Data processing method and device, electronic equipment and storage medium

Abstract

The application discloses a data processing method and device, electronic equipment and storage medium. It relates to the technical field of computer processing. The method comprises the following steps: acquiring physical information and behavior data corresponding to at least one to-be-measured object and area images corresponding to at least one to-be-monitored area; determining the passenger flow density of the at least one to-be-monitored area based on the information of each pixel point in the area images; when it is determined that an abnormal event exists based on the physical information, the behavior data and / or the passenger flow density, determining the target position and the corresponding position type of the abnormal event; and determining the target processing mode based on the event attribute, the target position and the position type of the abnormal event to perform emergency processing on the abnormal event. The application solves the problem that the abnormal event is processed by the staff near the abnormal event occurrence point in the prior art, resulting in poor abnormal event processing effect and low efficiency, and achieves the effect of improving the timeliness and effectiveness of abnormal event processing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer processing, and in particular to a data processing method and device, electronic equipment and storage medium. BACKGROUND

[0002] In recent years, with large-scale construction of urban rail transit in various cities, passenger traffic volume has also increased, and many safety hazards exist, such as abnormal events such as sudden public health events. In order to protect the safety and health of passengers, how to respond to such events in a timely and effective manner has become an important challenge.

[0003] At present, the emergency plan for abnormal events is usually to notify the on-site staff to handle when there is an abnormal event. This method is difficult to deal with abnormal events in multiple scenarios and situations, and has the problems of poor abnormal event handling effect and low efficiency. SUMMARY

[0004] The present application provides a data processing method and device, electronic equipment and storage medium to improve the timeliness and effectiveness of abnormal event handling.

[0005] According to an aspect of the present application, a data processing method is provided, which comprises:

[0006] Obtaining vital sign information and behavior data corresponding to at least one to-be-tested object, and area images corresponding to at least one to-be-monitored area;

[0007] Determining passenger flow density of the at least one to-be-monitored area based on pixel point information in the area images, and determining a target position and a corresponding position type of an abnormal event based on the vital sign information, behavior data and / or passenger flow density when it is determined that there is an abnormal event;

[0008] Determining a target handling mode based on the event attribute of the abnormal event, the target position and the position type, and performing emergency handling on the abnormal event based on the target handling mode.

[0009] According to another aspect of the present application, a data processing device is provided, which comprises:

[0010] A data acquisition module is configured to obtain vital sign information and behavior data corresponding to at least one to-be-tested object, and area images corresponding to at least one to-be-monitored area;

[0011] A position type determination module is configured to determine a passenger flow density of the at least one to-be-monitored area based on pixel point information in the area image, and determine a target position and a corresponding position type of an abnormal event when it is determined that the abnormal event exists based on the sign information, the behavior data and / or the passenger flow density.

[0012] A target processing mode determination module is configured to determine a target processing mode based on an event attribute of the abnormal event, the target position and the position type, so as to perform emergency processing on the abnormal event based on the target processing mode.

[0013] According to another aspect of the present application, an electronic device is provided, which comprises:

[0014] at least one processor; and

[0015] a memory connected to the at least one processor in communication; wherein,

[0016] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the data processing method according to any one of the embodiments of the present application.

[0017] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the data processing method according to any one of the embodiments of the present application when executed by the processor.

[0018] The technical scheme of the embodiment of the present application acquires the sign information and behavior data corresponding to at least one to-be-measured object, and the region image corresponding to at least one to-be-monitored region; determines the passenger flow density of the at least one to-be-monitored region based on the pixel point information in the region image, and when it is determined that an abnormal event exists based on the sign information, the behavior data and / or the passenger flow density, determines the target position and the corresponding position type of the abnormal event; determines the target processing mode based on the event attribute, the target position and the position type of the abnormal event, and performs emergency processing on the abnormal event based on the target processing mode, thereby solving the problem that in the prior art, the abnormal event is processed by the staff near the abnormal event occurrence point, resulting in poor abnormal event processing effect and low efficiency, and achieving the technical effect of improving the timeliness and effectiveness of abnormal event processing by collecting various types of data such as the sign information and behavior data corresponding to the to-be-measured object and the region image corresponding to the at least one to-be-monitored region, calculating the passenger flow density of the to-be-monitored region, and then monitoring whether an abnormal event exists based on the sign information, the behavior data and / or the passenger flow density, accurately positioning the abnormal event occurrence point when it is determined that an abnormal event exists, determining the target position and the corresponding position type, then calling the target processing mode matched with the event attribute, the target position and the position type of the abnormal event, and then performing emergency processing on the abnormal event based on the target processing mode.

[0019] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0021] Figure 1 is a flow chart of a data processing method according to an embodiment of the present application;

[0022] Figure 2 is a structural schematic diagram of an emergency management system according to an embodiment of the present application;

[0023] Figure 3 is a flow chart of an emergency management method according to an embodiment of the present application;

[0024] Figure 4 is a flow chart of an emergency management method according to an embodiment of the present application;

[0025] Figure 5 is a structural schematic diagram of a data processing device according to an embodiment three of the present application;

[0026] Figure 6 is a structural schematic diagram of an electronic device for implementing a data processing method according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the personnel in the technical field better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0028] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to include only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device. The acquisition, storage, use, processing and the like of data in the technical solutions of the present application all comply with the relevant provisions of national laws and regulations.

[0029] Embodiment one

[0030] Figure 1 is a flowchart of a data processing method according to an embodiment one of the present application. The embodiment can be applicable to the situation of coping with an emergency event. The method can be executed by a data processing device, which can be realized in the form of hardware and / or software, and can be configured in a computing device. As shown in the figure, the method comprises: Figure 1

[0031] S110, acquiring the physical information and behavior data corresponding to at least one to-be-tested object, and the region image corresponding to at least one to-be-monitored region.

[0032] ​The monitored area can be understood as the area where abnormal events need to be monitored, such as entrances and exits, station halls, platforms, carriages, staircases, and passageways. Abnormal events can be sudden public health emergencies. The subjects to be monitored can be those undergoing temperature checks, or passengers or staff located within the monitored area. Vital signs information can include body temperature. Behavioral data can include users' body movements, facial expressions, and items worn, etc.

[0033] In this embodiment, at least one temperature measuring device deployed within the monitoring area can be used to detect and output the vital signs information of each subject. For example, temperature measuring devices can be placed inside or outside the station. When a passenger actively approaches the temperature measuring device, their body temperature can be accurately measured without contact, with the user's knowledge and permission. This information is then used to determine whether there are individuals with abnormal body temperatures, thus strictly controlling the temperature detection of passengers entering and leaving the station. Behavior recognition devices can also be deployed within the monitoring area to collect behavioral data of users (i.e., subjects) within the monitoring area in real time or periodically, thereby determining whether there are users exhibiting abnormal behavior. Furthermore, camera devices can be deployed in at least one monitoring area to collect video frame images of the monitored area in real time or periodically, creating regional images. These images are then used to analyze passenger flow information based on the pixels in the regional images to determine whether there are any abnormal passenger flow situations. Finally, the collected information can be used to determine whether any abnormal events occur.

[0034] For example, non-contact temperature measurement devices can be used to collect the body temperature information of the object being measured. Based on the area and structure of each monitoring area, monitoring cameras in each monitoring area can be reasonably arranged, and the video images of different areas collected by the monitoring cameras can be used as area images.

[0035] S120. Based on the information of each pixel in the regional image, determine the passenger flow density of at least one area to be monitored, and when it is determined that there is an abnormal event based on vital sign information, behavioral data and / or passenger flow density, determine the target location of the abnormal event and the corresponding location type.

[0036] Among these, pixel information represents the attribute information of a pixel. A pixel is the smallest unit of an image, and each pixel has a specific location and an assigned color value. Passenger flow density is used to characterize the density of passengers in a space. For example, a higher passenger flow density indicates a denser number of passengers in the area, while a lower passenger flow density indicates a sparser number of passengers. Target location refers to the location where an abnormal event occurs. Location types can be categorized in various ways, such as directional locations (north, south, east, west), or area types (e.g., entrances / exits, concourses, platforms, carriages, stairs), or both inside and outside the station.

[0037] In this embodiment, after acquiring the area image of the area to be monitored, the passenger flow information, i.e., passenger flow density, can be determined based on the area image information. For example, by identifying each pixel in the area image, the number of pixels belonging to users can be determined, and the passenger flow density can be obtained by dividing the number of pixels corresponding to users by the total number of pixels in the area image. When any one of the following data—vital signs, behavioral data, and / or passenger flow density—is acquired, it can be determined whether an abnormal event exists. The method for determining the existence of an abnormal event can be: if the vital signs information is not within the standard vital signs range, the behavioral data contains abnormal behavioral characteristics, and / or the passenger flow density is greater than a preset density threshold, then an abnormal event is determined to exist. The standard vital signs range can be a preset normal body temperature range, such as 36.3-37.2℃ or 36.5-37.7℃. Abnormal behavioral characteristics can be feature information representing behaviors such as fainting, not wearing a mask, or falling. In practical applications, it's possible to check if vital signs are within the standard range. If not, it indicates the individual may have an abnormal body temperature, posing a potential health risk to other users. In this case, an alert can be generated by the temperature measurement device to facilitate timely investigation and handling, preliminarily classifying the event as an abnormal event. Abnormal behavior can be identified based on behavioral data. If abnormal behavioral characteristics are present, it suggests a user may be behaving abnormally (e.g., a passenger fainting). An alert can be generated, notifying management to initiate emergency procedures and confirming the existence of an abnormal event. Finally, passenger density can be compared to a preset density threshold. If the passenger density exceeds the threshold, it indicates potential overcrowding. An alert can be generated, notifying management to initiate emergency procedures for passenger evacuation and confirming the existence of an abnormal event. If an abnormal event is confirmed, the abnormal event can be located. The location where the abnormal event occurred can be identified as the target location. Based on the environment to which the target location belongs, the location type can be determined. For example, if the target location is on a platform, the corresponding location type is platform; if the target location is in a carriage, the corresponding location type is carriage; if the target location is on a staircase, the corresponding location type is staircase.

[0038] It should be noted that when determining the target location of an abnormal event, it can be based on a positioning module. For example, the positioning module can be a handheld terminal with functions such as high-definition video capture, voice communication intercom, wireless transmission, personnel positioning, video transmission, local storage, and hazard source early warning. Staff can use these real-name managed handheld terminals to accurately locate and manage the scene of the abnormal event, and can provide real-time feedback of the situation to the central control center. The positioning module can also be a backend device based on personnel positioning technology. For example, if an abnormal event is determined based on vital sign information, the location of the temperature measuring device providing that information can be used as the target location. If an abnormal event is determined based on a regional image, the target location can be located within that regional image. If an abnormal event is determined based on behavioral data, the positioning module can be integrated with abnormal behavior recognition technology to quickly locate the location of the abnormal personnel identified by the personnel abnormal behavior recognition device as the target location.

[0039] It should also be noted that different monitoring areas have different areas and spatial structures. Two monitoring areas with the same area and passenger flow may have different passenger flow densities due to differences in spatial structure. To improve the accuracy of passenger flow density determination and thus the accuracy of abnormal event detection, when determining the passenger flow density of at least one monitoring area based on the pixel information in the regional image, each monitoring area can be divided. For example, monitoring area 1 may include the platform concourse area and the passageway area. The passenger flow density of the two areas can be calculated separately, and then the passenger flow density of the entire monitoring area can be calculated by combining these two passenger flow densities.

[0040] Optionally, based on the pixel information in the regional image, the passenger flow density of at least one monitored area is determined, including: for each monitored area, the regional image of the current monitored area is divided into regions to obtain a regional sub-image of at least one monitored sub-region corresponding to the current monitored area; based on the pixel information in the regional sub-image, the passenger flow sub-density of the monitored sub-region corresponding to the regional sub-image is determined; based on the passenger flow sub-density of at least one monitored sub-region corresponding to the current monitored area, the passenger flow density of the current monitored area is determined.

[0041] It should be noted that the method for determining each area to be monitored is the same, and any one of the areas to be monitored can be used as the current area to be monitored for explanation.

[0042] In this embodiment, image recognition technology can be used to identify and process the regional image of the area to be monitored, thereby dividing the regional image into regions. For example, the seating area and corridor area waiting to be monitored sub-regions located in the area to be monitored can be identified, and correspondingly, regional sub-images of each sub-region to be monitored are obtained. Each regional sub-image contains multiple pixels. When determining the passenger flow sub-density of the sub-region to be monitored based on the pixel information in the regional sub-image, the total number of pixels can be determined based on the pixel information in the regional sub-image, and target detection can be performed on the regional sub-image to determine the number of sub-pixels corresponding to the detected target. Based on the total number of pixels and the number of sub-pixels, the passenger flow sub-density of the sub-region to be monitored corresponding to the regional sub-image can be determined. Here, the detection target can be a user. Specifically, target detection can be performed on each regional sub-image to determine the number of sub-pixels corresponding to the detected target. The number of sub-pixels and the total number of pixels in the regional sub-image can be divided, and the quotient value can be used as the passenger flow sub-density of the sub-region to which the regional sub-image belongs. Accordingly, the passenger flow sub-density of each sub-region to be monitored can be obtained. Furthermore, the passenger flow sub-density of at least one sub-region corresponding to the current monitored area can be averaged, and this average can be used as the passenger flow density of the current monitored area. Alternatively, the passenger flow sub-density of at least one sub-region corresponding to the current monitored area can be multiplied by its corresponding weight value to obtain multiple processed passenger flow sub-densities, and the average of these multiple processed passenger flow sub-densities can be used as the passenger flow density of the current monitored area.

[0043] For example, assuming the area to be monitored is the target carriage, surveillance cameras can be strategically placed within the carriage to acquire images. These images are then segmented to identify boundary areas (excluding passengers), seating areas, and aisle areas, all of which serve as sub-regions to be monitored. Seat passengers can be extracted from the seating areas, and aisle passengers from the aisle areas. The passenger density in the seating area is determined by the ratio of the number of pixels of seat passengers to the total number of pixels in the seating area; the passenger density in the aisle area is determined by the ratio of the number of pixels of aisle passengers to the total number of pixels in the aisle area; finally, the passenger density within the target carriage is determined based on both the seat and aisle passenger densities.

[0044] S130. Based on the event attributes, target location, and location type of the abnormal event, determine the target handling method to carry out emergency handling of the abnormal event based on the target handling method.

[0045] The event attributes can be the type of event, such as abnormal body temperature, abnormal behavior, or abnormal passenger flow. The target handling method refers to the approach used to handle the abnormal event, including the event attributes, target location and location type, handling measures, responsible user, and handling process. Handling measures can be the execution methods for handling the abnormal event, such as passenger evacuation, route adjustments, station disinfection, personnel isolation, and message dissemination. The responsible user can be the object of the abnormal event handling, such as station staff A, train crew member B, or train driver C.

[0046] In this embodiment, after determining the target location and location type, the event attributes of the abnormal event can be combined to determine the method of handling the abnormal event corresponding to the event attributes, target location, and location type as the target handling method. The target handling method can be sent to the smart terminal device so that the terminal user can perform emergency handling of the abnormal event based on the target handling method.

[0047] It should be noted that the handling methods for different abnormal events can be pre-configured. Based on information such as the event attributes, occurrence location, and location type of the abnormal event in the actual scenario, the corresponding handling method can be configured in a timely and effective manner to obtain the target handling method that matches the event attributes, target location, and location type when an abnormal event occurs, thereby improving the timeliness and accuracy of abnormal event handling.

[0048] Optionally, based on the event attributes, target location, and location type of the abnormal event, the target handling method is determined, including: retrieving the target handling method corresponding to the event attributes, target location, and location type from a pre-configured list of handling methods.

[0049] The list of handling methods can include multiple options. The target handling method includes at least one level of event handler, the executing users under the event handler, the corresponding execution method, and a route adjustment plan. The level can be a management level. The event handler can be a user, unit, or department handling the event; for example, depending on the management level, it can be divided into a central command center, a deputy command center, and member execution departments. The execution method can be passenger evacuation methods, environmental disinfection methods, public awareness campaigns, etc.

[0050] In this embodiment, after determining the event attributes, target location, and location type of the abnormal event, a target processing method that matches the event attributes, target location, and location type can be retrieved from a pre-configured processing method list. For example, the target processing method corresponding to event attribute A and location type B is C, and the target processing method corresponding to event attribute A, target location D, and location type T is E.

[0051] For example, the management responsibilities of multiple incident handling parties can be divided. For regional rail transit, Incident Handling Party 1 (such as the central command center) can be in overall management, responsible for emergency command of rail transit operation emergencies, and providing unified command for emergency response to emergencies in various types of rail transit operations. Incident Handling Party 2 (such as the deputy command center), at the next level below Incident Handling Party 1, can assist Incident Handling Party 1 in handling emergency response to rail transit operation emergencies. For instance, the responsibilities of Incident Handling Party 1 in handling public health emergencies may include: monitoring the real-time dynamics of public health emergencies, promptly disseminating information and summarizing public health events; developing policies, measures, and guidelines for responding to public health emergencies in rail transit based on the severity of the public health event and the characteristics of rail transit operations; directing the specific response work for public health emergencies in various types of rail transit; being responsible for the construction and management of emergency response teams; promptly reporting the progress of the handling to the next higher level; and monitoring and supervising the handling of public health events by the next lower level. The responsibilities of Incident Handling Party 2 in handling public health emergencies may include: being responsible for the unified command and coordination of the execution of public health emergencies in rail transit; being responsible for activating the emergency plan (i.e., the target handling method), and commanding and coordinating emergency actions; refining the target handling method based on the emergency plan, the response level of the abnormal event, and the characteristics of rail operation, making each handling process and measure visible and digital; and directing lower-level members to carry out emergency work, etc. The responsibilities of Incident Handling Party 3 in handling public health emergencies may include: on-site handling, environmental, information dissemination, and logistical support, etc. For example, being responsible for executing abnormal event handling tasks assigned by the higher level; proposing specific emergency response measures; being responsible for coordinating employees to form collaborative teams, implementing on-site organization, coordination, command, and post-event handling of emergency response, user evacuation and rescue, etc.; determining the activation of corresponding emergency response plans based on the development of the event; and being responsible for sterilizing locations, facilities, and equipment (such as turnstiles, escalators, elevators, etc.) where abnormal events occur, with the sterilization frequency configured uniformly according to the abnormality level and passenger flow. By finely dividing the management hierarchy, at least one level of event handlers, the executing users under each event handler, and the corresponding execution methods are identified to ensure timely and effective personnel allocation in the event of anomalies. Pre-configuration of route adjustment plans for entering and leaving stations is also possible. For example, when it is determined that train routes need to be adjusted, the train dispatcher can be notified to make the adjustment and the specific adjustment information can be sent to the relevant stations. After receiving the adjustment information, the stations can promptly send the information to passengers through effective station publicity channels and smart mobile terminals. If an abnormal user is listed as a person with an anomaly, their historical movement trajectory within the rail transit system can be promptly determined, and the information can be reported level by level to the higher-level event management body.

[0052] For example, when handling the abnormal event based on the target processing method, passenger guidance management can be implemented. This includes using electronic means to transmit the passenger flow density identification results of various areas within the station and the passenger flow density in the carriages of trains about to arrive to the station's PIS (Passenger Information System) and broadcasting system in real time. The information is displayed on the PIS and broadcast through the broadcasting system, guiding passengers to disperse and wait in an orderly manner, and reasonably controlling the passenger flow density in various areas of the station and the carriage occupancy rate. Alternatively, data transmission technology can be used to transmit the station's passenger flow density to relevant mobile apps for passengers, providing a reference when choosing their travel routes. It can also transmit the passenger flow density within the carriages to passengers, guiding them to disperse and wait independently, preventing passenger gatherings and improving safety.

[0053] To improve the effectiveness and timeliness of handling abnormal events, after determining the existence of an abnormal event based on vital signs, behavioral data, and / or passenger flow density, a warning message corresponding to the abnormal event can be generated; and the warning message can be broadcast based on the warning equipment associated with the abnormal event.

[0054] Specifically, if an abnormal event occurs, a corresponding early warning message can be generated. For example, if a user's body temperature is abnormal, the early warning device can be a temperature measuring device, which can then broadcast the early warning message. If a user's behavior is abnormal, the early warning device can be a behavior recognition device or a broadcasting device (such as a voice announcer) to broadcast the early warning message. If passenger flow is abnormal, the early warning device can be a passenger flow analysis device or a broadcasting device to broadcast the early warning message.

[0055] In this embodiment, after determining the target location where the abnormal event exists, the method further includes: determining the warning display location in the management interface that matches the target location; and displaying the abnormal event at the warning display location.

[0056] Specifically, a map corresponding to the monitoring area can be pre-configured and displayed on the management interface. If an abnormal event occurs, the system can identify the warning display location on the map that matches the target location and display the abnormal event at that location, for example, as an icon, a bounding box, or a highlighted point. For instance, the target location can be converted into an electronic signal and displayed on the management interface to maintain consistency with on-site information. When an abnormal event occurs, the system can quickly locate the corresponding personnel. Managers can visually select the on-site personnel to quickly handle the abnormal event at the target location, improving the timeliness of abnormal event handling.

[0057] The technical solution of this embodiment acquires vital sign information and behavioral data corresponding to at least one subject to be tested, as well as regional images corresponding to at least one area to be monitored; based on the pixel information in the regional images, it determines the passenger flow density of at least one area to be monitored; and when an abnormal event is determined based on the vital sign information, behavioral data, and / or passenger flow density, it determines the target location and corresponding location type of the abnormal event; based on the event attributes, target location, and location type of the abnormal event, it determines the target handling method, and performs emergency handling of the abnormal event based on the target handling method. This solves the problem in the prior art where abnormal events are handled by nearby staff at the point of occurrence, leading to difficulties in handling abnormal events. To address the issues of poor performance and low efficiency, this technology collects various types of data, such as the vital signs and behavioral data of the subjects under test, as well as regional images corresponding to at least one area to be monitored. It calculates the passenger flow density of the monitored area, and then monitors for abnormal events based on the vital signs, behavioral data, and / or passenger flow density. When an abnormal event is identified, it accurately locates the point of occurrence, determines the target location and corresponding location type, and then retrieves the target processing method that matches the event attributes, target location, and location type of the abnormal event. Finally, it performs emergency processing on the abnormal event based on the target processing method, thereby improving the timeliness and effectiveness of abnormal event handling.

[0058] Example 2

[0059] As an optional embodiment of the above embodiments, specific application scenario examples are provided to enable those skilled in the art to further understand the technical solutions of the embodiments of the present invention. Specifically, please refer to the following detailed content.

[0060] For example, see Figure 2 , Figure 2This can be represented as a structural diagram of an emergency management system. The technical solution provided in this embodiment can be implemented by an emergency management system, which includes a contactless temperature measurement and automatic temperature abnormality alarm module, an abnormal behavior recognition module, a passenger flow density recognition module, an abnormal location module, and an emergency plan module. The contactless temperature measurement and automatic temperature abnormality alarm module is used to perform contactless and accurate temperature measurement on the subject, collect the subject's vital signs information, and automatically alarm when an abnormal temperature is detected based on the vital signs information, so as to promptly conduct epidemic investigation and handling. The contactless temperature measurement and automatic temperature abnormality alarm module can be configured in a contactless temperature measurement and automatic temperature abnormality alarm device. This device can achieve physical isolation between the detection personnel and the subject, reducing the risk of staff being affected, while strictly controlling the temperature detection of passengers entering and exiting the station, thus assisting in the monitoring and response to abnormal events in various scenarios. The abnormal behavior recognition module is used to identify passengers who have not taken protective measures or whose protective measures are inadequate before entering the station, such as people entering the station without masks, and to identify users with abnormal behavior within the station. For example, if a passenger faints inside the station, the abnormal behavior recognition module can automatically identify and issue an alert, allowing staff to rush to the scene immediately for emergency handling and prevent the incident from escalating. The abnormal behavior recognition module can be configured in abnormal behavior recognition equipment to identify passengers exhibiting abnormal behavior on the train and promptly notify staff to initiate emergency procedures. The passenger flow density recognition module divides the station space into monitoring areas such as entrances / exits, platform levels, staircases, and the concourse level (which is divided into blocks according to waiting positions in carriages). Based on the area and structure of each monitoring area, surveillance cameras are strategically placed. Passenger flow information for different monitoring areas is obtained from the video images captured by the surveillance cameras. For example, by strategically placing surveillance cameras inside carriages to acquire monitoring images (i.e., area images) of the target carriage, the monitoring images are segmented to determine sub-images of boundary areas (areas excluding passengers), seat areas, and corridor areas. Seat passengers are extracted from the seat areas, and corridor passengers are extracted from the corridor areas. The passenger flow density in the seating area is determined by the ratio of the number of pixels of the target passengers in the seating area to the total number of pixels in the seating area; the passenger flow density in the corridor area is determined by the ratio of the number of pixels of the target passengers in the corridor area to the total number of pixels in the corridor area; finally, the passenger flow density in the target carriage is determined based on the passenger flow density in both the seating and corridor areas. An abnormal event can be identified if the passenger flow density exceeds a preset density threshold. For a further understanding of the technical solution of this embodiment, please refer to... Figure 3 , Figure 3This can be represented as a flowchart of an emergency management method. The presence of abnormal events can be identified through a contactless temperature measurement and automatic temperature alarm module, an abnormal behavior recognition module, or a passenger flow density recognition module. If the contactless temperature measurement and automatic temperature alarm module detects an abnormal temperature, or the abnormal behavior recognition module detects abnormal behavior, the location of the abnormal event (i.e., the target location) can be located using the abnormal location module and sent to the emergency plan module. This triggers the emergency plan module, initiating the station's abnormal event handling process. The emergency plan module determines the emergency scenario based on the location type of the target location, deciding whether to execute the station's emergency organization process or the train operation emergency organization process. After determining the emergency organization process, the appropriate emergency plan (i.e., the target handling method) is selected. If the passenger flow density recognition module detects a large passenger flow, the location of the abnormal event can be located using the abnormal location module, and the location can be sent to on-site staff for real-time evacuation strategies.

[0061] See Figure 4 , Figure 4 This can be represented as a flowchart of an emergency management method. For example, the emergency organization process for public health incidents (i.e., abnormal events) in rail transit can be divided into two main areas based on the location of occurrence: the emergency organization process within the station and the emergency organization process during train operation. The specific implementation of emergency management can be as follows: Before a public health incident occurs, monitoring can be conducted using technologies such as contactless temperature measurement and abnormal warning, abnormal personnel behavior identification, passenger flow density identification, and personnel positioning. During the detection of a public health incident, passenger entry and exit flow management, abnormal passenger temperature detection and warning, abnormal passenger behavior identification and warning, abnormal personnel entry identification and warning, passenger safety publicity and management, and daily emergency management of the station environment can be implemented based on the station's emergency organization process. Upon confirmation of abnormal passenger temperatures, abnormal passenger behavior, or abnormal personnel entry, the station's emergency response process is activated. After the emergency ends, routine management is restored. Passenger flow monitoring and forecasting, train operation plan adjustment, and passenger abnormal behavior identification and early warning can also be carried out based on the emergency organization process during the train operation. When it is determined that a passenger has abnormal behavior on the train, the train emergency handling process is activated, and daily management is restored after the emergency ends.

[0062] The technical solution of this embodiment acquires vital sign information and behavioral data corresponding to at least one subject to be tested, as well as regional images corresponding to at least one area to be monitored; based on the pixel information in the regional images, it determines the passenger flow density of at least one area to be monitored; and when an abnormal event is determined based on the vital sign information, behavioral data, and / or passenger flow density, it determines the target location and corresponding location type of the abnormal event; based on the event attributes, target location, and location type of the abnormal event, it determines the target handling method, and performs emergency handling of the abnormal event based on the target handling method. This solves the problem in the prior art where abnormal events are handled by nearby staff at the point of occurrence, leading to difficulties in handling abnormal events. To address the issues of poor performance and low efficiency, this technology collects various types of data, such as the vital signs and behavioral data of the subjects under test, as well as regional images corresponding to at least one area to be monitored. It calculates the passenger flow density of the monitored area, and then monitors for abnormal events based on the vital signs, behavioral data, and / or passenger flow density. When an abnormal event is identified, it accurately locates the point of occurrence, determines the target location and corresponding location type, and then retrieves the target processing method that matches the event attributes, target location, and location type of the abnormal event. Finally, it performs emergency processing on the abnormal event based on the target processing method, thereby improving the timeliness and effectiveness of abnormal event handling.

[0063] Example 3

[0064] Figure 5 This is a schematic diagram of the structure of a data processing device according to Embodiment 3 of the present invention. Figure 5 As shown, the device includes: a data acquisition module 510, a location type determination module 520, and a target processing method determination module 530.

[0065] The data acquisition module 510 is used to acquire vital sign information and behavioral data corresponding to at least one subject to be tested, as well as regional images corresponding to at least one area to be monitored; the location type determination module 520 is used to determine the passenger flow density of the at least one area to be monitored based on the pixel information in the regional image, and when an abnormal event is determined based on the vital sign information, behavioral data and / or passenger flow density, determine the target location and corresponding location type of the abnormal event; the target processing method determination module 530 is used to determine the target processing method based on the event attributes of the abnormal event, the target location and the location type, so as to perform emergency processing on the abnormal event based on the target processing method.

[0066] The technical solution of this embodiment acquires vital sign information and behavioral data corresponding to at least one subject to be tested, as well as regional images corresponding to at least one area to be monitored; based on the pixel information in the regional images, it determines the passenger flow density of at least one area to be monitored; and when an abnormal event is determined based on the vital sign information, behavioral data, and / or passenger flow density, it determines the target location and corresponding location type of the abnormal event; based on the event attributes, target location, and location type of the abnormal event, it determines the target handling method, and performs emergency handling of the abnormal event based on the target handling method. This solves the problem in the prior art where abnormal events are handled by nearby staff at the point of occurrence, leading to difficulties in handling abnormal events. To address the issues of poor performance and low efficiency, this technology collects various types of data, such as the vital signs and behavioral data of the subjects under test, as well as regional images corresponding to at least one area to be monitored. It calculates the passenger flow density of the monitored area, and then monitors for abnormal events based on the vital signs, behavioral data, and / or passenger flow density. When an abnormal event is identified, it accurately locates the point of occurrence, determines the target location and corresponding location type, and then retrieves the target processing method that matches the event attributes, target location, and location type of the abnormal event. Finally, it performs emergency processing on the abnormal event based on the target processing method, thereby improving the timeliness and effectiveness of abnormal event handling.

[0067] Based on the above-mentioned device, optionally, the location type determination module 520 includes a region sub-image determination unit, a passenger flow sub-density determination unit, and a passenger flow density determination unit.

[0068] The region sub-image determination unit is used to divide the region image of the current region to be monitored into regions for each region to be monitored, and obtain a region sub-image of at least one sub-region to be monitored corresponding to the current region to be monitored.

[0069] The passenger flow sub-density determination unit is used to determine the passenger flow sub-density of the sub-region to be monitored corresponding to the sub-region image based on the pixel information in the sub-region image.

[0070] The passenger flow density determination unit is used to determine the passenger flow density of the current monitoring area based on the passenger flow sub-density of at least one monitoring sub-area corresponding to the current monitoring area.

[0071] Based on the above-mentioned device, optionally, the passenger flow sub-density determination unit includes a pixel number determination sub-unit and a passenger flow sub-density determination sub-unit.

[0072] The pixel count determination subunit is used to determine the total number of pixels based on the pixel information in the region sub-image, and to perform target detection on the region sub-image to determine the number of sub-pixels corresponding to the detected target.

[0073] The passenger flow sub-density determination sub-unit is used to determine the passenger flow sub-density of the sub-region to be monitored corresponding to the sub-image of the region based on the total number of pixels and the number of sub-pixels.

[0074] Optionally, based on the above-mentioned device, the device may further include an abnormal event determination module, which is used to determine the existence of an abnormal event if the vital signs information is not within the standard vital signs range, the behavioral data contains abnormal behavioral characteristics, and / or the passenger flow density is greater than a preset density threshold.

[0075] Based on the above device, optionally, the target processing method determination module 530 is used to retrieve the target processing method corresponding to the event attribute, the target location and the location type from a pre-configured processing method list;

[0076] The target processing method includes at least one level of event handler, the executing user under the event handler and the corresponding execution method, as well as a line adjustment scheme.

[0077] Optionally, based on the above-mentioned device, the device may further include an early warning module, which includes an early warning information determination unit and a broadcasting unit.

[0078] The early warning information determination unit is used to generate early warning information corresponding to the abnormal event;

[0079] The broadcasting unit is used to broadcast the warning information based on the warning device associated with the abnormal event.

[0080] Optionally, based on the above-mentioned device, the device may further include a location display module, which includes a warning display location determination unit and a location display unit.

[0081] The warning display location determination unit is used to determine the warning display location in the management interface that matches the target location;

[0082] A location display unit is used to display the abnormal event at the warning display location.

[0083] The data processing apparatus provided in the embodiments of the present invention can execute the data processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0084] Example 4

[0085] Figure 6This is a schematic diagram of the structure of an electronic device implementing the data processing method of an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0086] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0087] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0088] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as data processing methods.

[0089] In some embodiments, the data processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the data processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the data processing method by any other suitable means (e.g., by means of firmware).

[0090] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0091] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0092] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0093] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0094] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0095] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0096] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0097] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A data processing method, characterized in that, include: Acquire vital sign information and behavioral data corresponding to at least one subject to be tested, as well as regional images corresponding to at least one area to be monitored; Based on the pixel information in the regional image, the passenger flow density of the at least one area to be monitored is determined, and when an abnormal event is determined based on the vital signs information, behavioral data and / or passenger flow density, the target location and corresponding location type of the abnormal event are determined. The target location is the location where the abnormal event occurred; Based on the event attributes of the abnormal event, the target location, and the location type, a target handling method is determined to perform emergency handling on the abnormal event based on the target handling method; the event attributes include abnormal body temperature, abnormal behavior, and abnormal passenger flow. The target handling method includes the event attributes of the abnormal event, the target location and the location type, the handling measures, the responsible user, and the handling process; An abnormal event has been identified, including: If the vital signs information is not within the standard range, the behavioral data contains abnormal behavioral characteristics, and / or the passenger flow density is greater than the preset density threshold, then an abnormal event is determined to exist.

2. The method according to claim 1, characterized in that, Determining the passenger flow density of the at least one area to be monitored based on the pixel information in the area image includes: For each area to be monitored, the area image of the current area to be monitored is divided into regions to obtain at least one sub-image of the sub-region to be monitored corresponding to the current area to be monitored. Based on the pixel information in the sub-image of the region, the passenger flow sub-density of the sub-region to be monitored corresponding to the sub-image of the region is determined; The passenger flow density of the current area to be monitored is determined based on the passenger flow sub-density of at least one sub-area to be monitored corresponding to the current area to be monitored.

3. The method according to claim 2, characterized in that, The step of determining the passenger flow sub-density of the sub-region to be monitored corresponding to the sub-image of the region based on the pixel information in the sub-image of the region includes: Based on the pixel information in the sub-image of the region, the total number of pixels is determined, and target detection is performed on the sub-image of the region to determine the number of sub-pixels corresponding to the detected target. Based on the total number of pixels and the number of sub-pixels, the passenger flow sub-density of the sub-region to be monitored corresponding to the sub-image of the region is determined.

4. The method according to claim 1, characterized in that, The step of determining the target processing method based on the event attributes of the abnormal event, the target location, and the location type includes: Retrieve the target processing method corresponding to the event attribute, the target location, and the location type from the pre-configured processing method list; The target processing method includes at least one level of event handler, the executing user under the event handler and the corresponding execution method, as well as a line adjustment scheme.

5. The method according to claim 1, characterized in that, After determining the existence of an abnormal event based on the vital signs information, behavioral data, and / or passenger flow density, the process further includes: Generate early warning information corresponding to the abnormal event; The warning message is broadcast based on the warning device associated with the abnormal event.

6. The method according to claim 1, characterized in that, After determining the target location where the anomalous event exists, the following steps are also included: Determine the warning display location in the management interface that matches the target location; The abnormal event will be displayed at the warning display location.

7. A data processing apparatus, characterized in that, include: The data acquisition module is used to acquire vital sign information and behavioral data corresponding to at least one subject to be tested, as well as regional images corresponding to at least one area to be monitored. The location type determination module is used to determine the passenger flow density of the at least one area to be monitored based on the pixel information in the area image, and when it is determined that there is an abnormal event based on the vital signs information, behavioral data and / or passenger flow density, it determines the target location of the abnormal event and the corresponding location type. The target location is the location where the abnormal event occurred; The target handling method determination module is used to determine the target handling method based on the event attributes of the abnormal event, the target location, and the location type, so as to carry out emergency handling of the abnormal event based on the target handling method; the event attributes include abnormal body temperature, abnormal behavior, and abnormal passenger flow; The target handling method includes the event attributes of the abnormal event, the target location and the location type, the handling measures, the responsible user, and the handling process; The device further includes an abnormal event determination module, which is used to determine the existence of an abnormal event if the vital signs information is not within the standard vital signs range, the behavioral data contains abnormal behavioral characteristics, and / or the passenger flow density is greater than a preset density threshold.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the data processing method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the data processing method according to any one of claims 1-6.

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