Perception recognition storage terminal system based on big data

Abstract

The present invention discloses a perception recognition storage terminal system based on big data, comprising a behavior recognition terminal, a scanning and analysis terminal, a prejudgment analysis terminal and a judgment control terminal, wherein the behavior recognition terminal is electrically connected to the scanning and analysis terminal, the scanning and analysis terminal is electrically connected to the prejudgment analysis terminal, the prejudgment analysis terminal is electrically connected to the judgment control terminal, the behavior recognition terminal is used to recognize the state of a rider when riding a bicycle, the scanning and analysis terminal is used to scan and analyze the situation in front of the rider, the prejudgment analysis terminal is used to calculate and analyze the probability of an accident after detecting an object, the judgment control module is used to prejudge the degree of danger of the environment and execute control on the vehicle, the behavior recognition terminal comprises a vehicle speed recognition module, an eye tracking module, a motion capture module and a danger recognition module, the vehicle speed recognition module is electrically connected to the eye tracking module, and the present invention has the characteristics of judging the rider's situation and emergency control in an emergency.

Description

Technical Field

[0001] The present invention relates to the field of perception recognition storage technology, and in particular to a perception recognition storage terminal system based on big data. Background Art

[0002] With the continuous development of the Internet and the catering industry, the "lazy economy" has become an economic phenomenon. Takeout has become one of people's mainstream lifestyles. Online takeout platforms are the first choice for netizens to order takeout. Under the trend of consumption upgrading, people's requirements for takeout delivery services are getting higher and higher, which has also prompted online takeout platforms to continuously increase their service system with delivery as the core. Therefore, every time it is mealtime, you can see busy takeout riders in the streets and alleys, rain or shine.

[0003] In addition, the traffic safety of food delivery drivers has always been a hot topic in society. Speeding, driving against traffic, running red lights, and talking on the phone while riding are common behaviors that "risk their lives to deliver food". Riders are constrained by assessments such as punctuality and bad review rates. Being in a hurry is the core factor for illegal driving of food delivery vehicles. For convenience, many riders will install mobile phone holders on their vehicles, which will result in them looking at their phones while riding, making traffic accidents more likely to occur. Therefore, it is necessary to design a perception, recognition, and storage terminal system based on big data to judge the rider's situation and conduct emergency control in emergencies. Summary of the invention

[0004] The purpose of the present invention is to provide a perception recognition storage terminal system based on big data to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a perception, recognition and storage terminal system based on big data, including a behavior recognition end, a scanning and analysis end, a pre-judgment analysis end and a judgment and control end, the behavior recognition end and the scanning and analysis end are electrically connected, the scanning and analysis end and the pre-judgment analysis end are electrically connected, the pre-judgment analysis end and the judgment and control end are electrically connected, the behavior recognition end is used to identify the state of the rider when riding a bicycle, the scanning and analysis end is used to scan and analyze the situation in front of the rider, the pre-judgment analysis end is used to calculate and analyze the probability of an accident after detecting an object, and the judgment and control end is used to predict the degree of danger of the environment and control the vehicle.

[0006] According to the above technical solution, the behavior recognition end includes a vehicle speed recognition module, an eye tracking module, a motion capture module and a danger recognition module. The vehicle speed recognition module is electrically connected to the eye tracking module, the eye tracking module is electrically connected to the motion capture module, and the motion capture module is electrically connected to the danger recognition module. The vehicle speed recognition module is used to identify the current riding speed, the eye tracking module is used to track the rider's pupil focus point, the eye tracking module includes a timing calculation submodule, the timing calculation submodule is used to time the rider's dangerous actions, the motion capture module is used to capture the rider's dangerous actions during riding, the danger recognition module is used to identify the degree of danger of the rider's riding action, the danger recognition module includes a motion analysis submodule and a danger transmission submodule, the motion analysis submodule is electrically connected to the danger transmission submodule, the motion analysis submodule is used to analyze the danger coefficient of the action, and the danger transmission submodule is used to transmit a danger instruction when the dangerous action is maintained above a certain threshold.

[0007] According to the above technical solution, the scanning and analysis end includes a scanning detection module, a trajectory judgment module and an object analysis module, the scanning detection module and the trajectory judgment module are electrically connected, the trajectory judgment module and the object analysis module are electrically connected, the scanning detection module is used to perform infrared scanning detection on the environment in front of the rider, the trajectory judgment module is used to judge the movement trajectory of the detected object, the trajectory judgment module includes a speed analysis submodule, the speed analysis submodule is used to analyze the movement speed of the identified object according to the database, the object analysis module is used to analyze whether the detected object is moving, and the object analysis module includes a radar detection submodule, and the radar detection submodule is used to perform radar detection on the detected object.

[0008] According to the above technical solution, the prediction and analysis end includes a route analysis module and a calculation and analysis module, the route analysis module and the calculation and analysis module are electrically connected, the route analysis module is used to predict the route where the accident occurs for analysis, the route analysis module includes an infrared ranging submodule, the infrared ranging submodule is used to measure the distance between the object and the rider when the dangerous behavior of the rider is identified, the calculation and analysis module is used to calculate the required speed of the vehicle according to the situation, and the calculation and analysis module includes an information collection submodule, and the information collection submodule is used to collect the acquired information;

[0009] The judgment control end includes an information judgment submodule and an execution control module, and the information judgment submodule and the execution control module are electrically connected. The information judgment submodule is used to distinguish the current dangerous situation and make corresponding responses. The information judgment submodule includes an information receiving submodule, and the information receiving submodule is used to receive the transmitted instructions and information of the analysis results. The execution control module is used to execute control over the vehicle.

[0010] According to the above technical solution, the operation method of the perception recognition storage terminal system mainly includes the following steps:

[0011] Step S1: When the delivery rider is riding, the behavior recognition terminal recognizes the rider's behavior;

[0012] Step S2: When a dangerous behavior is identified, the scanning and analysis terminal performs infrared scanning detection on the environment in front of the rider;

[0013] Step S3: Based on the scanned and detected objects, the prediction analysis terminal predicts the probability of an accident occurring;

[0014] Step S4: For the probability of advance prediction, determine whether the control end performs control on the vehicle.

[0015] According to the above technical solution, step S1 further includes the following steps:

[0016] Step S11: When the vehicle speed recognition module recognizes that the vehicle has a speed value, it transmits a signal to the eye tracking module;

[0017] Step S12: The eye tracking module on the mobile phone is started to scan the rider's face and capture the rider's eye information. When the rider's pupil focus is tracked in the direction of the mobile phone, the timing calculation submodule times the current action;

[0018] Step S13: At the same time, the motion capture module captures the rider's motion of touching and sliding the mobile phone screen;

[0019] Step S14: Based on the identified rider's behavior, the danger identification module analyzes the rider's current action based on the action analysis submodule to identify the rider's danger level;

[0020] Step S15: When the duration of the dangerous behavior exceeds a certain threshold, the danger transmission submodule sends a braking command;

[0021] The step S14 further comprises the following steps:

[0022] Step S141: the action analysis submodule analyzes the rider's current dangerous behavior;

[0023] Step S142: when it is identified that the rider's pupil focus is on the mobile phone, the behavior risk coefficient M=1;

[0024] Step S143: It is identified that the rider's pupil focus is on the mobile phone, and there is an action of touching and sliding the mobile phone screen, and the behavior risk coefficient M=2.

[0025] According to the above technical solution, in S14, the calculation formula of the identified danger level W is:

[0026] W=K*T*V1*M

[0027] Among them, W is the identified degree of danger, K is the danger situation conversion coefficient, V1 is the current riding speed, T is the time value of the dangerous behavior, and M is the risk coefficient of the behavior performed by the rider while riding. The riding speed, the time of the dangerous behavior and the dangerous behavior performed are all proportional to the degree of danger. The time value T of the dangerous behavior and the current riding speed value V1 cannot exceed a certain threshold. When the two sufficient conditions of T>6s and V1>10m / s are met, the danger identification module sends a braking command.

[0028] According to the above technical solution, step S2 further includes the following steps:

[0029] Step S21: The dangerous behavior of the rider is identified, and at the same time, the scanning detection module of the front of the vehicle forms an infrared detection barrier to scan and detect the environment in front of the rider;

[0030] Step S22: using the radar detection submodule to perform radar detection on whether the scanned object is moving;

[0031] Step S23: Based on the analysis and judgment results of the object analysis module, the trajectory judgment module judges the object movement trajectory, and the speed analysis submodule analyzes the movement speed of the target object from radar detection.

[0032] According to the above technical solution, step S3 further includes the following steps:

[0033] Step S31: the infrared distance measuring submodule uses infrared rays to measure the distance between the object and the rider;

[0034] Step S32: the route analysis module analyzes the probability of predicting an accident;

[0035] Step S33: According to the predicted situation, the calculation and analysis module analyzes the information collected by the information collection submodule to calculate the current vehicle speed required;

[0036] In step S32, the calculation formula for predicting the accident is:

[0037]

[0038] Among them, t is the time value of the predicted accident at the current speed, t>0, C is the distance between the rider and the object measured at the current position, V1 is the original speed of the rider, V2 is the speed of the object whose trajectory intersects with the rider per second, and N is the probability of an accident between the rider and the identified object at the same time. When V1>V2, the accident must satisfy both formula 1 and formula 2. When V2>V1, the accident must satisfy both formula 1 and formula 3. ε is the conversion coefficient. The faster the predicted time of an accident, the higher the possibility of an accident.

[0039] In step S33, the calculation formula of the current vehicle required speed V0 is:

[0040]

[0041] Among them, V0 is the speed that can avoid accidents. The identified danger level W and the probability N of an accident between the rider and the identified object at the same time are inversely proportional to the current vehicle speed V0. The higher the danger level, the slower the vehicle speed V0. <V1。

[0042] According to the above technical solution, step S4 further includes the following steps:

[0043] Step S41: the information receiving submodule receives the transmitted instruction and the information of the analysis result;

[0044] Step S42: the information identification submodule identifies the current dangerous situation based on the received data and responds accordingly;

[0045] Step S43: According to the response, the execution control module controls the vehicle.

[0046] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, by providing a behavior recognition terminal, a scanning and analysis terminal, a pre-judgment analysis terminal and a judgment and control terminal, can identify the dangerous actions of the rider, and detect the situation in front of the rider's vehicle in the process of making dangerous actions. When a moving object is scanned, the probability of an accident is calculated and analyzed based on the rider's current speed and the speed of the moving object, the degree of danger is judged, and the vehicle is controlled to avoid accidents and delays in delivery time. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0048] In the attached picture:

[0049] Figure 1It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] See also Figure 1 The present invention provides a technical solution: a perception recognition and storage terminal system based on big data, including a behavior recognition terminal, a scanning and analysis terminal, a prejudgment analysis terminal and a judgment control terminal, the behavior recognition terminal is electrically connected to the scanning and analysis terminal, the scanning and analysis terminal is electrically connected to the prejudgment analysis terminal, and the prejudgment analysis terminal is electrically connected to the judgment control terminal. The behavior recognition terminal is used to recognize the state of the rider when riding a bicycle, the scanning and analysis terminal is used to scan and analyze the situation in front of the rider, the prejudgment analysis terminal is used to calculate and analyze the probability of an accident after detecting an object, and the judgment control terminal is used to predict the degree of danger of the environment and control the vehicle; by providing the behavior recognition terminal, the scanning and analysis terminal, the prejudgment analysis terminal and the judgment control terminal, the dangerous actions of the rider can be identified, and the situation in front of the rider's vehicle is detected in the process of making dangerous actions. When a moving object is scanned, the probability of an accident is calculated and analyzed according to the current speed of the rider and the speed of the moving object, the degree of danger is judged, and the vehicle is controlled to avoid accidents and delays in food delivery time.

[0052] The behavior recognition end includes a vehicle speed recognition module, an eye tracking module, a motion capture module and a danger recognition module. The vehicle speed recognition module is electrically connected to the eye tracking module, the eye tracking module is electrically connected to the motion capture module, and the motion capture module is electrically connected to the danger recognition module. The vehicle speed recognition module is used to identify the current riding speed, the eye tracking module is used to track the rider's pupil focus point, the eye tracking module includes a timing calculation submodule, the timing calculation submodule is used to time the rider's dangerous actions, the motion capture module is used to capture the rider's dangerous actions during riding, the danger recognition module is used to identify the degree of danger of the rider's riding actions, the danger recognition module includes a motion analysis submodule and a danger transmission submodule, the motion analysis submodule is electrically connected to the danger transmission submodule, the motion analysis submodule is used to analyze the danger coefficient of the action, and the danger transmission submodule is used to transmit danger instructions when the dangerous action is maintained above a certain threshold.

[0053] The scanning and analysis end includes a scanning detection module, a trajectory judgment module and an object analysis module. The scanning detection module and the trajectory judgment module are electrically connected. The trajectory judgment module and the object analysis module are electrically connected. The scanning detection module is used to perform infrared scanning detection on the environment in front of the rider. The trajectory judgment module is used to judge the movement trajectory of the detected object. The trajectory judgment module includes a speed analysis submodule. The speed analysis submodule is used to analyze the movement speed of the identified object according to the database. The object analysis module is used to analyze whether the detected object is moving. The object analysis module includes a radar detection submodule. The radar detection submodule is used to perform radar detection on the detected object.

[0054] The prediction and analysis end includes a route analysis module and a calculation and analysis module, the route analysis module and the calculation and analysis module are electrically connected, the route analysis module is used to predict the route where the accident occurs for analysis, the route analysis module includes an infrared distance measurement submodule, the infrared distance measurement submodule is used to measure the distance between the object and the rider when the dangerous behavior of the rider is identified, the calculation and analysis module is used to calculate the required speed of the vehicle according to the situation, and the calculation and analysis module includes an information collection submodule, the information collection submodule is used to collect the acquired information;

[0055] The judgment control end includes an information discrimination submodule and an execution control module. The information discrimination submodule and the execution control module are electrically connected. The information discrimination submodule is used to identify the current dangerous situation and make corresponding responses. The information discrimination submodule includes an information receiving submodule. The information receiving submodule is used to receive the transmitted instructions and information of the analysis results. The execution control module is used to execute control over the vehicle.

[0056] The operation method of the perception recognition storage terminal system mainly includes the following steps:

[0057] Step S1: When the delivery rider is riding, the behavior recognition terminal recognizes the rider's behavior;

[0058] Step S2: When a dangerous behavior is identified, the scanning and analysis terminal performs infrared scanning detection on the environment in front of the rider;

[0059] Step S3: Based on the scanned and detected objects, the prediction analysis terminal predicts the probability of an accident occurring;

[0060] Step S4: For the probability of advance prediction, determine whether the control end performs control on the vehicle.

[0061] Step S1 further comprises the following steps:

[0062] Step S11: When the vehicle speed recognition module recognizes that the vehicle has a speed value, it transmits a signal to the eye tracking module;

[0063] Step S12: The eye tracking module on the mobile phone is started to scan the rider's face and capture the rider's eye information. When the rider's pupil focus is tracked in the direction of the mobile phone, the timing calculation submodule times the current action;

[0064] Step S13: At the same time, the motion capture module captures the rider's motion of touching and sliding the mobile phone screen;

[0065] Step S14: Based on the identified rider's behavior, the danger identification module analyzes the rider's current action based on the action analysis submodule to identify the rider's danger level;

[0066] Step S15: When the duration of the dangerous behavior exceeds a certain threshold, the danger transmission submodule sends a braking command;

[0067] Step S14 further includes the following steps:

[0068] Step S141: the action analysis submodule analyzes the rider's current dangerous behavior;

[0069] Step S142: when it is identified that the rider's pupil focus is on the mobile phone, the behavior risk coefficient M=1;

[0070] Step S143: It is identified that the rider's pupil focus is on the mobile phone, and there is an action of touching and sliding the mobile phone screen, and the behavior risk coefficient M=2.

[0071] In S14, the calculation formula of the identified danger level W is:

[0072] W=K*T*V1*M

[0073] Among them, W is the identified degree of danger, K is the danger situation conversion coefficient, V1 is the current riding speed, T is the time value of the dangerous behavior, and M is the risk coefficient of the behavior performed by the rider while riding. The riding speed, the time of the dangerous behavior and the dangerous behavior performed are all proportional to the degree of danger. The time value T of the dangerous behavior and the current riding speed value V1 cannot exceed a certain threshold. When the two sufficient conditions of T>6s and V1>10m / s are met, the danger identification module sends a braking command.

[0074] Step S2 further comprises the following steps:

[0075] Step S21: The dangerous behavior of the rider is identified, and at the same time, the scanning detection module of the front of the vehicle forms an infrared detection barrier to scan and detect the environment in front of the rider;

[0076] Step S22: using the radar detection submodule to perform radar detection on whether the scanned object is moving, the radar detects the scanned object twice with an interval of one second;

[0077] Step S23: Based on the analysis and judgment results of the object analysis module, the trajectory judgment module judges the object movement trajectory, and the speed analysis submodule analyzes the movement speed of the target object from radar detection.

[0078] Step S3 further comprises the following steps:

[0079] Step S31: the infrared distance measuring submodule uses infrared rays to measure the distance between the object and the rider;

[0080] Step S32: the route analysis module analyzes the probability of predicting an accident;

[0081] Step S33: According to the predicted situation, the calculation and analysis module analyzes the information collected by the information collection submodule to calculate the current vehicle speed required;

[0082] In step S32, the calculation formula for predicting the accident is:

[0083]

[0084] Among them, t is the time value of the predicted accident at the current speed, t>0, C is the distance between the rider and the object measured at the current position, V1 is the original speed of the rider, V2 is the speed of the object whose trajectory intersects with the rider per second, and N is the probability of an accident between the rider and the identified object at the same time. When V1>V2, the accident must satisfy both formula 1 and formula 2. When V2>V1, the accident must satisfy both formula 1 and formula 3. ε is the conversion coefficient. The faster the predicted time of an accident, the higher the possibility of an accident. If both formulas are satisfied, the distance traveled by the rider and the distance traveled by the identified object at the same time can be predicted, and the relationship between the distance between the rider and the object measured at the current position can form the conditions for a triangle.

[0085] In step S33, the calculation formula of the current vehicle required speed V0 is:

[0086]

[0087] Among them, V0 is the speed that can avoid accidents. The identified danger level W and the probability N of an accident between the rider and the identified object at the same time are inversely proportional to the current vehicle speed V0. The higher the danger level, the slower the vehicle speed V0. <V1。

[0088] Step S4 further comprises the following steps:

[0089] Step S41: the information receiving submodule receives the transmitted instruction and the information of the analysis result;

[0090] Step S42: The information discrimination sub-module discriminates the current dangerous situation based on the received data and makes corresponding responses.

[0091] Step S43: The execution control module controls the vehicle according to the made response.

[0092] Embodiment 1: When the vehicle speed value is recognized, the upper end of the mobile phone scans the rider's face, captures the rider's pupil focus point, the behavior risk coefficient M = 1, and the dangerous situation conversion coefficient The current vehicle speed of the rider V1 = 14 m / s, the time T that the pupil focus point maintains on the mobile phone is 3 s. According to the formula W = K * T * V1 * M, the recognized danger level W = 8.4. A moving object is scanned and detected. The distance C between the rider's current position and the object is 8 m. It is judged by the radar that the moving object and the rider's moving trajectory cross, and the moving speed of the detected moving object V2 = 4 m / s. According to the formula It can be obtained that ε = 4, 5 < N < 9. According to the formula It can be obtained that 13.7 m / s < V0 < 13.81 m / s.

[0093] Embodiment 2: When the vehicle speed value is recognized, the upper end of the mobile phone scans the rider's face, captures the rider's pupil focus point, the behavior risk coefficient M = 1, and the dangerous situation conversion coefficient The current vehicle speed of the rider V1 = 13 m / s, the time T that the pupil focus point maintains on the mobile phone is 5 s. According to the formula W = K * T * V1 * M, the recognized danger level W = 6.5. A moving object is scanned and detected. The distance C between the rider's current position and the object is 10 m. It is judged by the radar that the moving object and the rider's moving trajectory cross, and the moving speed of the detected moving object V2 = 6 m / s. According to the formula It can be obtained that ε = 10, 7 < N < 19. According to the formula It can be obtained that 12.71 m / s < V0 < 12.9 m / s.

[0094] Embodiment 3: When the vehicle speed value is recognized, the upper end of the mobile phone scans the rider's face, captures the rider's pupil focus point, and there is an action of touching and sliding the mobile phone screen. The behavior risk coefficient M = 2, and the dangerous situation conversion coefficient The current speed of the rider is V1=12m / s, and the time the pupil focuses on the mobile phone is T=7S. According to the formula W=K*T*V1*M, the identified danger level is W=16.8. When the two sufficient conditions of T>6s and V>10m / s are met, the danger recognition module sends a braking command to control the vehicle to stop slowly to ensure safety.

[0095] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0096] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A perception recognition and storage terminal system based on big data, including a behavior recognition terminal, a scanning analysis terminal, a prejudgment analysis terminal and a judgment control terminal, characterized by: The behavior recognition end is electrically connected to the scanning and analysis end, the scanning and analysis end is electrically connected to the prejudgment analysis end, and the prejudgment analysis end is electrically connected to the judgment control end. The behavior recognition end is used to recognize the state of the rider when riding the bicycle, the scanning and analysis end is used to scan and analyze the situation in front of the rider, the prejudgment analysis end is used to calculate and analyze the probability of an accident after detecting an object, and the judgment control end is used to prejudge the degree of danger of the environment and control the vehicle; The behavior recognition terminal includes a vehicle speed recognition module, an eye tracking module, a motion capture module and a danger recognition module. The vehicle speed recognition module is electrically connected to the eye tracking module, the eye tracking module is electrically connected to the motion capture module, and the motion capture module is electrically connected to the danger recognition module. The vehicle speed recognition module is used to recognize the current riding speed. The eye tracking module is used to track the pupil focus point of the rider. The eye tracking module includes a timing calculation submodule, the timing calculation submodule is used to time the rider's dangerous actions. The motion capture module is used to capture the rider's dangerous actions during riding. The danger recognition module is used to recognize the degree of danger of the rider's riding action. The danger recognition module includes a motion analysis submodule and a danger transmission submodule. The motion analysis submodule is electrically connected to the danger transmission submodule. The motion analysis submodule is used to analyze the danger coefficient of the action. The danger transmission submodule is used to transmit a danger instruction when the dangerous action is maintained above a certain threshold. The scanning and analyzing end includes a scanning detection module, a trajectory judgment module and an object analysis module, the scanning detection module and the trajectory judgment module are electrically connected, the trajectory judgment module and the object analysis module are electrically connected, the scanning detection module is used to perform infrared scanning detection on the environment in front of the rider, the trajectory judgment module is used to judge the movement trajectory of the detected object, the trajectory judgment module includes a speed analysis submodule, the speed analysis submodule is used to analyze the movement speed of the identified object according to the database, the object analysis module is used to analyze whether the detected object is moving, and the object analysis module includes a radar detection submodule, the radar detection submodule is used to perform radar detection on the detected object; The prediction and analysis end includes a route analysis module and a calculation and analysis module, the route analysis module and the calculation and analysis module are electrically connected, the route analysis module is used to predict the route where the accident occurs for analysis, the route analysis module includes an infrared distance measurement submodule, the infrared distance measurement submodule is used to measure the distance between the object and the rider when the dangerous behavior of the rider is identified, the calculation and analysis module is used to calculate the required speed of the vehicle according to the situation, and the calculation and analysis module includes an information collection submodule, the information collection submodule is used to collect the acquired information; The judgment control end includes an information determination submodule and an execution control module, the information determination submodule and the execution control module are electrically connected, the information determination submodule is used to identify the current dangerous situation and make a corresponding response, the information determination submodule includes an information receiving submodule, the information receiving submodule is used to receive the transmitted instructions and the information of the analysis result, and the execution control module is used to execute the control of the vehicle; The operating method of the perception recognition storage terminal system mainly includes the following steps: Step S1: When the delivery rider is riding, the behavior recognition terminal recognizes the rider's behavior; Step S2: When a dangerous behavior is identified, the scanning and analysis terminal performs infrared scanning detection on the environment in front of the rider; Step S3: Based on the scanned and detected objects, the prediction analysis terminal predicts the probability of an accident occurring; Step S4: for the probability of advance prediction, determine whether the control end controls the vehicle; The step S1 further comprises the following steps: Step S11: When the vehicle speed recognition module recognizes that the vehicle has a speed value, it transmits a signal to the eye tracking module; Step S12: The eye tracking module on the mobile phone is started to scan the rider's face and capture the rider's eye information. When the rider's pupil focus is tracked in the direction of the mobile phone, the timing calculation submodule times the current action; Step S13: At the same time, the motion capture module captures the rider's motion of touching and sliding the mobile phone screen; Step S14: Based on the identified rider's behavior, the danger identification module analyzes the rider's current action based on the action analysis submodule to identify the rider's danger level; Step S15: When the duration of the dangerous behavior exceeds a certain threshold, the danger transmission submodule sends a braking command; The step S14 further comprises the following steps: Step S141: the action analysis submodule analyzes the rider's current dangerous behavior; Step S142: when it is identified that the rider's pupil focus is on the mobile phone, the behavior risk coefficient M=1; Step S143: It is identified that the rider's pupil focus is on the mobile phone, and there is an action of touching and sliding the mobile phone screen, and the behavior risk coefficient M=2; In S14, the calculation formula of the identified danger level W is: W=K*T*V1*M Among them, W is the identified danger level, K is the danger situation conversion coefficient, V1 is the current riding speed, T is the duration of the dangerous behavior, and M is the risk coefficient of the behavior performed by the rider during riding. The riding speed, the duration of the dangerous behavior, and the dangerous behavior performed are all proportional to the danger level. The duration T of the dangerous behavior and the current riding speed V1 cannot exceed a certain threshold. When the two sufficient conditions of T>6s and V1>10m / s are met, the danger recognition module sends a braking command; The step S2 further comprises the following steps: Step S21: The dangerous behavior of the rider is identified, and at the same time, the scanning detection module of the front of the vehicle forms an infrared detection barrier to scan and detect the environment in front of the rider; Step S22: using the radar detection submodule to perform radar detection on whether the scanned object is moving; Step S23: according to the result of the analysis and judgment by the object analysis module, the trajectory judgment module judges the object's movement trajectory, and the speed analysis submodule analyzes the movement speed of the target object from the radar detection; The step S3 further comprises the following steps: Step S31: the infrared distance measuring submodule uses infrared rays to measure the distance between the object and the rider; Step S32: the route analysis module analyzes the probability of predicting an accident; Step S33: According to the predicted situation, the calculation and analysis module analyzes the information collected by the information collection submodule to calculate the current vehicle speed required; In step S32, the calculation formula for predicting the accident is: Among them, t is the time value of the predicted accident at the current speed, t>0, C is the distance between the rider and the object measured at the current position, V1 is the original speed of the rider, V2 is the speed of the object whose trajectory intersects with the rider per second, and N is the probability of an accident between the rider and the identified object at the same time. When V1>V2, the accident must satisfy both formula 1 and formula 2. When V2>V1, the accident must satisfy both formula 1 and formula 3. ε is the conversion coefficient. The faster the predicted time of an accident, the higher the possibility of an accident. In step S33, the calculation formula of the current vehicle required speed V0 is: Among them, V0 is the speed that can avoid accidents. The identified danger level W and the probability N of an accident between the rider and the identified object at the same time are inversely proportional to the current vehicle speed V0. The higher the danger level, the slower the vehicle speed V0. <V1。 2. The big data-based perception recognition storage terminal system according to claim 1, characterized in that: The step S4 further comprises the following steps: Step S41: the information receiving submodule receives the transmitted instruction and the information of the analysis result; Step S42: the information identification submodule identifies the current dangerous situation based on the received data and responds accordingly; Step S43: According to the response, the execution control module controls the vehicle.

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