Unmanned operating vehicle emergency monitoring method and system

Abstract

The invention discloses an unmanned operating vehicle emergency monitoring method and system, and the method comprises the steps: S1, monitoring the order normality / cancel condition of an unmanned vehicle of an order initiator, and obtaining a first monitoring result; s2, when the first monitoring result is normal, classifying service types to obtain a second monitoring result; s3, collecting abnormal signals in the journey based on the second monitoring result; s4, abnormal signals are received and processed, and a third monitoring result is obtained; and S5, realizing unmanned operating vehicle emergency monitoring by combining the second monitoring result and the third monitoring result.

Description

Technical Field

[0001] The present invention relates to the technical field of state monitoring, and in particular to a method and system for monitoring emergencies of unmanned operating vehicles. Background Art

[0002] While unmanned operating vehicles improve transportation efficiency and safety, their emergency monitoring systems still face a series of challenges and shortcomings, which are mainly reflected in the following aspects:

[0003] In complex environments, such as crowded areas, poor lighting conditions or obstructions, the vehicle's vision, radar and other sensor systems may find it difficult to accurately identify the location of passengers or cargo, affecting the efficiency and experience of picking up passengers. The current human-computer interaction interface of driverless vehicles may not be intuitive and easy to use, especially for elderly passengers or users with unskilled technology. How to easily and quickly complete operations such as summoning a vehicle, confirming identity and indicating the loading location remains a major challenge. Ensuring that the identity of the person being picked up is accurate while protecting personal privacy and avoiding information leakage is a problem that the monitoring system needs to balance. The existing verification mechanism may have loopholes and may cause passengers to worry about privacy violations in actual operations; vehicles need to be able to effectively identify and respond to abnormal behaviors of passengers, such as illegal intrusion and improper use of on-board facilities, but current technology may have blind spots in real-time monitoring and judging of such behaviors; in the face of different loading requirements (such as packages of various sizes and shapes), as well as special needs of passengers (such as assistance in getting on and off the vehicle for people with limited mobility), the monitoring system and the vehicle's automated adjustment capabilities still need to be improved; in the event of emergencies during the pick-up process, such as sudden health problems of passengers, sudden external interference, etc., how to quickly identify and take appropriate measures, the current monitoring system and background support may not be perfect. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method for monitoring emergencies of unmanned operating vehicles, the method comprising:

[0005] Step S1, monitoring the normal / cancelled status of the order initiator's unmanned vehicle to obtain a first monitoring result;

[0006] Step S2: When the first monitoring result is normal, classify the service type to obtain a second monitoring result;

[0007] Step S3, based on the second monitoring result, collecting abnormal signals during the trip;

[0008] Step S4, receiving an abnormal signal and performing emergency processing to obtain a third monitoring result;

[0009] Step S5: combining the second monitoring result and the third monitoring result to realize emergency event monitoring of unmanned operating vehicles.

[0010] Optionally, in step S1, the process of monitoring the normal / cancelled status of the order initiator's driverless vehicle to obtain the first monitoring result specifically includes:

[0011] Follow up the order of the order initiator's driverless vehicle in real time and obtain its order status;

[0012] Obtain the origin and destination of the order initiator, and plan the route to the origin and destination of the order;

[0013] When the order status is normal, opening the order service to the order initiator;

[0014] When the order status is canceled, the order is ended, nearby orders are searched, and the order is followed up in real time.

[0015] Optionally, in step S3, the abnormal signals during the trip specifically include: route change request abnormal signal, destination change request abnormal signal, vehicle failure abnormal signal, environment abnormal signal, platform signal abnormality, personnel abnormal signal and cargo abnormal signal.

[0016] Optionally, in step S4, the process of receiving the abnormal signal and performing emergency processing to obtain the third monitoring result specifically includes:

[0017] Receive abnormal signals and classify them into: abnormal passenger signals, abnormal vehicle signals, abnormal platform signals, and abnormal cargo signals;

[0018] When abnormal signals from the passengers are detected, SOS is activated;

[0019] When the abnormal signal of the vehicle is detected, the order is stopped and the vehicle system is tested;

[0020] When abnormal signals from the platform are detected, the order is stopped and the platform connection test is performed;

[0021] When the abnormal signal of the goods is detected, the order is interrupted and a message of the abnormal goods is sent to the order initiator, and the platform is connected to manually handle the abnormality.

[0022] The present invention also discloses an unmanned operating vehicle emergency monitoring system, the system comprising: a state monitoring module, a service classification module, an abnormality collection module, an abnormality processing module and an order summary module;

[0023] The status monitoring module is used to monitor the normal / cancelled status of the order of the order initiator's unmanned vehicle to obtain a first monitoring result;

[0024] The service classification module is used to classify the service type and obtain the second monitoring result when the first monitoring result is normal;

[0025] The abnormality collection module is used to collect abnormal signals in the trip based on the second monitoring result;

[0026] The abnormality processing module is used to receive abnormal signals and perform emergency processing to obtain a third monitoring result;

[0027] The order summary module is used to combine the second monitoring result and the third monitoring result to realize emergency event monitoring of unmanned operating vehicles.

[0028] Optionally, the workflow of the status monitoring module specifically includes:

[0029] Follow up the order of the order initiator's driverless vehicle in real time and obtain its order status;

[0030] Obtain the origin and destination of the order initiator, and plan the route to the origin and destination of the order;

[0031] When the order status is normal, opening the order service to the order initiator;

[0032] When the order status is canceled, the order is ended, nearby orders are searched, and the order is followed up in real time.

[0033] Optionally, in the abnormal collection module, the abnormal signals in the trip specifically include: route change request abnormal signal, destination change request abnormal signal, vehicle failure abnormal signal, environment abnormal signal, platform signal abnormality, personnel abnormal signal and cargo abnormal signal.

[0034] Optionally, the workflow of the abnormality collection module specifically includes:

[0035] Receive abnormal signals and classify them into: abnormal passenger signals, abnormal vehicle signals, abnormal platform signals, and abnormal cargo signals;

[0036] When abnormal signals from the passengers are detected, SOS is activated;

[0037] When the abnormal signal of the vehicle is detected, the order is stopped and the vehicle system is tested;

[0038] When abnormal signals from the platform are detected, the order is stopped and the platform connection test is performed;

[0039] When the abnormal signal of the goods is detected, the order is interrupted and a message of the abnormal goods is sent to the order initiator, and the platform is connected to manually handle the abnormality.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The pick-up process of the present invention reduces human errors, such as accurate navigation to the passenger location and automatic loading of goods, which speeds up the boarding and disembarking of passengers and improves the overall transportation efficiency; the massive data collected by the monitoring system can provide in-depth analysis for operators, help optimize operation strategies, predict maintenance needs, and identify service hotspots, thereby continuously improving service quality; in the loading process, accurate cargo monitoring and intelligent loading solutions reduce the risk of damage, ensuring that the goods are delivered on time and on demand, which is especially suitable for the transportation of high-value or sensitive goods. Based on multi-level dynamic link selection, the unmanned vehicle terminal and the unmanned vehicle operation management platform transmit information, thereby improving the reliability and fault tolerance of the unmanned vehicle in the event of an emergency, and improving traffic safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0043] Figure 1 This is a method step diagram of a method for monitoring emergencies of unmanned commercial vehicles according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] Embodiment 1

[0046] A method for monitoring emergencies of unmanned operating vehicles, such as Figure 1As shown, the method includes: step S1, monitoring the normal / cancelled status of the order of the order initiator's unmanned vehicle, and obtaining the first monitoring result. Specifically, it includes: real-time follow-up of the order of the order initiator's unmanned vehicle to obtain its order status; obtaining the starting place and destination of the order initiator, and planning the path to the starting place and destination of the order; in this embodiment, the planning path adopts the dynamic extended neighborhood ant colony algorithm, and adopts the dynamic extended neighborhood method in the ant search method, and defines a new pheromone calculation method and incremental rules, while obtaining a better convergence path length, reducing the number of path turning points and the number of path nodes; introducing an adaptive adjustment factor to improve the heuristic function, improve the global search ability of the algorithm, and set an iteration threshold to improve the convergence speed of the algorithm; propose a path node dual optimization strategy to further optimize the planned path and improve the overall quality of the path. Simulation experiments in grid maps of different complexity and different scales show that the path planned by the DENACO algorithm is better, the number of path turning points is reduced, the convergence speed is accelerated, and the number of path nodes is significantly reduced, indicating that the algorithm has higher feasibility and applicability.

[0047] When the order status is normal, the order service is opened to the order initiator; the order service includes: door opening request and trunk opening request; when the order status is canceled, the order is ended, nearby orders are searched, and the order is followed up in real time.

[0048] Step S2: When the first monitoring result is normal, classify the service type to obtain the second monitoring result. Here, the service type includes passenger service and cargo service. If the order is for passengers to board the bus, monitor whether the passengers are wearing seat belts. If the order is for cargo to board the bus, remind the order initiator to place the cargo correctly to reduce the probability of cargo abnormality.

[0049] Step S3: Based on the second monitoring result, collect abnormal signals in the trip; the abnormal signals in the trip specifically include: route change request abnormal signal, destination change request abnormal signal, vehicle failure abnormal signal, environment abnormal signal, platform signal abnormality, personnel abnormal signal and cargo abnormal signal.

[0050] Specifically, for the abnormal signal of route change request, the following operations are performed: receiving the abnormal signal of route change request, accepting the route, and driving the unmanned vehicle according to the route plan; it is worth mentioning here that if the order initiator initiates a waypoint request, accept the waypoint, and use the improved RR-Connect algorithm to plan the path, specifically including: setting the waypoint as the target sampling point so that the RR-Connect algorithm can quickly search for the waypoint. In this algorithm, the threshold of target bias sampling is set to P, and a random value PR is generated. The value range of PR is 0-1, and the target bias sampling rule is defined as:

[0051]

[0052] The expansion space is a map with a fixed range. The expansion is performed in a plane coordinate system with the lower left vertex of the map as the origin of the coordinate system. The starting point and the end point are the root nodes of expansion tree 1 and expansion tree 2 respectively. After setting the coordinates of the starting point and the end point, the two trees start to expand from their respective root nodes.

[0053] After target bias sampling, expansion tree 1 generates sampling point FR1, and then finds the node closest to sampling point FR1 on expansion tree 1 as the parent node of the new node to be expanded. The node closest to the sampling point FR1 is used to obtain the final expansion direction through the combined effect of the force direction of the artificial potential field and the target bias sampling direction:

[0054] θ(P)=arctan(FR(y)-F near (y)

[0055] FR(x)-F near (x)

[0056] θ=θ(P)+θ(F)

[0057] The path planning result is obtained based on the expansion direction.

[0058] Receive a destination change request exception signal and re-plan the route from the current vehicle location.

[0059] When receiving an abnormal vehicle failure signal, first determine whether it is a vehicle hardware problem or a vehicle software problem; if it is a hardware problem, stop the order immediately, find the nearest parking spot nearby, notify the platform, and wait for rescue; if it is a software problem, determine whether the platform can still be contacted. If it can still be connected to the platform, issue a platform alarm; if it cannot be contacted, stop the order and find a nearby parking spot to park.

[0060] When an abnormal environment signal is received, it is determined whether the abnormality is caused by a traffic participant, road infrastructure, weather environment, or traffic condition.

[0061] When the platform signal is abnormal, if the network is abnormal, the screen display is abnormal, and the data transmission is abnormal, an alarm signal is sent. When the network is not connected, the alarm signal is sent first.

[0062] Based on this embodiment, when an emergency occurs, due to the damage of various sensors and the instability of the network, the unmanned vehicle is in a weak network environment and the passengers cannot call for help. In order to ensure that the abnormal signal can be transmitted reliably, the present invention uses a dynamic routing algorithm to achieve reliable transmission. The specific method includes:

[0063] When a terminal (defined as the driverless vehicle terminal in this embodiment) sends an alarm signal to another terminal (driverless vehicle operation management platform), the server automatically selects the transport layer protocol (TCP / UDP) according to the connection status of the transmission node;

[0064] In a weak network environment, due to limited network bandwidth, unstable connection and lack of domain name system (DNS), in order to achieve efficient communication between the driverless vehicle terminal and the driverless vehicle operation and management platform, a dynamic routing table needs to be established. The specific steps are as follows: 1. Select the central node: select a superior unit as the central node, which is responsible for receiving and managing the network information of other secondary nodes. The multi-active mechanism ensures the stability of the network connection of the central node and improves the reliability and availability of the system. 2. Set up secondary nodes: set other units as secondary nodes, which are responsible for reporting their own network status to the central node. 3. Heartbeat reporting mechanism: set a scheduled task so that each secondary node regularly sends heartbeat data to the central node. The heartbeat data mainly includes the IP address, unique identifier and online status of the node. 4. Dynamic routing table construction: the central node dynamically generates and maintains the routing table based on the received heartbeat data. The routing table records the network status and reachability of each secondary node. 5. Routing table delivery mechanism: the central node regularly delivers the generated dynamic routing table to each secondary node. After receiving the routing table, each secondary node updates its own routing information to ensure the optimal selection of the communication path. Through the above mechanism, even in poor network conditions, each node can maintain effective communication connection to ensure accurate transmission and efficient processing of information. Based on the principle of dynamic programming, the routing breadth-first traversal algorithm is used to calculate the global optimal path for communication between the driverless vehicle terminal and the driverless vehicle operation management platform.

[0065] When a personnel abnormal signal and a cargo abnormal signal are received, the abnormal signal is sent to step S4.

[0066] Step S4, receiving abnormal signals and handling emergencies to obtain a third monitoring result; specifically including: receiving abnormal signals and classifying the abnormal signals: abnormal passenger signals, abnormal vehicle signals, abnormal platform signals, and abnormal cargo signals; when the abnormal passenger signal is detected, turning on the SOS for help; when the abnormal vehicle signal is detected, stopping the order and performing a vehicle system test; when the platform abnormal signal is detected, stopping the order and performing a platform connection test; when the abnormal cargo signal is detected, interrupting the order and sending a cargo abnormality message to the order initiator, and connecting to the platform to manually handle the abnormality.

[0067] Step S5: combining the second monitoring result and the third monitoring result to realize emergency event monitoring of unmanned operating vehicles.

[0068] According to the classification result of step S2 and the monitoring result of step S4, in the absence of abnormal information, it is determined whether passengers or goods get off the vehicle after arriving at the destination, thereby realizing the monitoring of emergencies of this unmanned operating vehicle.

[0069] Embodiment 2

[0070] An unmanned operating vehicle emergency monitoring system, the system comprising: a status monitoring module, a service classification module, an abnormality collection module, an abnormality processing module and an order summary module;

[0071] The state monitoring module is used to monitor the normal / cancelled status of the order initiator's unmanned vehicle and obtain the first monitoring result. Specifically, it includes: real-time follow-up of the order initiator's unmanned vehicle order to obtain its order status; obtain the starting place and destination of the order initiator, and plan the path to the starting place and destination of the order; in this embodiment, the planning path adopts the dynamic extended neighborhood ant colony algorithm, and adopts the dynamic extended neighborhood method in the ant search method, and defines a new pheromone calculation method and incremental rules, while obtaining a better convergence path length, reducing the number of path turning points and the number of path nodes; introducing an adaptive adjustment factor to improve the heuristic function, improve the global search ability of the algorithm, and set an iteration threshold to improve the convergence speed of the algorithm; propose a path node dual optimization strategy to further optimize the planned path and improve the overall quality of the path. Simulation experiments in grid maps of different complexity and different scales show that the path planned by the DENACO algorithm is better, the number of path turning points is reduced, the convergence speed is accelerated, and the number of path nodes is significantly reduced, indicating that the algorithm has higher feasibility and applicability.

[0072] When the order status is normal, the order service is opened to the order initiator; the order service includes: door opening request and trunk opening request; when the order status is canceled, the order is ended, nearby orders are searched, and the order is followed up in real time.

[0073] The service classification module is used to classify the service type to obtain the second monitoring result when the first monitoring result is normal.

[0074] The service types here include passenger service and cargo service. If the order is for passengers to board the vehicle, the passenger's seat belt will be monitored. If the order is for cargo to board the vehicle, the order initiator will be reminded to place the cargo correctly to reduce the probability of cargo abnormality.

[0075] The abnormality collection module is used to collect abnormal signals in the trip based on the second monitoring result; the abnormal signals in the trip specifically include: route change request abnormal signal, destination change request abnormal signal, vehicle failure abnormal signal, environment abnormal signal, platform signal abnormality, personnel abnormal signal and cargo abnormal signal.

[0076] Specifically, for the abnormal signal of route change request, the following operations are performed: receiving the abnormal signal of route change request, accepting the route, and driving the unmanned vehicle according to the route plan; it is worth mentioning here that if the order initiator initiates a waypoint request, accept the waypoint, and use the improved RR-Connect algorithm to plan the path, specifically including: setting the waypoint as the target sampling point so that the RR-Connect algorithm can quickly search for the waypoint. In this algorithm, the threshold of target bias sampling is set to P, and a random value PR is generated. The value range of PR is 0-1, and the target bias sampling rule is defined as:

[0077]

[0078] The expansion space is a map with a fixed range. The expansion is performed in a plane coordinate system with the lower left vertex of the map as the origin of the coordinate system. The starting point and the end point are the root nodes of expansion tree 1 and expansion tree 2 respectively. After setting the coordinates of the starting point and the end point, the two trees start to expand from their respective root nodes.

[0079] After target bias sampling, expansion tree 1 generates sampling point FR1, and then finds the node closest to sampling point FR1 on expansion tree 1 as the parent node of the new node to be expanded. The node closest to the sampling point FR1 is used to obtain the final expansion direction through the combined effect of the force direction of the artificial potential field and the target bias sampling direction:

[0080] θ(P)=arctan(FR(y)-F near (y)

[0081] FR(x)-F near (x)

[0082] θ=θ(P)+θ(F)

[0083] The path planning result is obtained based on the expansion direction.

[0084] Receive a destination change request exception signal and re-plan the route from the current vehicle location.

[0085] When receiving an abnormal vehicle failure signal, first determine whether it is a vehicle hardware problem or a vehicle software problem; if it is a hardware problem, stop the order immediately, find the nearest parking spot nearby, notify the platform, and wait for rescue; if it is a software problem, determine whether the platform can still be contacted. If it can still be connected to the platform, issue a platform alarm; if it cannot be contacted, stop the order and find a nearby parking spot to park.

[0086] When an abnormal environment signal is received, it is determined whether the abnormality is caused by a traffic participant, road infrastructure, weather environment, or traffic condition.

[0087] When the platform signal is abnormal, if the network is abnormal, the screen display is abnormal, and the data transmission is abnormal, an alarm signal is sent. When the network is not connected, the alarm signal is sent first.

[0088] When a personnel abnormal signal and a cargo abnormal signal are received, the abnormal signal is sent to step S4.

[0089] The exception handling module is used to receive abnormal signals and handle emergencies to obtain a third monitoring result; specifically, it includes: receiving abnormal signals and classifying the abnormal signals: passenger abnormal signals, vehicle abnormal signals, platform abnormal signals, and cargo abnormal signals; when the passenger abnormal signal is detected, S OS is activated for help; when the vehicle abnormal signal is detected, the order is stopped and the vehicle system is tested; when the platform abnormal signal is detected, the order is stopped and the platform connection test is performed; when the cargo abnormal signal is detected, the order is interrupted and a cargo abnormal message is sent to the order initiator, and the platform is connected to manually handle the abnormality.

[0090] The order summary module is used to combine the second monitoring result and the third monitoring result to realize emergency event monitoring of unmanned operating vehicles.

[0091] According to the above classification results and monitoring results, in the absence of abnormal information, it is determined whether passengers or goods get off the vehicle after arriving at the destination, thereby realizing the monitoring of emergencies of this unmanned operating vehicle.

[0092] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for monitoring emergencies of unmanned operating vehicles, characterized in that: The method comprises: Step S1, monitoring the normal / cancelled status of the order of the unmanned vehicle of the order initiator to obtain a first monitoring result; Step S2: When the first monitoring result is normal, classify the service type to obtain a second monitoring result; Step S3, based on the second monitoring result, collecting abnormal signals during the trip; Step S4, receiving an abnormal signal and performing emergency processing to obtain a third monitoring result; Step S5: combining the second monitoring result and the third monitoring result to realize emergency event monitoring of unmanned operating vehicles.

2. The method for monitoring emergencies of unmanned operating vehicles according to claim 1, characterized in that: In step S1, the process of monitoring the normal / cancelled status of the order of the driverless vehicle of the order initiator to obtain the first monitoring result specifically includes: Follow up the order of the order initiator's driverless vehicle in real time and obtain its order status; Obtain the origin and destination of the order initiator, and plan the route to the origin and destination of the order; When the order status is normal, opening the order service to the order initiator; When the order status is canceled, the order is ended, nearby orders are searched, and the order is followed up in real time.

3. The method for monitoring emergencies of unmanned operating vehicles according to claim 1, characterized in that: In step S3, the abnormal signals in the trip specifically include: route change request abnormal signal, destination change request abnormal signal, vehicle failure abnormal signal, environment abnormal signal, platform signal abnormality, personnel abnormal signal and cargo abnormal signal.

4. The method for monitoring emergencies of unmanned operating vehicles according to claim 3, characterized in that: In step S4, the process of receiving the abnormal signal and performing emergency processing to obtain the third monitoring result specifically includes: Receive abnormal signals and classify them into: abnormal passenger signals, abnormal vehicle signals, abnormal platform signals, and abnormal cargo signals; When abnormal signals from the passengers are detected, SOS is activated; When the abnormal signal of the vehicle is detected, the order is stopped and the vehicle system is tested; When abnormal signals from the platform are detected, the order is stopped and the platform connection test is performed; When the abnormal signal of the goods is detected, the order is interrupted and a message of the abnormal goods is sent to the order initiator, and the platform is connected to manually handle the abnormality.

5. A monitoring system for emergencies of unmanned operating vehicles, the monitoring system being used to implement the monitoring method according to any one of claims 1 to 4, characterized in that: The system includes: status monitoring module, service classification module, exception collection module, exception handling module and order summary module; The status monitoring module is used to monitor the normal / cancelled status of the order of the order initiator's unmanned vehicle to obtain a first monitoring result; The service classification module is used to classify the service type and obtain the second monitoring result when the first monitoring result is normal; The abnormality collection module is used to collect abnormal signals in the trip based on the second monitoring result; The abnormality processing module is used to receive abnormal signals and perform emergency processing to obtain a third monitoring result; The order summary module is used to combine the second monitoring result and the third monitoring result to realize emergency event monitoring of unmanned operating vehicles.

6. The unmanned vehicle emergency monitoring system according to claim 5, characterized in that: The workflow of the status monitoring module specifically includes: Follow up the driverless vehicle orders of the order initiator in real time and obtain their order status; Obtain the origin and destination of the order initiator, and plan the route to the origin and destination of the order; When the order status is normal, opening the order service to the order initiator; When the order status is canceled, the order is ended, nearby orders are searched, and the order is followed up in real time.

7. The unmanned vehicle emergency monitoring system according to claim 5, characterized in that: In the abnormal collection module, the abnormal signals in the trip specifically include: route change request abnormal signal, destination change request abnormal signal, vehicle failure abnormal signal, environment abnormal signal, platform signal abnormality, personnel abnormal signal and cargo abnormal signal.

8. The unmanned vehicle emergency monitoring system according to claim 5, characterized in that: The workflow of the abnormality collection module specifically includes: Receive abnormal signals and classify them into: abnormal passenger signals, abnormal vehicle signals, abnormal platform signals, and abnormal cargo signals; When abnormal signals from the passengers are detected, SOS is activated; When the abnormal signal of the vehicle is detected, the order is stopped and the vehicle system is tested; When abnormal signals from the platform are detected, the order is stopped and the platform connection test is performed; When the abnormal signal of the goods is detected, the order is interrupted and a message of the abnormal goods is sent to the order initiator, and the platform is connected to manually handle the abnormality.

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