Safety rescue method and system based on intelligent helmet

Abstract

The invention provides a safety rescue method and system based on an intelligent helmet, and relates to the technical field of computers, and the method comprises the steps: obtaining sensor data corresponding to a plurality of sensors in the intelligent helmet worn by a target user; if it is determined that the target user is in a tumble state according to the sensor data, displaying an alarm page on an application platform logged in by the target user, and obtaining field data of an accident corresponding to the tumble state; if the selection operation of the alarm page meets the trigger condition, the obtained field data is reported, under the condition that it is determined that the accident needs to be upgraded, a claim settlement work order with a standard structure format is generated, the claim settlement work order corresponds to the field data, and the claim settlement work order corresponding to the field data is used for automatic auditing. And skipping to an insurance platform system after the automatic auditing is passed, so as to carry out claim settlement operation on the claim settlement work order through the insurance platform system. The claim settlement efficiency and reliability can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a safety rescue method and system based on a smart helmet. Background Art

[0002] Smart helmets are widely used in various delivery scenarios.

[0003] Related technologies use gyroscopes and accelerometers to detect abnormal posture, but these are susceptible to interference from bumpy rides, resulting in low recall rates and high false alarm rates. One-touch alarm functions only provide a one-way call for help. When generating insurance claim tickets, manual reporting is required, resulting in low claim processing efficiency and reliability. Summary of the Invention

[0004] The purpose of the present disclosure is to provide a safety rescue method and system based on a smart helmet, thereby overcoming, at least to a certain extent, the problems of low claims settlement efficiency and low reliability caused by the limitations and defects of related technologies.

[0005] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by practice of the present disclosure.

[0006] According to one aspect of the present disclosure, a safety rescue method based on a smart helmet is provided, comprising: obtaining sensor data corresponding to multiple sensors in a smart helmet worn by a target user; determining that the target user is in a fall state based on the sensor data, displaying an alarm page on an application platform logged in by the target user, and obtaining on-site data of the accident corresponding to the fall state; if the selection operation on the alarm page meets the triggering condition, reporting the obtained on-site data, and generating a claim work order with a standard structure format when it is determined that the accident needs to be upgraded; the claim work order corresponds to the on-site data, and the claim work order corresponding to the on-site data is used for automated review, and after the automated review is passed, jumps to the insurance platform system, so as to perform a claim operation on the claim work order through the insurance platform system.

[0007] In an exemplary embodiment of the present disclosure, the generating of a claim work order with a standard structure format includes: automatically acquiring one or more of the following on-site data to generate the claim work order with a standard structure format corresponding to the on-site data: sensor data, timestamp data, and trajectory data of the smart helmet.

[0008] In an exemplary embodiment of the present disclosure, generating a claim work order having a standard structure format includes:

[0009] Acquire audio data and / or video data corresponding to the accident scene to generate a claim work order corresponding to the scene data.

[0010] In an exemplary embodiment of the present disclosure, the method further includes: responding to the target user's selection operation of the target option in the alarm page, jumping to the details page of the target option; responding to a click operation on the report control on the details page, generating a claim work order corresponding to the on-site data, the claim work order corresponding to the on-site data is used for automated review, and after the review is passed, jumping to the insurance platform system to perform a claim operation on the claim work order through the insurance platform system.

[0011] In an exemplary embodiment of the present disclosure, the trigger condition includes that no selection operation acting on any option in the alarm page is detected, or that a selection operation acting on a target option in the alarm page is detected.

[0012] In an exemplary embodiment of the present disclosure, the determination that the accident needs to be upgraded includes: determining a duration during which the selection operation acting on any option is not detected, and calling a first terminal or a server to which the target person belongs according to the duration; determining whether an accident has occurred based on the first terminal or the server, and in the event that an accident has occurred and the accident level of the accident is the target level, determining that the accident corresponding to the fall state needs to be upgraded; wherein the upgrade process is triggered by a second terminal of the administrator corresponding to the target user.

[0013] In an exemplary embodiment of the present disclosure, determining whether an accident has occurred through the first terminal or the server includes: calling the first terminal to analyze the accident work order corresponding to the accident to determine whether an accident has occurred; or, determining the audio data and / or video data corresponding to the accident scene through the server, and identifying the audio data and / or video data to determine whether an accident has occurred.

[0014] In an exemplary embodiment of the present disclosure, the automatic acquisition of the following one or more on-site data to generate the claim work order with a standard structure format corresponding to the on-site data includes: determining the accident level based on the collision audio data and / or barometer drop data in the sensor data, determining the accident time based on the timestamp data in the on-site data, and determining the accident location based on the trajectory data in the on-site data; automatically filling in the work order template according to the accident time, the accident location and the accident level to generate the claim work order with a standard structure format.

[0015] In an exemplary embodiment of the present disclosure, the claim work order corresponding to the on-site data is used for automated review and jumps to the insurance platform system after the automated review is passed, including: performing image recognition based on the image data in the on-site data to determine the collision traces, and determining the reference accident level based on the audio data and the image data and sensor data; triggering an intelligent question-and-answer session with the target user to determine the content of the conversation, and updating the claim work order based on one or more of the conversation content, reference data of the accident, collision traces, and reference accident levels to obtain an updated claim work order; if the updated claim work order complies with the claim rules, it is determined that the review is passed, and automatically jumps to the insurance platform system to perform the claim operation through the insurance platform system.

[0016] In an exemplary embodiment of the present disclosure, the claim work order is updated according to one or more of the conversation content, reference data of the accident, collision traces, and reference accident levels to obtain an updated claim work order, including: extracting keywords from the conversation content to obtain keywords related to the accident; and modifying or supplementing the claim work order based on one or more of the keywords, accident solutions, collision traces, and reference accident levels to obtain an updated claim work order.

[0017] In an exemplary embodiment of the present disclosure, the sensor data includes acceleration and posture angle; and determining that the target user is in a falling state based on the sensor data includes: when the acceleration in multiple consecutive frames of sensor data is greater than an acceleration threshold and the posture angle is greater than a posture angle threshold, determining that the target user is in a falling state.

[0018] According to one aspect of the present disclosure, a safety rescue system based on a smart helmet is provided, comprising: a smart helmet, which is equipped with multiple sensors for collecting sensor data of a target user; an application platform, which is used to display an alarm page when it is determined that the target user is in a fall state based on the sensor data, and obtain on-site data of the accident corresponding to the fall state; if the selection operation on the alarm page meets the trigger condition, the on-site data is reported, and when it is determined that the accident needs to be upgraded, a claim work order with a standard structure format is generated, the claim work order corresponds to the on-site data, and the claim work order corresponding to the on-site data is used for automated review, and after the automated review is passed, it jumps to the insurance platform system to perform a claim operation on the claim work order through the insurance platform system.

[0019] In the technical solutions provided in some embodiments of the present disclosure, on the one hand, multiple sensors are used to jointly detect whether a fall has occurred and the accident level is automatically determined based on the sensor data, thereby avoiding the low recall rate and high false alarm rate caused by the related art fall detection based on acceleration sensors and the judgment of abnormal posture by gyroscopes and accelerometers, thereby improving the accuracy and reliability of fall status detection. On the other hand, the insurance platform system can be automatically triggered by the sensor data of the smart helmet to achieve a linkage response between multiple platforms, avoiding the limitation of the smart helmet that can only provide one-way rescue, enabling timely rescue and improving real-time performance. On the other hand, by automatically triggering the insurance platform system for claims, the problem of users manually filling in accident information and submitting on-site accident data, which is time-consuming and has the risk of information tampering, is avoided. This reduces user operations, increases the authenticity of information, improves the reliability and efficiency of insurance claims, and reduces claims costs.

[0020] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0022] Figure 1 A schematic diagram schematically illustrates a safe rescue method based on a smart helmet according to an embodiment of the present disclosure.

[0023] Figure 2 A schematic diagram schematically illustrates an alarm page in an embodiment of the present disclosure.

[0024] Figure 3 The following schematically illustrates a flow chart for automated review of claim settlement work orders in an embodiment of the present disclosure.

[0025] Figures 4A to 4B A schematic diagram schematically illustrates click target options according to an embodiment of the present disclosure.

[0026] Figure 5 The flowchart of fall detection, rescue and insurance linkage according to an embodiment of the present disclosure is schematically shown.

[0027] Figure 6 A simplified flowchart schematically illustrates the collision detection function of an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present disclosure will be more comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or that other methods, components, devices, steps, etc. may be employed. In other cases, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0029] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0030] Smart helmets are widely used in user safety monitoring, but related technologies often focus on single functions. For example, they rely on mobile phone GPS to determine whether a rider has moved or if an order has been stalled for an extended period, requiring manual phone calls for verification. This results in low timeliness and accuracy. Furthermore, the insurance claims process requires users to manually fill out accident information, leading to issues such as delays, distortion of insurance information and documents, and even the possibility of fabricating accidents and falsifying evidence by exploiting information opacity. This also results in insufficient post-accident processing capabilities. Accelerometer-based fall detection uses gyroscopes and accelerometers to detect posture anomalies, but is susceptible to interference from bumpy rides, resulting in low recall rates and high false alarm rates. Single-point alarm systems, such as the SOS button alarm function on fire helmets, only provide a one-way call for help and lack a platform-level coordinated response mechanism. Manual insurance reporting requires riders to manually fill out accident information and submit supporting documents, a time-consuming process that carries the risk of information tampering. Single sensors have low detection accuracy (false alarm rates >50%) and struggle to distinguish between normal riding and accidents. Accident information is disconnected from insurance platforms, requiring multiple manual intervention steps, resulting in low efficiency and high information distortion. The platform also lacks real-time rescue dispatch capabilities, leading to delayed accident response and missed rescue windows, potentially endangering rider safety.

[0031] In order to solve the technical problems in the related art, an embodiment of the present disclosure provides a safety rescue method based on a smart helmet, which can be applied to the delivery scenario of any type of items.

[0032] Next, refer to Figure 1 As shown, each step in the smart helmet-based safety rescue method in the embodiment of the present disclosure is described in detail.

[0033] In step S110, sensor data corresponding to multiple sensors in the smart helmet worn by the target user is obtained.

[0034] In some embodiments of the present disclosure, the target user may be a user wearing a smart helmet, for example, a food delivery rider or any other type of user. The target user is taken as an example of a food delivery rider. A plurality of sensors may be configured in the smart helmet, and the plurality of sensors may include one or more of an accelerometer, a gyroscope, a microphone, and a barometer. Based on this, the sensor data may include one or more of acceleration, attitude angle, collision audio data, and barometer drop data. After the target user places an order on the application platform, the application platform will assign a delivery person. The application platform can be any type of delivery platform. The target user can drive an electric vehicle to deliver items, and the items delivered can be any type of compliant items, for example, food purchased and delivered on the application platform or items delivered only through the application platform.

[0035] In step S120, it is determined based on the sensor data that the target user is in a fall state, an alarm page is displayed on the application platform where the target user logs in, and on-site data of the accident corresponding to the fall state is obtained.

[0036] In the embodiment of the present disclosure, it is first possible to determine whether the target user is in a falling state based on the sensor data. For example, it is possible to determine whether the target user is in a falling state based on the acceleration and attitude angle in the sensor data. When the acceleration in multiple consecutive frames of sensor data is greater than the acceleration threshold and the attitude angle is greater than the attitude angle threshold, it is determined that the target user is in a falling state. The sensor data can be input into the edge computing unit. If the acceleration in three consecutive frames of sensor data is greater than the acceleration threshold and the attitude angle is greater than the attitude angle threshold, it can be determined that the target user is in a falling state. The acceleration threshold and the state angle threshold can be determined according to actual needs. For example, the acceleration threshold can be 5g and the attitude angle threshold can be 60 degrees.

[0037] If the acceleration in three consecutive frames of sensor data exceeds 5g and the attitude angle exceeds 60 degrees, the target user is determined to have fallen. At this point, an alert page can be displayed on the application platform logged in by the target user. The application platform logged in by the target user can be the delivery platform that matches the delivery order. The alert page can be displayed on the application platform in the form of a floating window.

[0038] refer to Figure 2 As shown in , the alert page may include multiple options, such as a rescue option 210, a self-resolve option 220, and a not-fallen option 230. The rescue option 210 is used to automatically call a medical facility in the event of a serious injury accident. The self-resolve option is used to guide the target user to upload data and generate a structured claim work order in the event of a minor accident. The not-fallen option 230 is used to indicate that the target user is in a normal driving state.

[0039] When a fall is detected, the user can also obtain on-site data of the accident. This on-site data can include sensor data and one or more of timestamp data, trajectory data, image data, and audio data. Image data can be a separate image or a picture in a video; audio data can be a separate recording or audio in a video.

[0040] In step S130, if the selection operation on the alarm page meets the triggering conditions, the acquired on-site data will be reported. If it is determined that the accident needs to be upgraded, a claim work order with a standard structure format will be generated. The claim work order corresponds to the on-site data. The claim work order corresponding to the on-site data is used for automated review, and after the automated review is passed, it jumps to the insurance platform system to perform a claim operation on the claim work order through the insurance platform system.

[0041] In the embodiment of the present disclosure, the trigger condition may include that no selection operation acting on any option in the alarm page is detected, or the trigger condition may also be that a selection operation acting on a target option in the alarm page is detected.

[0042] If the target user does not select any option on the alert page within the preset time, it can be assumed that an incident has occurred with the target user. At this time, the on-site data of the incident can be directly reported, and the need for incident escalation can be determined based on the on-site data. The preset time here can be 1 minute or 2 minutes, etc., depending on actual needs. If a selection operation is detected for the target option on the alert page, the on-site data of the incident can also be directly reported, and the need for incident escalation can be determined based on the on-site data. The target option can be a self-resolve option.

[0043] Upgraded processing means that the target user needs rescue or insurance claims and other high-level processing. In some embodiments, if the selection operation of any option is not detected, the duration of the selection operation not being detected can be determined, and the first terminal or server of the site to which the target user belongs can be called according to the duration; further, it can be determined whether an accident has occurred by calling the first terminal or server. The first terminal of the site to which the target user belongs can be the terminal of the administrator of the site associated with the application platform logged in by the target user. On-site data can be reported via SMS or beacon tower. At the same time, a time work order can be created. When the duration in the time work order is 5 minutes, it can be pushed to the first terminal of the station manager; when the duration in the time work order is 10 minutes, it can be pushed to the first terminal of the safety officer; when the duration in the time work order is 20 minutes, it can be pushed to the server associated with the application platform logged in by the target user. The server can be an intelligent system / intelligent server, and the intelligent system or intelligent server can be a digital person.

[0044] When the first terminal or the service end determines that an accident has occurred and the accident level is the target level, it is determined that the accident corresponding to the fall state needs to be upgraded; wherein the upgrade process is triggered by the second terminal of the administrator corresponding to the target user.

[0045] In some embodiments, if it is determined through the first terminal whether an accident has occurred, the on-site data of the accident can first be recorded through the first terminal, and whether an accident has occurred can be determined directly based on the on-site data. For example, in the event of an accident, the accident level of the accident can be determined based on the on-site data. The accident level is determined based on the collision audio data and / or barometer drop data collected by the microphone. The collision audio data can be a high-frequency shock wave. For example, the collision audio data and / or barometer drop data can be input into a machine learning model, the feature vectors of the collision audio data and / or barometer drop data can be extracted, and the feature vectors can be classified to predict the accident level. The accident level may include a first level and a second level, and the severity of the second level is greater than the first level. When the accident level is the target level, it can be determined that the accident corresponding to the fall state needs to be upgraded. The target level can be the second level.

[0046] In other embodiments, when determining whether an accident has occurred through a server-side such as an intelligent system or intelligent server associated with the application platform logged in by the target user, the server may first determine whether audio data and / or video data exist. If so, the server may then determine whether an accident has occurred based on the audio data and / or video data. If an accident has occurred, the server may further determine whether the accident requires escalation. For example, if the accident level is at the target level, the server may determine that the accident corresponding to the fall condition requires escalation.

[0047] In the event of an accident, the accident level can be determined based on on-site data. The accident level is determined based on one or more of the collision audio data (high-frequency shock wave), barometer drop data, driving speed, video data, and audio data collected by the microphone. The collision audio data can be a high-frequency shock wave. If the collision audio data, barometer drop data, and driving speed do not meet the threshold, and / or the image or audio corresponding to the second level of the accident level is automatically identified through video data or audio data, it can be determined that the accident corresponding to the fall state needs to be upgraded.

[0048] In addition, collision audio data, barometer drop data, driving speed, video data, and audio data can be input into a machine learning model to extract their corresponding feature vectors. These feature vectors are then fused to generate fused features. Classification is then performed based on the fused features to predict the accident level. Accident levels can include Level 1 and Level 2, with Level 2 being more severe than Level 1. If the accident level is Level 2, it can be determined that the accident corresponding to the fall condition needs to be upgraded.

[0049] It should be noted that when the incident corresponding to the fall state is upgraded, the second terminal of the manager corresponding to the target user can intervene and handle it. The second terminal can start the accident handling process and fully record the accident information and accident handling records.

[0050] If it is determined that an accident requires escalation, one or more on-site data points can be automatically acquired to generate a claim form in a standard structure format corresponding to the on-site data. The on-site data may include sensor data, timestamp data, and trajectory data from the smart helmet. The standard structure format is used to correspond to the on-site data. For example, the accident level is determined based on the sensor data in the on-site data, the accident time is determined based on the timestamp data in the on-site data, and the accident location is determined based on the trajectory data in the on-site data. The work form template is automatically populated based on the accident time, location, and level to generate a claim form. The accident level can be determined based on sensor data such as collision audio data collected by a microphone and barometer drop data. If the accident time falls within the insurance validity period and the accident location is within the coverage area, the accident time, location, and level are entered into the corresponding fields of the work form template. Combined with the target user's insurance policy information, a structured claim form corresponding to the on-site data is generated.

[0051] In addition, audio and / or video data corresponding to the accident scene can be obtained to generate a claim settlement ticket corresponding to the on-site data. This acquisition can be automatic, for example, by automatically instructing the target user's mobile phone terminal to turn on the camera, or by instructing the smart helmet's camera to turn on the camera for automatic acquisition. Alternatively, it can be by guiding the target user to upload the on-site data collected by the smart helmet or the target user's mobile terminal on the data upload page of the logged-in application platform.

[0052] If an incident is determined to require escalation, the system can automatically capture on-site data and generate a corresponding claim ticket. Alternatively, it can capture audio and / or video data from the incident scene to generate a claim ticket. The claim ticket is a standard structured format that matches the on-site data.

[0053] When generating a claim form, the accident level can be determined based on the collision audio data and / or barometer drop data in the sensor data, the accident time can be determined based on the timestamp data in the scene data, and the accident location can be determined based on the trajectory data in the scene data. Furthermore, the work form template is automatically filled in based on the accident time, location, and level to generate a claim form with a standard structure. Specifically, the claim form is first generated based on the sensor data, timestamp data, and trajectory data automatically acquired by the smart helmet.

[0054] In addition, when there is image data and / or audio data corresponding to the accident scene, data related to the type of scene data can be automatically extracted based on the audio data and / or video data corresponding to the accident scene, thereby automatically generating a claim work order based on the audio data and / or video data.

[0055] The generated claim work order with a standard structure format is used to implement the claim operation through the insurance platform system. In the embodiment of the present disclosure, after the claim work order is generated, the intelligent service end can be automatically triggered to collect evidence and retrieve the sensor data (such as acceleration curves) and on-site data such as vehicle camera video clips within a preset time before and after the incident. The preset time can be 1 minute or 30 seconds, etc. Based on this, the process of automated review of the claim work order can be as follows: Figure 3 As shown, it mainly includes the following steps:

[0056] In step S310, image recognition is performed based on the image data in the scene data to determine the collision trace, and a reference accident level is determined based on the audio data, image data, and sensor data;

[0057] In step S320, an intelligent question-and-answer session is triggered with the target user to determine the content of the conversation, and the claim settlement work order is updated based on one or more of the conversation content, the reference data of the accident, the collision traces, and the reference accident level to obtain an updated claim settlement work order;

[0058] In step S330, if the updated claim work order complies with the claim rules, it is determined to be approved and automatically jump to the insurance platform system to perform the claim operation through the insurance platform system.

[0059] Image data from the scene data can be used for image recognition based on a convolutional neural network or other model to determine collision traces, and audio data, image data, and sensor data can be input into a machine learning model to determine a reference accident level. The reference accident level refers to the accident level predicted based on the audio data, image data, and sensor data. The reference accident level can be the same as or different from the accident level predicted based on the collision audio data and barometer drop data. If they are different, the accident level can be modified based on the reference accident level.

[0060] In addition, during the automated review process of the insurance platform system, the intelligent customer service of the insurance platform system can also be triggered to conduct intelligent Q&A with the target user to achieve intelligent return visits. The intelligent Q&A method can be voice methods such as telephone or video. It should be noted that the conversation content can also include conversations with relevant personnel who are around the target user and related to the accident. Intelligent customer service can generate question information based on image data, audio data, and uploaded accident time, accident location, accident level and context information, so that the target user can generate reply information corresponding to the question information, and then generate conversation content based on the question information and reply information.

[0061] After obtaining the content of the conversation, the claim work order can be updated according to one or more of the conversation content, reference data of the accident, collision traces, and reference accident levels to obtain an updated claim work order. The reference data of the accident can be the solution of the relevant agency to the accident, such as the solution of a medical institution, the solution of a transportation agency, and so on. Based on this, the original claim work order can be updated according to objective data such as collision traces and reference accident levels determined by sensor data collected by multiple sensors on the smart helmet, as well as subjective data. The subjective data may include the content of the conversation and the reference data of the accident. Exemplarily, the conversation content can be subjected to keyword extraction to obtain keywords related to the accident; based on one or more of the keywords, solutions to the accident, collision traces, and reference accident levels, the claim work order can be modified or supplemented to obtain an updated claim work order.

[0062] Furthermore, the updated claim work order can be reviewed based on the claim rules corresponding to the target user's insurance policy information. If the updated claim work order complies with the claim rules, the review can be determined to be approved. If the automated review is passed, the claim work order can automatically jump to the insurance platform system page, and the insurance platform system can implement the claim processing for the updated claim work order. It should be noted that the jump here can be an interface jump, or it can keep the page of the application platform where the target user logs in unchanged, and control the claim work order to enter the insurance company's insurance policy processing system.

[0063] In the disclosed embodiment, when the target user does not select any option on the alert page or clicks the self-resolve option and the accident requires escalation, a claim work order with a standard structure format is automatically generated, and the insurance platform system is called to perform a claim settlement operation based on the claim work order. This can reduce manual verification operations, improve the timeliness of claims settlement, and achieve a coordinated response between multiple platforms. Accident data and the insurance platform system can be seamlessly connected and automatically synchronized, improving response speed and efficiency and enabling timely rescue. In addition, since automated claims settlement can be achieved, the process of manually filling in information is avoided, efficiency is improved, the risk of tampering is avoided, and data authenticity is improved.

[0064] In other embodiments, if it is detected that the target user has selected a target option in the alert page, the page is redirected to the details page of the target option; the target option may be a self-resolve option. Figure 4A As shown in , if the target user clicks the self-solve option, you can jump to the details page corresponding to the self-solve option. Figure 4B As shown in , the report page can be displayed in the form of a floating window. The report page may include controls for reporting and controls for not reporting. If a click operation on the control for reporting is detected, a claim work order related to the on-site data can be generated. For example, the accident level is determined based on the sensor data in the on-site data, the accident time is determined based on the timestamp in the on-site data, and the accident location is determined based on the trajectory data in the on-site data; the work order template is automatically filled in according to the accident time, accident location, and accident level to generate a claim work order. Among them, when the accident time is within the insurance validity period and the accident location is within the coverage, the accident time, accident location, and accident level are filled in the corresponding positions of the work order template, and a structured claim work order corresponding to the on-site data is generated in combination with the target user's insurance policy information.

[0065] Furthermore, after generating a claim ticket, image data from the on-site data can be used for image recognition using a convolutional neural network to identify collision traces, and the reference accident level can be determined based on the audio and image data. This can also trigger intelligent customer service to engage in intelligent Q&A with the target user, enabling intelligent follow-up visits. During this intelligent Q&A process, the intelligent customer service can generate questions based on the image and audio data, as well as uploaded accident time, location, level, and contextual information. This allows the target user to generate a response corresponding to the question, and then generate conversation content based on the question and response information.

[0066] After obtaining the conversation content, keywords can be extracted from the conversation content to obtain keywords related to the accident; based on one or more of the keywords, the accident solution, the collision traces, and the reference accident level, the claim work order can be modified or supplemented to obtain an updated claim work order. Furthermore, the updated claim work order can be reviewed based on the claim rules, and if the updated claim work order complies with the claim rules, it can be determined to have passed the review. If the review is passed, it can automatically jump to the insurance platform system page, and the insurance platform system can implement the claim operation for the updated claim work order.

[0067] In the embodiment of the present disclosure, when it is detected that the target user clicks on the self-resolve option, it is considered a minor accident. The target user can be guided to upload on-site data through the application platform or automatically obtain on-site data to generate a claim work order. Based on the obtained on-site data and interaction with the target user, the claim operation is implemented through subjective data such as the conversation content and objective data, thereby improving the timeliness and reliability of the claim.

[0068] In the case where the target user clicks the rescue option on the alarm page, the accident level can be considered a serious accident. At this time, the intelligent service end can automatically contact the medical institution to trigger the rescue, and push the real-time location and on-site data of the target user to the first terminal and / or the second terminal and the medical institution, so that the management personnel can know the details of the accident in time, ensure that the accident warning information reaches the relevant personnel in time, and ensure the timeliness of the rescue. In other embodiments, if the image data and / or audio data obtained by the mobile phone terminal used by the target user or the camera of the smart helmet, or the sensor data of the smart helmet determines that the accident level of the target user is the third level, and the third level is higher than the second level, it can be instructed to establish a communication connection with the medical institution, and automatically generate interactive content with the medical institution through timestamp data, trajectory data, image data and audio data, thereby automatically triggering the rescue, thereby improving the timeliness of the rescue.

[0069] In the disclosed embodiments, the accuracy and real-time nature of collision and fall detection are enhanced through the fusion of sensor data collected by multiple sensors deployed on the smart helmet and an intelligent hierarchical recognition algorithm. An automatic push mechanism ensures that accident warning information reaches those responsible in a timely manner, ensuring prompt rescue. Accident scene data is seamlessly integrated and automatically synchronized with the insurance platform system, reducing claims costs, improving claims efficiency, and enhancing claims reliability.

[0070] Figure 5 The flowchart of fall detection rescue and insurance linkage is shown schematically in Figure 5 As shown in , it mainly includes the following steps:

[0071] Step S502: The target user falls down.

[0072] Step S504: Display the alarm page in a floating window on the application platform.

[0073] Step S506, determine whether the person has fallen, if not, go to step S508; if so, go to step S510.

[0074] Step S508: No fall.

[0075] Step S510: If the rescue option is clicked or no option is clicked, the first terminal is notified via SMS or Parllay for verification. The first terminal can be a franchisee's terminal, such as a station manager or a security officer. If no processing is done within 20 minutes, go to step S524.

[0076] Step S512: record the accident situation through the accident work order.

[0077] Step S514: Determine whether an accident has occurred; if not, go to step S516; if yes, go to step S518.

[0078] Step S516, safe.

[0079] Step S518, determine whether an upgrade is required, if so, go to step S532; if not, go to step S520.

[0080] Step S520: Franchise disposal.

[0081] Step S522, record the accident information, such as the accident location, accident site, on-site data, and franchisee handling method, etc.

[0082] Step S524: Intelligent server access.

[0083] Step S526, determine whether there is audio or video; if so, go to step S528; if not, go to step S510.

[0084] Step S528: Determine whether an accident has occurred; if not, go to step S530. If yes, go to step S510.

[0085] Step S530, determine whether an upgrade is required, if so, go to step S532; if not, go to step S510.

[0086] Step S532: The second terminal intervenes and takes action.

[0087] Step S534: The second terminal initiates an emergency event handling process.

[0088] Step S536: Record the complete accident information and the processing record of the second terminal.

[0089] Step S538: After receiving the claim work order, the insurance platform system combines the intelligent service end and manual follow-up.

[0090] Step S540: Perform claim settlement operations based on the reported on-site data.

[0091] In the disclosed embodiment, the presence of a fall is detected jointly by sensor data from multiple sensors, and the accident level is determined based on the sensor data, thereby avoiding the problems of low recall rate and high false alarm rate caused by fall detection based on acceleration sensors and abnormal posture judgment by gyroscopes and accelerometers in related technologies, thereby improving the accuracy and reliability of fall status detection. The insurance platform system can be automatically called through the sensor data of the smart helmet to achieve a linkage response between multiple platforms, thus avoiding the limitation of the smart helmet that can only achieve one-way rescue, enabling timely rescue and improving real-time performance. By automatically calling the insurance platform system for claims, the problem of users manually filling in accident information and submitting on-site data of the accident, which is time-consuming and has the risk of information tampering, is avoided, user operations are reduced, the authenticity of information is increased, the reliability and efficiency of insurance claims are improved, and the claims cost is reduced.

[0092] Figure 6 A simplified flowchart of the collision detection function is shown schematically in Figure 6 As shown in , accident perception is first performed, and the target user's user terminal performs audio and video acquisition. When the target user is detected to have fallen, an alarm page is displayed in the form of a floating window on the target user's application platform. If no selection operation is detected for any option on the alarm page within a preset time period, or if a selection operation is detected for the self-resolve option on the alarm page, the on-site data of the accident corresponding to the fall status can be reported to the franchisee, so that the first terminal records the on-site data of the accident. For minor accidents, the accident information can be recorded directly.

[0093] In the disclosed embodiment, multimodal sensor data is collected through a smart helmet, the sensor data is fused, and high-precision accident identification is performed based on the sensor data through an intelligent grading algorithm. The instant messaging platform is linked to automatically trigger alarms / rescue instructions, and based on the structured data synchronization mechanism and the insurance platform system, automatic generation of intelligent work orders and rapid response to claims are achieved, thereby improving the timeliness of accident response, realizing full process automation of insurance processing, and authentic traceability of accident data, thereby improving the reliability and timeliness of insurance claims.

[0094] In addition, some embodiments of the present disclosure provide a safety and rescue device based on a smart helmet. The safety and rescue device based on a smart helmet may include the following modules:

[0095] A sensor data acquisition module is used to obtain sensor data corresponding to multiple sensors in the smart helmet worn by the target user;

[0096] a page display module, configured to determine, based on the sensor data, that the target user is in a fall state, display an alarm page on the application platform logged in by the target user, and obtain on-site data of the accident corresponding to the fall state;

[0097] The claim work order generation module is used to report the on-site data if the selection operation on the alarm page meets the trigger condition, and generate a claim work order with a standard structure format when it is determined that the accident needs to be upgraded. The claim work order corresponds to the on-site data. The claim work order corresponding to the on-site data is used for automated review and jumps to the insurance platform system after the automated review is passed, so that the claim operation on the claim work order can be performed through the insurance platform system.

[0098] In an exemplary embodiment of the present disclosure, the generating of a claim work order with a standard structure format includes: automatically acquiring one or more of the following on-site data to generate the claim work order with a standard structure format corresponding to the on-site data: sensor data, timestamp data, and trajectory data of the smart helmet.

[0099] In an exemplary embodiment of the present disclosure, generating a claim settlement work order having a standard structure format includes: acquiring audio data and / or video data corresponding to the accident scene to generate a claim settlement work order corresponding to the scene data.

[0100] In an exemplary embodiment of the present disclosure, the method further includes: responding to the target user's selection operation of the target option in the alarm page, jumping to the details page of the target option; responding to a click operation on the report control on the details page, generating a claim work order corresponding to the on-site data, the claim work order corresponding to the on-site data is used for automated review, and after the review is passed, jumping to the insurance platform system to perform a claim operation on the claim work order through the insurance platform system.

[0101] In an exemplary embodiment of the present disclosure, the trigger condition includes that no selection operation acting on any option in the alarm page is detected, or that a selection operation acting on a target option in the alarm page is detected.

[0102] In an exemplary embodiment of the present disclosure, the determination that the accident needs to be upgraded includes: determining a duration during which the selection operation acting on any option is not detected, and calling a first terminal or a server to which the target person belongs according to the duration; determining whether an accident has occurred based on the first terminal or the server, and in the event that an accident has occurred and the accident level of the accident is the target level, determining that the accident corresponding to the fall state needs to be upgraded; wherein the upgrade process is triggered by a second terminal of the administrator corresponding to the target user.

[0103] In an exemplary embodiment of the present disclosure, determining whether an accident has occurred through the first terminal or the server includes: calling the first terminal to analyze the accident work order corresponding to the accident to determine whether an accident has occurred; or, determining the audio data and / or video data corresponding to the accident scene through the server, and identifying the audio data and / or video data to determine whether an accident has occurred.

[0104] In an exemplary embodiment of the present disclosure, the automatic acquisition of the following one or more on-site data to generate the claim work order with a standard structure format corresponding to the on-site data includes: determining the accident level based on the collision audio data and / or barometer drop data in the sensor data, determining the accident time based on the timestamp data in the on-site data, and determining the accident location based on the trajectory data in the on-site data; automatically filling in the work order template according to the accident time, the accident location and the accident level to generate the claim work order with a standard structure format.

[0105] In an exemplary embodiment of the present disclosure, the claim work order corresponding to the on-site data is used for automated review and jumps to the insurance platform system after the automated review is passed, including: performing image recognition based on the image data in the on-site data to determine the collision traces, and determining the reference accident level based on the audio data and the image data and sensor data; triggering an intelligent question-and-answer session with the target user to determine the content of the conversation, and updating the claim work order based on one or more of the conversation content, reference data of the accident, collision traces, and reference accident levels to obtain an updated claim work order; if the updated claim work order complies with the claim rules, it is determined that the review is passed, and automatically jumps to the insurance platform system to perform the claim operation through the insurance platform system.

[0106] In an exemplary embodiment of the present disclosure, the claim work order is updated according to one or more of the conversation content, reference data of the accident, collision traces, and reference accident levels to obtain an updated claim work order, including: extracting keywords from the conversation content to obtain keywords related to the accident; and modifying or supplementing the claim work order based on one or more of the keywords, accident solutions, collision traces, and reference accident levels to obtain an updated claim work order.

[0107] In an exemplary embodiment of the present disclosure, the sensor data includes acceleration and posture angle; and determining that the target user is in a falling state based on the sensor data includes: when the acceleration in multiple consecutive frames of sensor data is greater than an acceleration threshold and the posture angle is greater than a posture angle threshold, determining that the target user is in a falling state.

[0108] In an embodiment of the present disclosure, a safety rescue system based on a smart helmet is also provided, which includes a smart helmet, which is equipped with multiple sensors for collecting sensor data of a target user; an application platform, which is used to determine that the target user is in a fall state based on the sensor data, display an alarm page, and obtain on-site data of the accident corresponding to the fall state; if the selection operation on the alarm page meets the trigger condition, the on-site data is reported, and when it is determined that the accident needs to be upgraded, a claim work order with a standard structure format is generated, and the claim work order corresponds to the on-site data. The claim work order corresponding to the on-site data is used for automated review, and after the automated review is passed, it jumps to the insurance platform system to perform a claim operation on the claim work order through the insurance platform system.

[0109] Among them, the specific processing process of the above-mentioned device and system can be as shown in the above-mentioned smart helmet-based safety rescue method, which will not be repeated here.

[0110] Below, an electronic device is exemplified in the form of a general-purpose computing device. This electronic device is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0111] Components of the electronic device may include, but are not limited to: the at least one processing unit mentioned above, the at least one storage unit mentioned above, a bus connecting different system components (including the storage unit and the processing unit), and a display unit.

[0112] The storage unit stores program codes, which can be executed by the processing unit, so that the processing unit performs the steps described in the "Exemplary Method" section of this specification according to various exemplary embodiments of the present disclosure. For example, the processing unit can perform the following steps: Figure 1 Follow the steps shown in .

[0113] The storage unit may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM) and / or a cache memory unit, and may further include a read-only memory unit (ROM).

[0114] The storage unit may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0115] The bus can represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0116] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. As shown, the network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0117] It should be noted that some embodiments of the present disclosure also provide a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements any of the above-mentioned smart helmet-based safety rescue methods.

[0118] In one embodiment, a computer program product may be a tangible product containing a computer program, such as a computer-readable storage medium storing the computer program. The computer-readable storage medium may be a storage medium based on electrical, magnetic, optical, electromagnetic, infrared, or other signals, including but not limited to random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory (Flash), mechanical hard disk drive (HDD), solid-state drive (SSD), and the like. Exemplarily, the computer program product may be implemented as a non-volatile storage medium storing the computer program, such as a read-only memory, NAND flash memory, and the like.

[0119] In one embodiment, the computer program product may be an intangible product containing a computer program. For example, the computer program product may be implemented as a virtual digital product, such as a digital file such as an executable file or installation package storing the computer program.

[0120] The code of the computer program can be written in one or more programming languages. Programming languages include C, Java, C++, etc. The program code can be executed entirely on the user computing device, partially on the user computing device, or as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device via any type of network, such as a local area network (LAN), a wide area network (WAN), etc., or can be connected to an external computing device (e.g., via an Internet connection provided by a carrier).

[0121] Computer programs can be carried or transmitted via electrical, magnetic, optical, electromagnetic, infrared, or other signals. Electronic devices can convert signals carrying computer programs into digital signals to run the computer programs. When the computer program is run on an electronic device, its code causes the electronic device to execute (more specifically, the processor of the electronic device to execute) the method steps of various exemplary embodiments of the present disclosure.

[0122] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a client device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0123] Furthermore, the figures above are merely illustrative of the processes included in the methods according to exemplary embodiments of the present disclosure and are not intended to be limiting. It is readily understood that the processes illustrated in the figures above do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0124] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0125] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing what is disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the claims.

[0126] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A safe rescue method based on a smart helmet, characterized in that: include: Obtain sensor data corresponding to multiple sensors in the smart helmet worn by the target user; Determining that the target user is in a fall state based on the sensor data, displaying an alarm page on the application platform logged in by the target user, and obtaining on-site data of the accident corresponding to the fall state; If the selection operation on the alarm page meets the triggering conditions, the acquired on-site data will be reported. If it is determined that the accident needs to be upgraded, a claim work order with a standard structure format will be generated. The claim work order corresponds to the on-site data. The claim work order corresponding to the on-site data is used for automated review and jumps to the insurance platform system after the automated review is passed, so that the claim operation on the claim work order can be performed through the insurance platform system.

2. The method according to claim 1, characterized in that The generation of a claim work order with a standard structure format includes: Automatically obtain one or more of the following on-site data to generate the claim work order with a standard structure format corresponding to the on-site data: sensor data, timestamp data, and trajectory data of the smart helmet.

3. The method according to claim 1, characterized in that The generation of a claim work order with a standard structure format includes: Acquire audio data and / or video data corresponding to the accident scene to generate a claim work order corresponding to the scene data.

4. The method according to claim 1, wherein The method further comprises: In response to the target user selecting a target option in the alert page, jumping to a detail page of the target option; In response to a click operation on the report control on the details page, a claim work order corresponding to the on-site data is generated. The claim work order corresponding to the on-site data is used for automated review and is redirected to the insurance platform after the review is passed to perform a claim operation on the claim work order through the insurance platform.

5. The method according to claim 1, wherein The trigger condition includes that no selection operation acting on any option in the alarm page is detected, or that a selection operation acting on a target option in the alarm page is detected.

6. The method according to claim 1, characterized in that The determination that the incident needs to be escalated includes: Determining a duration during which the selection operation on any option is not detected, and calling a first terminal or a server of the site to which the target person belongs according to the duration; determining, based on the first terminal or the server, whether an accident has occurred, and if an accident has occurred and the accident level of the accident is at a target level, determining that an escalation process is required for the accident corresponding to the fall state; The upgrade process is triggered by a second terminal of a manager corresponding to the target user.

7. The method according to claim 6, characterized in that The determining whether an accident occurs by the first terminal or the server includes: Calling the first terminal to analyze on-site data corresponding to the accident to determine whether an accident has occurred; Alternatively, the server determines audio data and / or video data corresponding to the accident scene, and identifies the audio data and / or video data to determine whether an accident has occurred.

8. The method according to claim 2, characterized in that The automatic acquisition of one or more of the following on-site data to generate the claim settlement work order having a standard structure format corresponding to the on-site data includes: determining an accident level based on the collision audio data and / or the barometer drop data in the sensor data, determining an accident time based on the timestamp data in the scene data, and determining an accident location based on the trajectory data in the scene data; The work order template is automatically filled in according to the accident time, the accident location and the accident level to generate the claim work order with a standard structure format.

9. The method according to claim 1, characterized in that The claim work order corresponding to the on-site data is used for automated review and is redirected to the insurance platform system after passing the automated review, including: performing image recognition based on the image data in the scene data to determine collision traces, and determining a reference accident level based on the audio data, the image data, and the sensor data; triggering an intelligent question-and-answer session with the target user to determine the content of the conversation, and updating the claim settlement work order based on one or more of the conversation content, reference data of the accident, collision traces, and a reference accident level, to obtain an updated claim settlement work order; If the updated claim work order complies with the claim rules, it will be determined to be approved and automatically transferred to the insurance platform system to perform the claim operation through the insurance platform system.

10. The method according to claim 9, characterized in that The updating of the claim settlement work order according to one or more of the conversation content, the reference data of the accident, the collision trace, and the reference accident level to obtain an updated claim settlement work order includes: Extracting keywords from the conversation content to obtain keywords related to the accident; Based on one or more of the keywords, the accident solution, the collision traces, and the reference accident level, the claim settlement work order is modified or supplemented to obtain an updated claim settlement work order.

11. The method according to claim 1, wherein The sensor data includes acceleration and posture angle; and determining that the target user is in a falling state according to the sensor data includes: When the acceleration in the continuous multiple frames of sensor data is greater than the acceleration threshold and the posture angle is greater than the posture angle threshold, it is determined that the target user is in a falling state.

12. A safety rescue system based on a smart helmet, characterized in that: include: A smart helmet, which is equipped with multiple sensors for collecting sensor data of the target user; The application platform is used to display an alarm page when it is determined according to the sensor data that the target user is in a fall state, and obtain the on-site data of the accident corresponding to the fall state; if the selection operation on the alarm page meets the trigger condition, the on-site data is reported, and when it is determined that the accident needs to be upgraded, a claim work order with a standard structure format is generated, and the claim work order corresponds to the on-site data. The claim work order corresponding to the on-site data is used for automated review, and after the automated review is passed, it jumps to the insurance platform system to perform a claim operation on the claim work order through the insurance platform system.

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