conditioner can recognize and execute. The adjustment command is a data message conforming to vehicle communication protocol standards (such as the CAN bus protocol). It not only contains the target set temperature value but may also carry auxiliary control parameters such as fan speed, airflow mode (e.g., face, feet, or mixed mode), and circulation mode (internal or external circulation) according to a preset temperature adjustment strategy. The process of generating the adjustment command involves encapsulating these adjustment parameters according to a predetermined data frame format to form the adjustment command. Subsequently, the control terminal sends this adjustment command to the air conditioner installed inside the vehicle via a communication connection.

[0053] S150. Generate joint control reminder information corresponding to the lock status information according to the preset reminder strategy and the scene recognition information; the lock status information is collected by the cockpit joint control component.

[0054] The locking status information reflects the current status of various safety and locking mechanisms inside the vehicle cabin and is collected in real time by the cabin control system. The cabin control system is integrated into the vehicle's body electronic system and obtains information such as door lock status, window position, child seat belt buckle status, and child safety lock status through a sensor network.

[0055] The pre-defined reminder strategy defines the reminder rules and content to be triggered for different locking states in different scenarios. The strategy is built during the system configuration phase: First, safety risks in child pick-up and drop-off scenarios are identified, such as not wearing seatbelts when dropping off children, incompletely locked doors posing a driving risk when picking them up, and children being left behind when leaving the vehicle. Second, trigger conditions and reminder methods are set for each risk point; for example, a voice alarm is triggered when the vehicle is detected to be in motion and the child's seatbelt is not fastened. Finally, a strategy configuration file is generated, containing conditional judgment logic, reminder text templates, and output channels (such as voice, pop-ups, and dashboard warning lights). This strategy allows parents to customize it according to their actual needs, such as adjusting the reminder volume or enabling / disabling specific types of reminders.

[0056] During operation, the control terminal first obtains real-time locking status information from the cockpit control components. Then, combining this with current scene recognition information, it performs logical judgments based on preset reminder strategies. For example, in a school drop-off scenario, if the locking status information shows the rear seatbelts are unlocked, a voice-activated reminder to fasten the rear child seatbelts is generated according to the reminder strategy. Similarly, after the vehicle arrives at its destination and is turned off, if the locking status information shows the doors are still locked and there are still children inside (based on fusion recognition results), an emergency reminder to not leave children in the vehicle is generated. These generated reminders are output through multimedia terminals, instrument panel displays, or multimedia terminal interfaces, proactively reminding users to pay attention to safety at critical moments and effectively reducing safety hazards for children during vehicle travel.

[0057] Through the steps described above, the control terminal, in collaboration with the positioning components, pressure sensor array, infrared camera, multimedia terminal, and cockpit control components, achieves fully automated linkage throughout the entire process, from data acquisition and scene recognition to resource delivery and safety alerts. This multi-source information fusion and closed-loop control mechanism not only accurately identifies child pick-up and drop-off scenarios but also provides personalized services based on the child's profile. Simultaneously, targeted safety alerts ensure passenger safety, significantly enhancing the intelligence level and user experience of the smart cockpit in family travel scenarios.

[0058] In a specific embodiment, the above execution steps further include the following method: if air conditioning control information collected by the cockpit control component or multimedia control information collected by the multimedia terminal is received; update the historical child portrait according to the preset portrait update strategy and the air conditioning control information or the multimedia control information.

[0059] The user profile update strategy, as the core algorithmic logic for revising and optimizing the user preference model, is constructed during the system initialization and operation / maintenance phases. The input information for this strategy comes from the user's explicit interactive behaviors during the ride, such as air conditioning control information generated by adjusting the air conditioning panel, or multimedia control information generated by switching songs or videos via the touchscreen. The construction logic includes defining the mapping relationship between behaviors and profile features; for example, mapping repeatedly raising the temperature to a feature label indicating a preference for higher room temperatures, and frequently switching science-related audio to a feature label indicating a preference for science-related content. A weighted time decay algorithm is used to process historical data, ensuring that recent behaviors have a higher correction weight. Once formed, the user profile update strategy manifests as a series of correction rule sets or parameter configuration files, stored in the control terminal's storage medium. Its purpose is to enable the system to dynamically adjust the user profile model based on real-time user operations, thereby achieving adaptive evolution of the recommendation system.

[0060] Air conditioning control information represents the user's real-time adjustment needs for the vehicle's thermal environment and fan speed. This information is not a single temperature setpoint, but a high-dimensional data sequence containing dimensions such as adjustment action, adjustment range, adjustment time, and ambient temperature at the time of triggering the adjustment. When the user adjusts the air conditioning via physical buttons or the touchscreen in the cabin, the air conditioning controller sends corresponding status change messages to the control terminal via the CAN bus. Key parameters such as the target temperature value, target fan speed value, current actual temperature, current actual fan speed and temperature adjustment direction, and fan speed setting are parsed from the messages and used as air conditioning control information. This data reflects the child's perceived comfort preferences at different times and under different external weather conditions, and is used to update the ambient temperature preference model in the historical child profile. For example, if a user repeatedly raises the temperature setpoint to above 26 degrees Celsius during a winter trip, the profile update strategy will weight the winter preference for higher temperatures into the profile, and automatically recommend a higher set temperature in subsequent similar ambient temperatures.

[0061] Multimedia control information is generated by user interactions with multimedia terminals, specifically including logs of user actions such as playing, pausing, skipping songs, adding to favorites, unadding to favorites, and adjusting volume on audio and video content. This information is collected in real-time and pushed to the control terminal via the multimedia terminal's application programming interface (API). During the profile update process, the focus is on extracting content identification fields from the control information, such as unique identifiers for songs, program names for videos, or category tags for the content. Through statistical analysis of these actions, the profile update strategy can identify children's interest shifts and content preferences. For example, if, over a period of time, a user frequently skips English vocabulary audio and plays children's story audio for extended periods, the profile update strategy will correspondingly reduce the weight of the English learning category and increase the weight of the story / entertainment category, thereby correcting the distribution of interest tags in the historical child profile.

[0062] During the update process, the control terminal first cleans and standardizes the received air conditioning control information or multimedia control information to extract effective behavioral feature vectors. Then, according to the feature mapping rules in the preset profile update strategy, the extracted behavioral features are converted into modified values ​​for profile attributes. For example, a historical child profile includes at least the attribute values ​​corresponding to audio / video content type, content tags (such as English, classical poetry, and cartoons), playback duration, number of replays, temperature setting, and fan speed setting. The attribute value corresponding to the audio / video content type is [0,1], with values ​​closer to 0 indicating a preference for audio content and values ​​closer to 1 indicating a preference for video content. Among the content tags, English, classical poetry, and cartoons each correspond to three attribute values, all ranging from [0,1]. The magnitude of the attribute value indicates the degree of preference for the content tag type; the closer to 1, the higher the preference. The attribute value corresponding to playback duration is [0,1], where closer to 0 indicates a preference for short playback and closer to 1 indicates a preference for long playback; the attribute value corresponding to the number of replays is [0,1], where closer to 0 indicates a preference for no replays and closer to 1 indicates a preference for continuous replays; the attribute value corresponding to temperature setting is [0,1], where closer to 0 indicates a preference for cool air and closer to 1 indicates a preference for hot air; the attribute value corresponding to wind speed setting is [0,1], where closer to 0 indicates a preference for a light breeze and closer to 1 indicates a preference for strong winds. Based on the obtained correction values, the attribute values ​​in the historical child portrait are updated. The correction values ​​include a sign and a numerical value, such as "+0.13" which means increasing the original attribute value in the historical child portrait by 0.13; and "-0.2" which means decreasing the original attribute value in the historical child portrait by 0.2. After the calculation is completed, the system persistently stores the corrected attribute values ​​to update the historical child portrait, overwriting or merging it into the original historical child portrait. Through this continuous feedback learning mechanism, the system can continuously approach the user's true preferences, thereby providing more accurate resource recommendations and environmental adjustment services in subsequent pick-up and drop-off scenarios.

[0063] In a specific embodiment, the control terminal and the user terminal 80 establish a communication connection to transmit data information. The above execution steps also include the following methods: sending a corresponding home-school information acquisition request to the user terminal based on the historical child profile to obtain the corresponding home-school feedback information from the user terminal; generating corresponding home-school reminder information based on the home-school feedback information and pushing it to the multimedia terminal.

[0064] The control terminal interacts with the user terminal via a communication link. The user terminal refers to the smart device carried by the parent, such as a smartphone or tablet, which has an application or mini-program installed to complement the in-vehicle system, or is linked to a relevant home-school communication platform account. The generation of requests for home-school information is strictly based on the continuously updated historical child profile described in the preceding steps. This historical child profile includes key dimensions such as the child's identification (e.g., student ID, class), learning stage (e.g., second grade), subject preferences, and past pick-up and drop-off time patterns. Based on this profile information, the control terminal constructs targeted query instructions, such as requesting only homework, course schedules, and school notices after the current date or within a specific time period, thereby avoiding the acquisition of all redundant data. This request is sent to the user terminal via communication channels such as cellular networks (e.g., 4G / 5G), Wi-Fi, or Bluetooth. Upon receiving a request, the user terminal responds by searching the locally cached home-school application data or accessing the school's server in real time via the Internet to obtain the latest information. The search results are then packaged into home-school feedback information and sent back to the control terminal. This feedback information typically includes structured or semi-structured text data such as assignment content, submission deadlines, a list of items to bring (e.g., art supplies, sportswear), and school announcements.

[0065] The system performs semantic analysis and formatting on feedback information from home and school obtained from user terminals to generate home and school reminder messages adapted to the in-vehicle auditory environment. Given the limited visual resources in a driving environment, these reminders are primarily configured as natural language text in voice broadcast format, rather than complex graphic interfaces. During processing, key fields in the feedback information are analyzed, such as extracting the math test paper as the core item and "tomorrow morning" as the time constraint. These elements are then combined into natural voice prompts, such as: "You need to submit your math test paper tomorrow morning. Please check your bag before getting out of the car." The multimedia terminal, as the in-vehicle information output center, receives this text information and broadcasts it. By proactively synchronizing and converting school notifications, originally scattered across parents' mobile phones, into in-vehicle voice reminders, the system ensures that parents and children are aware of their learning tasks before the end of the trip, preventing academic disruption due to forgetfulness. This bridges the information gap between family, school, and vehicle, enhancing the smart cockpit's scenario-based service capabilities in children's education and daily life assistance.

[0066] In the aforementioned technological process, the precise request mechanism based on historical child profiles ensures the efficiency and relevance of information acquisition, avoiding irrelevant notifications from interfering with the in-vehicle space; while transforming digital home-school feedback into voice reminders that conform to human-vehicle interaction habits realizes the last mile of information flow, effectively improving the efficiency of information acquisition and the convenience of family education management in the scenario of picking up and dropping off children.

[0067] This invention discloses a smart cockpit linkage control method for child pick-up and drop-off scenarios. The method includes: parsing positioning information at a preset detection time point, integrating pressure sensing and infrared image recognition to identify the child's state, and determining the pick-up and drop-off scenario type based on time and location features; matching and pushing target playback resources based on the scenario type and historical child profiles; and generating linkage reminder information based on the cockpit's locking status. This application can accurately determine whether the vehicle is in a child pick-up and drop-off scenario, and performs multimedia terminal playback control and intelligent linkage of cockpit equipment based on scenario recognition information, improving passenger safety and enabling intelligent cockpit control in child pick-up and drop-off scenarios.

[0068] This invention also provides an intelligent cockpit linkage control system for child pick-up and drop-off scenarios. This system can be configured in a control terminal and is used to execute any embodiment of the aforementioned intelligent cockpit linkage control method for child pick-up and drop-off scenarios. Specifically, please refer to... Figure 3 , Figure 3 This is a schematic block diagram of an intelligent cockpit linkage control system for child pick-up and drop-off scenarios provided in an embodiment of the present invention.

[0069] like Figure 3 As shown, the intelligent cockpit linkage control system 100 for children's pick-up and drop-off scenarios includes a positioning information parsing unit 110, a fusion recognition unit 120, a scene recognition information acquisition unit 130, a target playback resource push unit 140, and a joint control reminder information generation unit 150.

[0070] The positioning information parsing unit 110 is used to parse the positioning information collected by the positioning component according to the preset positioning parsing rules when a preset detection time point is reached, so as to obtain the corresponding positioning parsing information.

[0071] The fusion recognition unit 120 is used to perform fusion recognition on pressure sensing information and infrared image according to a preset fusion recognition strategy to obtain the corresponding fusion recognition result; the pressure sensing information is collected by the pressure sensor array and the infrared image is collected by the infrared camera.

[0072] The scene recognition information acquisition unit 130 is used to identify the positioning parsing information and the fusion recognition result according to the pick-up and drop-off scene recognition rules, so as to obtain the corresponding scene recognition information.

[0073] The target playback resource push unit 140 is used to match and obtain the corresponding target playback resource from the preset resource library based on the pre-stored historical child portrait and the scene recognition information, and push it to the multimedia terminal.

[0074] The joint control reminder information generation unit 150 is used to generate joint control reminder information corresponding to the lockout status information according to the preset reminder strategy and the scene recognition information; the lockout status information is collected by the cockpit joint control component.

[0075] The intelligent cockpit linkage control device for child pick-up and drop-off scenarios provided in this embodiment of the invention applies the aforementioned intelligent cockpit linkage control method for child pick-up and drop-off scenarios. It analyzes positioning information at preset detection time points, integrates pressure sensing and infrared image recognition to identify the child's state, and determines the pick-up and drop-off scenario type based on time and location characteristics. Based on the scenario type and historical child profiles, it matches and pushes target playback resources, and simultaneously generates linkage reminder information based on the cockpit's locking status. This application can accurately determine whether the vehicle is in a child pick-up and drop-off scenario, and performs multimedia terminal playback control and intelligent linkage of cockpit equipment based on scenario recognition information, improving passenger safety and enabling intelligent cockpit control in child pick-up and drop-off scenarios.

[0076] The aforementioned intelligent cockpit linkage control device for child pick-up and drop-off scenarios can be implemented in the form of a computer program, which can, for example... Figure 4 It runs on the computer device shown.

[0077] Please see Figure 4 , Figure 4 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. This computer device can be a control terminal used to execute an intelligent cockpit linkage control method for child pick-up and drop-off scenarios to achieve data analysis and component control.

[0078] See Figure 4 The computer device 500 includes a processor 502, a memory, and a communication interface 505 connected via a communication bus 501. The memory may include a storage medium 503 and internal memory 504.

[0079] The storage medium 503 may store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, it enables the processor 502 to execute a smart cockpit linkage control method for child pick-up and drop-off scenarios. The storage medium 503 may be a volatile storage medium or a non-volatile storage medium.

[0080] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0081] The internal memory 504 provides an environment for the operation of the computer program 5032 in the storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a smart cockpit linkage control method for child pick-up and drop-off scenarios.

[0082] This communication interface 505 is used for network communication, such as providing data transmission. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device 500 to which the present invention is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0083] The processor 502 is used to run the computer program 5032 stored in the memory to implement the corresponding functions in the above-mentioned intelligent cockpit linkage control method for child pick-up and drop-off scenarios.

[0084] Those skilled in the art will understand that Figure 4 The embodiments of the computer device shown do not constitute a limitation on the specific configuration of the computer device. In other embodiments, the computer device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. For example, in some embodiments, the computer device may include only memory and a processor. In such embodiments, the structure and function of the memory and processor are different from those shown. Figure 4 The embodiments shown are consistent and will not be described again here.

[0085] It should be understood that, in this embodiment of the invention, the processor 502 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0086] In another embodiment of the invention, a computer-readable storage medium is provided. This computer-readable storage medium may be volatile or non-volatile. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps included in the above-described intelligent cockpit linkage control method for child pick-up and drop-off scenarios.

[0087] Those skilled in the art will readily understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0088] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Units with the same function may be grouped into one unit. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, or it may be an electrical, mechanical, or other form of connection.

[0089] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.

[0090] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0091] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a computer-readable storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned computer-readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks.

[0092] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A smart cockpit linkage control method for child pick-up and drop-off scenarios, characterized in that, The method is applied to a control terminal, which is communicatively connected to a positioning component, a pressure sensor array, an infrared camera, a multimedia terminal, and a cockpit control component to transmit data information. The pressure sensor array is disposed within the seat cushion of the vehicle seat, and the infrared camera is disposed on the side facing the seat. The method includes: If the preset detection time point is reached, the positioning information collected by the positioning component is parsed according to the preset positioning parsing rules to obtain the corresponding positioning parsing information; The pressure sensing information and infrared image are fused and recognized according to a preset fusion recognition strategy to obtain the corresponding fusion recognition result; the pressure sensing information is collected by the pressure sensor array and the infrared image is collected by the infrared camera. The location parsing information and the fusion recognition result are identified according to the pick-up and drop-off scene recognition rules to obtain the corresponding scene recognition information; Based on the pre-stored historical child portraits and the scene recognition information, the corresponding target playback resources are matched and obtained from the pre-set resource library and pushed to the multimedia terminal; Based on the preset reminder strategy and the scene recognition information, a joint control reminder message corresponding to the lock status information is generated; the lock status information is collected by the cockpit joint control component.

2. The intelligent cockpit linkage control method for child pick-up and drop-off scenarios according to claim 1, characterized in that, The step of parsing the positioning information collected by the positioning component according to the preset positioning parsing rules to obtain the corresponding positioning parsing information includes: Determine whether the location deviation between the target location corresponding to the location information and the historical pick-up and drop-off trajectory in the location parsing rule is within the deviation range in the location parsing rule; If the positioning deviation is within the deviation range, matching positioning parsing information is obtained; If the location deviation is not within the deviation range, the location information is compared with the historical pick-up and drop-off trajectory in the location parsing rule to obtain the corresponding trajectory overlap. Determine whether the overlap degree is greater than the overlap degree threshold set in the positioning parsing rule to obtain the positioning parsing information of whether they match.

3. The intelligent cockpit linkage control method for child pick-up and drop-off scenarios according to claim 2, characterized in that, The step of fusing and recognizing pressure-sensing information and infrared images according to a preset fusion recognition strategy to obtain corresponding fusion recognition results includes: According to the feature extraction rules in the fusion recognition strategy, the corresponding pressure sensing feature parameters and infrared image feature parameters are extracted from the pressure sensing information and the infrared image, respectively. The pressure-sensing feature parameters and the infrared image feature parameters are input into the recognition model in the fusion recognition strategy to obtain the corresponding recognition probability and confidence level. The recognition probability and confidence level are judged according to the judgment rules in the fusion recognition strategy to obtain the corresponding fusion recognition result.

4. The intelligent cockpit linkage control method for child pick-up and drop-off scenarios according to claim 3, characterized in that, The step of identifying the location parsing information and the fused identification result according to the pick-up and drop-off scene identification rules to obtain the corresponding scene identification information includes: Based on the probability analysis model in the pick-up and drop-off scenario recognition rules, a probability analysis is performed on the current time, the location parsing information, and the fusion recognition result to obtain the corresponding classification probability analysis information. The scene type is parsed based on the scene parsing model in the pick-up and drop-off scene recognition rules to determine the corresponding scene type as scene recognition information.

5. The intelligent cockpit linkage control method for child pick-up and drop-off scenarios according to claim 4, characterized in that, The control terminal communicates with a temperature sensor located outside the vehicle and an air conditioner located inside the vehicle. The method further includes: The adjustment temperature is obtained according to a preset temperature adjustment strategy, corresponding to the historical child portrait and temperature monitoring information; the temperature monitoring information is collected by the temperature sensor. An adjustment command corresponding to the adjusted temperature is generated and sent to the air conditioner.

6. The intelligent cockpit linkage control method for child pick-up and drop-off scenarios according to any one of claims 1-5, characterized in that, The method further includes: If the cockpit control component receives air conditioning control information or the multimedia terminal receives multimedia control information; The historical child portrait is updated according to the preset portrait update strategy and the air conditioning control information or the multimedia control information.

7. The intelligent cockpit linkage control method for child pick-up and drop-off scenarios according to claim 6, characterized in that, The control terminal establishes a communication connection with the user terminal to transmit data information, and the method further includes: Based on the historical child profile, a corresponding home-school information retrieval request is sent to the user terminal to obtain the corresponding home-school feedback information from the user terminal. Based on the feedback information from home and school, a corresponding home-school reminder message is generated and pushed to the multimedia terminal.

8. A smart cockpit linkage control system for child pick-up and drop-off scenarios, characterized in that, The system is configured on a control terminal, which communicates with a positioning component, a pressure sensor array, an infrared camera, a multimedia terminal, and a cockpit control component to transmit data. The pressure sensor array is installed inside the seat cushion of the vehicle seat, and the infrared camera is positioned facing the seat. The system is used to execute the intelligent cockpit linkage control method for child pick-up and drop-off scenarios as described in any one of claims 1-7. The system includes: The positioning information parsing unit is used to parse the positioning information collected by the positioning component according to the preset positioning parsing rules when a preset detection time point is reached, so as to obtain the corresponding positioning parsing information. The fusion recognition unit is used to perform fusion recognition on pressure sensing information and infrared image according to a preset fusion recognition strategy to obtain the corresponding fusion recognition result; the pressure sensing information is collected by the pressure sensor array and the infrared image is collected by the infrared camera. The scene recognition information acquisition unit is used to identify the positioning parsing information and the fusion recognition result according to the pick-up and drop-off scene recognition rules to obtain the corresponding scene recognition information; The target playback resource push unit is used to match and obtain the corresponding target playback resource from the preset resource library based on the pre-stored historical child portrait and the scene recognition information, and push it to the multimedia terminal. The joint control reminder information generation unit is used to generate joint control reminder information corresponding to the lockout status information based on the preset reminder strategy and the scene recognition information; the lockout status information is collected by the cockpit joint control component.

9. A computer device, characterized in that, The device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When the processor executes the program stored in the memory, it implements the steps of the intelligent cockpit linkage control method for child pick-up and drop-off scenarios as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent cockpit linkage control method for child pick-up and drop-off scenarios as described in any one of claims 1-7.

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