Emergency call method and device based on artificial intelligence, equipment and medium

Abstract

The invention relates to the technical field of personnel behavior monitoring, in particular to an emergency call method, device and equipment based on artificial intelligence and a medium. Relatively dangerous areas in a hospital are marked and graded in advance, and key guardians of different age groups are configured based on the grades; when a plurality of old people enter the vicinity of the corresponding high-risk area, key monitoring objects are screened out based on human body parameter monitoring equipment worn by the old people; after the key monitored object enters the high-risk area, zooming is carried out through a camera corresponding to the high-risk area, and the body posture of the old people passing through the high-risk area is key monitored; due to the fact that automatic analysis and intelligent judgment are carried out through high-definition video monitoring, an Internet of Things sensor and big data in the whole process, the personnel monitoring task load in a nursing home or a sanatorium is effectively reduced, and the labor cost expenditure is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of personnel behavior monitoring, and specifically, to an emergency call method, device, equipment and medium based on artificial intelligence. Background Art

[0002] In today's society, as the aging problem becomes increasingly serious, nursing homes and sanatoriums are important places for elderly people's life care and health management. Improving their operational efficiency and service quality has become a key issue that needs to be solved urgently. Traditional manual monitoring methods not only have high labor costs, but also make it difficult to achieve comprehensive monitoring of every elderly person.

[0003] In recent years, the rapid development of artificial intelligence technology has provided new ideas for solving the above problems. Therefore, how to realize automatic identification, analysis and early warning of the daily behavior of the elderly, especially in dangerous areas such as square stairs, based on technologies such as high-definition video surveillance, Internet of Things sensors and big data analysis has become an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present invention is to provide an emergency call method, device, equipment and medium based on artificial intelligence to improve the problem that the above-mentioned traditional manual monitoring is difficult to monitor the elderly in some high-risk activity areas in nursing homes or sanatoriums around the clock without blind spots.

[0005] In order to achieve the above objectives, the present application provides the following technical solutions:

[0006] On the one hand, an embodiment of the present application provides an emergency call method based on artificial intelligence, which is applicable to a human parameter monitoring device, wherein the human parameter monitoring device is used to monitor the physiological data and position information of the wearer. The method includes: responding to the position information fed back by multiple human parameter monitoring devices, and marking the human parameter monitoring device located in a dangerous radiation zone as a risk device based on the position information; retrieving the wearer's health data corresponding to the risk device, and screening out high-risk devices from multiple risk devices based on the danger level corresponding to the dangerous radiation zone; constructing a forward range based on the running speed and vector of the high-risk device, and when the forward range intersects with the high-risk zone, determining the focus probability based on the historical motion trajectory of the high-risk device, and when the focus probability is greater than a first threshold, generating a first focus instruction based on the position code of the high-risk area, so that the corresponding surveillance camera focuses on the high-risk area according to a preset focal length, and feeds back a surveillance video; determining the body posture of the wearer of the high-risk device when passing through the high-risk area based on the surveillance video, and making an emergency call to the corresponding service personnel when the body posture is abnormal.

[0007] Optionally, determining the focus probability based on the historical motion trajectory of the high-risk device includes:

[0008] Dividing the historical running trajectory into a first running trajectory and a second running trajectory based on a time limit, wherein the first running trajectory is the running trajectory within the dangerous radiation zone today, and the second running trajectory is the activity trajectory within the past 1-2 weeks;

[0009] Marking a plurality of reference points on the first running track based on a preset time interval, generating a forward range corresponding to each reference point, and calculating a proportion of reference points that intersect the forward range with the high-risk area;

[0010] The number of times the second running trajectory crosses the high-risk area is counted, and the probability of the current high-risk device crossing the high-risk area is calculated by a weighted algorithm based on the proportion and the number of times, which is the focusing probability.

[0011] Optionally, the determining, based on the surveillance video, the body posture of the wearer of the high-risk device when passing through the high-risk area includes:

[0012] Construct a key reference plane based on the building characteristics in the high-risk area, and divide the key reference plane into position zones, thereby obtaining the position parameter range corresponding to each position zone;

[0013] The PAF algorithm is used to construct the joint points of multiple characters in the surveillance video, and the shoulder joint points and foot joint points of each character are extracted;

[0014] Based on the inclusion relationship between the foot joint point of each character and the key reference plane, characters that are not in the key reference plane are eliminated, thereby obtaining a plurality of first shoulder joint points and first foot joint points corresponding to the characters in the key reference plane;

[0015] Determine the position partition where the wearer of the high-risk device is located based on the position information fed back by the high-risk device and the position parameter range corresponding to each position partition, and then find the second shoulder joint point and the second foot joint point corresponding to the wearer of the high-risk device among multiple characters located in the key reference plane;

[0016] The body posture of the wearer of the high-risk device when passing through the high-risk area is determined based on the position changes of the second shoulder joint point and the second foot joint point when passing through the high-risk area.

[0017] Optionally, the determining the body posture of the wearer of the high-risk device when passing through the high-risk area based on the position changes of the second shoulder joint point and the second foot joint point when passing through the high-risk area includes:

[0018] Detect the integrity of the joint points corresponding to the wearer of the high-risk equipment in real time, and identify the second shoulder joint point and the second foot joint point in the case of integrity, and monitor the height difference between the two in real time, and when the height difference is less than a first threshold, calculate the time for the height difference to collapse to the first threshold, recorded as the first time, and identify the head pixel point coordinate set and the foot pixel point coordinate set of the suspected wrestling figure through a shape-based image feature extraction algorithm after the height difference is less than the first threshold;

[0019] Determine the key reference plane where the feet of the suspected wrestling figure are located based on the set of foot pixel coordinates, and record it as the wrestling reference plane;

[0020] The estimated collapse degree of the suspected wrestling figure is determined based on the wrestling reference surface, the foot pixel point coordinate set and the head pixel point coordinate set, and then based on the estimated collapse degree and the first duration weighted determination, whether the suspected wrestling figure is in a falling state or a sitting state.

[0021] Optionally, the step of determining the estimated collapse degree of the suspected wrestling character based on the wrestling reference surface, the foot pixel point coordinate set, and the head pixel point coordinate set includes:

[0022] Determine the first center coordinates based on the set of pixel coordinates of the lower part, and determine the second center coordinates based on the set of pixel coordinates of the head;

[0023] The corresponding collapse degree estimation is evaluated based on the longitudinal arrangement order of the first center coordinate, the second center coordinate and the wrestling reference surface and the height difference between them. The greater the difference between the longitudinal arrangement order and the preset arrangement order, the higher the collapse degree estimation is. The greater the difference between the height difference between the first center coordinate, the second center coordinate and the wrestling reference surface and the preset threshold range, the higher the collapse degree estimation is. The collapse degree estimation is used to characterize the degree of aggregation between joint points. The greater the collapse degree estimation is, the greater the probability of the target person falling is.

[0024] In a second aspect, this embodiment provides an emergency call device based on artificial intelligence, characterized in that the device includes:

[0025] A first calculation module is used to respond to the position information fed back by the multiple human parameter monitoring devices, and mark the human parameter monitoring devices located in the dangerous radiation zone as risky devices based on the position information;

[0026] A second calculation module is used to retrieve the wearer's health data corresponding to the risky device, and screen out high-risk devices from multiple risky devices based on the risk level corresponding to the dangerous radiation area;

[0027] A third calculation module is used to construct an advance range based on the running speed and vector of the high-risk device, and when the advance range intersects with the high-risk area, determine the focus probability based on the historical motion trajectory of the high-risk device, and when the focus probability is greater than a first threshold, generate a first focus instruction based on the position code of the high-risk area, so that the corresponding surveillance camera focuses on the high-risk area according to a preset focal length, and feeds back the surveillance video;

[0028] The early warning module is used to determine the body posture of the wearer of the high-risk equipment when passing through the high-risk area based on the surveillance video, and to urgently call the corresponding service personnel when the body posture is abnormal.

[0029] In a third aspect, an embodiment of the present application provides an emergency call device based on artificial intelligence, wherein the device includes a memory and a processor.

[0030] The memory is used to store computer programs; the processor is used to implement the steps of the above-mentioned emergency call method based on artificial intelligence when executing the computer program.

[0031] In a fourth aspect, an embodiment of the present application provides a medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned artificial intelligence-based emergency call method are implemented.

[0032] The beneficial effects of the present invention are:

[0033] The present invention marks and grades the more dangerous areas in the hospital in advance, configures key monitoring groups of different age groups based on the grades, and configures corresponding cameras for monitoring the area. When multiple elderly people enter the vicinity of the corresponding high-risk area, the key monitoring objects are screened out based on the human body parameter monitoring equipment worn by the elderly. After the key monitoring objects enter the high-risk area, the camera corresponding to the high-risk area is used to zoom and focus on monitoring the body posture of the elderly passing through the high-risk area, and then detect whether the elderly have fallen. Since the entire process is automatically analyzed and intelligently determined through high-definition video monitoring, Internet of Things sensors and big data, the workload of personnel monitoring in nursing homes or sanatoriums is effectively reduced, and the labor cost expenditure is reduced.

[0034] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 It is a schematic flow chart of an emergency call method based on artificial intelligence described in an embodiment of the present invention;

[0037] Figure 2 It is a schematic diagram of the structure of an emergency call device based on artificial intelligence described in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] It should be noted that similar reference numerals or letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0040] Embodiment 1:

[0041] like Figure 1 As shown, this embodiment provides an emergency call method based on artificial intelligence, which is applicable to a human body parameter monitoring device, wherein the human body parameter monitoring device is used to monitor the physiological data and location information of the wearer, and the method includes step S100, step S200, step S300 and step S400.

[0042] Step S100, in response to the location information fed back by multiple human parameter monitoring devices, in order to provide more accurate positioning, an additional positioning base station is configured in the hospital, and based on the location information, the human parameter monitoring devices located in the dangerous radiation zone are marked as risky devices. Before the overall monitoring system enters the site, it is necessary to manually classify and number the buildings in the hospital according to the distribution of the buildings, and divide them into levels. The classification of the danger level of the site is mainly used to screen the key monitoring groups during the later monitoring. Since the age span of users in nursing homes or sanatoriums is large, the early staff will divide the occupants into levels according to their age and specific physical conditions when entering the information of the occupants, thereby facilitating the intelligent screening and monitoring of the later system, that is, in the same place, such as an activity square with a small number of stairs, for people of different age groups, the danger level is different, and the camera gives priority to monitoring the older or poorer physical fitness people in the area;

[0043] Step S200, retrieve the health data of the wearer corresponding to the risk device, and screen out high-risk devices from multiple risk devices based on the risk level corresponding to the dangerous radiation zone. The dangerous radiation zone is an area artificially divided in the early stage, which is used to characterize the area close to the high-risk zone, such as the flat area close to the stairs. The system will determine whether there are elderly people who are older or whose physical fitness is not suitable for activities in the current dangerous area in multiple dangerous radiation areas based on the risk level corresponding to the dangerous radiation zone, and mark the human parameter monitoring device worn by the corresponding elderly as a high-risk device;

[0044] Step S300, based on the running speed and vector of the high-risk equipment, construct the forward range, and when the forward range intersects with the high-risk area, determine the focus probability based on the historical motion trajectory of the high-risk equipment, and when the focus probability is greater than the first threshold, generate a first focus instruction based on the position code of the high-risk area, so that the corresponding surveillance camera focuses on the high-risk area according to the preset focal length, and feeds back the surveillance video. In some special occasions, such as on the convex platform for sunbathing or on the edge of the lower step of a large flat area; constructing the forward range is used to focus the camera in advance, thereby increasing the detection accuracy of the later posture detection algorithm. In principle, the camera is used to monitor the entire area, and will only focus when a dangerous person is about to enter the preset dangerous area, and the focal length is preset, and it cannot be moved or locked according to the person, etc.; the forward range is the area within 45 degrees to the left and right of the current vector direction, and the size of the fan-shaped forward range is proportional to the current running speed;

[0045] Step S400: determining the body posture of the wearer of the high-risk device when passing through the high-risk area based on the surveillance video, and calling the corresponding service personnel urgently when the body posture is abnormal.

[0046] The artificial intelligence-based emergency call described in this embodiment marks and grades the more dangerous areas in the hospital in advance, configures key monitoring groups of different age groups based on the grades, and configures corresponding cameras for monitoring the area. When multiple elderly people enter the vicinity of the corresponding high-risk area, key monitoring objects are screened out based on the human parameter monitoring equipment worn by the elderly. After the key monitoring objects enter the high-risk area, the camera corresponding to the high-risk area is used to zoom and focus on monitoring the body posture of the elderly when passing through the high-risk area, and then detect whether the elderly have fallen, etc. Since the entire process is automatically analyzed and intelligently determined through high-definition video monitoring, Internet of Things sensors and big data, the workload of personnel monitoring in nursing homes or sanatoriums is effectively reduced, and the labor cost expenditure is reduced.

[0047] For some elderly people who regularly wander around the square stairs, it is necessary to determine the probability of them going down the stairs based on their historical running trajectories, and dispatch surveillance cameras based on the probability. For some people who often wander around and basically do not go down the stairs, it is unnecessary to dispatch cameras frequently, which may cause damage to the cameras. That is, the implementation method of determining the focus probability based on the historical movement trajectory of high-risk equipment in step S300 is:

[0048] Step S310: dividing the historical running trajectory into a first running trajectory and a second running trajectory based on a time limit, wherein the first running trajectory is the running trajectory within the dangerous radiation zone today, and the second running trajectory is the activity trajectory within the past 1-2 weeks;

[0049] Step S320: Mark multiple reference points on the first running track based on a preset time interval, generate a forward range corresponding to each reference point, and calculate the proportion of reference points that intersect with the high-risk area in the forward range, that is, although it lingers near the stairs but always keeps a certain distance from the stairs, at this time, the proportion of reference points that intersect with the high-risk area in the forward range is low, and the probability of going down the stairs is low;

[0050] Step S330: Count the number of times the second running trajectory crosses the high-risk area, and calculate the probability of the current high-risk device crossing the high-risk area through a weighted algorithm based on the proportion and the number of times, which is the focus probability.

[0051] The implementation method of determining the body posture of the wearer of the high-risk device passing through the high-risk area based on the surveillance video in step S400 is as follows:

[0052] Step S410: construct a key reference plane based on the building features in the high-risk area, and divide the key reference plane into position zones, thereby obtaining a position parameter range corresponding to each position zone. For the square steps, the key reference plane is the plane corresponding to each step;

[0053] Step S420: construct the joint points of multiple characters in the surveillance video by using the PAF algorithm, and extract the shoulder joint points and foot joint points of each character;

[0054] Step S430: based on the inclusion relationship between the foot joint point of each character and the key reference plane, characters that are not in the key reference plane are eliminated, thereby obtaining a plurality of first shoulder joint points and first foot joint points corresponding to the characters in the key reference plane;

[0055] Step S440, based on the position information fed back by the high-risk device and the position parameter range corresponding to each position partition, determine the position partition where the wearer of the high-risk device is located, and then find the second shoulder joint point and the second foot joint point corresponding to the wearer of the high-risk device among multiple people located in the key reference plane, that is, when multiple elderly people approach the edge of the stairs at the same time, there are both subjects who do not need to be monitored and subjects who are key monitored in the crowd, and it is necessary to further mark the key monitored subjects in the surveillance video, so it is necessary to perform position partitioning on the key reference plane, and mark the target person in the video based on the position data range threshold corresponding to each zone and the position information fed back by the human body parameter monitoring device;

[0056] Step S450: determining the body posture of the wearer of the high-risk device when passing through the high-risk area based on the position changes of the second shoulder joint point and the second foot joint point when passing through the high-risk area.

[0057] The method for determining the body posture of the wearer of the high-risk device when passing through the high-risk area based on the position changes of the second shoulder joint point and the second foot joint point when passing through the high-risk area in step S450 is as follows:

[0058] Step S451, detecting the integrity of the joint points corresponding to the wearer of the high-risk equipment in real time, and identifying the second shoulder joint point and the second foot joint point in the case of integrity, and monitoring the height difference between the two in real time, and when the height difference is less than a first threshold, calculating the time for the height difference to collapse to the first threshold, recorded as the first time, and identifying the head pixel point coordinate set and the foot pixel point coordinate set of the suspected wrestler through a shape-based image feature extraction algorithm after the height difference is less than the first threshold;

[0059] Step S452: determining the key reference plane where the feet of the suspected wrestling figure are located based on the foot pixel point coordinate set, and recording it as the wrestling reference plane;

[0060] Step S453, determining an estimated collapse degree of the suspected wrestling figure based on the wrestling reference surface, the foot pixel point coordinate set and the head pixel point coordinate set, and then determining whether the suspected wrestling figure is in a falling state or a sitting state based on the estimated collapse degree and the first duration weighting.

[0061] To avoid misjudgment of falling in human posture recognition, that is, how to distinguish whether the target person is sitting down on the edge of the stairs or falling down, these two are similar in human posture recognition based on the PAF algorithm, that is, the joints of the person will converge / collapse toward the center. Therefore, this embodiment further determines whether the target person is sitting down or falling down based on the collapse speed and the positional relationship between the key joints after collapse and the corresponding reference plane.

[0062] Secondly, it should be noted that the PAF algorithm is more adept at recognizing the standing posture of the human body, but it is more difficult to recognize the sitting or wrestling posture. Therefore, the recognition after collapse in this embodiment is based on the shape-based image features to identify key positions, which are used to find the pixel clusters that best reflect the head and foot areas in the video pattern, and determine the body posture based on the sorting relationship between the center positions of the pixel clusters and the distance relationship between each other.

[0063] The specific implementation method of determining the estimated collapse degree of the suspected wrestling figure based on the wrestling reference surface, the foot pixel point coordinate set and the head pixel point coordinate set in step S453 is:

[0064] Step S4531, determining the first center coordinates based on the body pixel point coordinate set, and determining the second center coordinates based on the head pixel point coordinate set;

[0065] Step S4531: Evaluate the corresponding collapse degree based on the longitudinal arrangement order of the first center coordinate, the second center coordinate and the wrestling reference surface and the height difference between them. The greater the difference between the longitudinal arrangement order and the preset arrangement order, the higher the collapse degree estimate. The greater the difference between the height difference between the first center coordinate, the second center coordinate and the wrestling reference surface and the preset threshold range, the higher the collapse degree estimate. The collapse degree estimate is used to characterize the degree of aggregation between the joints. The greater the collapse degree estimate, the greater the probability of the target person falling.

[0066] In the sitting posture, the head, feet, and the reference plane where the feet are located will have a strict height order and a distance threshold range between the two points. The more chaotic the height order and the greater the difference between the height difference between any two points and the corresponding distance threshold, the greater the probability of falling.

[0067] Embodiment 2:

[0068] This embodiment provides an emergency call device based on artificial intelligence, the device comprising:

[0069] A first calculation module is used to respond to the position information fed back by the multiple human parameter monitoring devices, and mark the human parameter monitoring devices located in the dangerous radiation zone as risky devices based on the position information;

[0070] A second calculation module is used to retrieve the wearer's health data corresponding to the risky device, and screen out high-risk devices from multiple risky devices based on the risk level corresponding to the dangerous radiation area;

[0071] A third calculation module is used to construct an advance range based on the running speed and vector of the high-risk device, and when the advance range intersects with the high-risk area, determine the focus probability based on the historical motion trajectory of the high-risk device, and when the focus probability is greater than a first threshold, generate a first focus instruction based on the position code of the high-risk area, so that the corresponding surveillance camera focuses on the high-risk area according to a preset focal length, and feeds back the surveillance video;

[0072] The early warning module is used to determine the body posture of the wearer of the high-risk equipment when passing through the high-risk area based on the surveillance video, and to urgently call the corresponding service personnel when the body posture is abnormal.

[0073] It should be noted that, regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0074] Embodiment 3:

[0075] Corresponding to the above method embodiments, the embodiments of the present disclosure also provide an emergency call device based on artificial intelligence. The emergency call device based on artificial intelligence described below and the emergency call method based on artificial intelligence described above can refer to each other.

[0076] Figure 2 FIG. 8 is a block diagram of an emergency call device 800 based on artificial intelligence according to an exemplary embodiment. Figure 2 As shown, the electronic device 800 may include: a processor 801 and a memory 802. The electronic device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0077] The processor 801 is used to control the overall operation of the electronic device 800 to complete all or part of the steps in the emergency call method based on artificial intelligence. The memory 802 is used to store various types of data to support the operation of the electronic device 800. These data may include, for example, instructions for any application or method used to operate on the electronic device 800, and application-related data, such as contact data, messages sent and received, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, which is used to receive external audio signals. The received audio signal may be further stored in the memory 802 or sent through the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the electronic device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 may include: Wi-Fi module, Bluetooth module, NFC module.

[0078] In an exemplary embodiment, the electronic device 800 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned artificial intelligence-based emergency call method.

[0079] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the emergency call method based on artificial intelligence are implemented. For example, the computer-readable storage medium can be the memory 802 including the program instructions, and the program instructions can be executed by the processor 801 of the electronic device 800 to complete the emergency call method based on artificial intelligence.

[0080] Embodiment 4:

[0081] Corresponding to the above method embodiment, the embodiment of the present disclosure further provides a readable storage medium. The readable storage medium described below and the artificial intelligence-based emergency call method described above can refer to each other.

[0082] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the emergency call method based on artificial intelligence in the above method embodiment.

[0083] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0084] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An emergency call method based on artificial intelligence, applicable to a human parameter monitoring device, wherein the human parameter monitoring device is used to monitor the wearer's physiological data and location information, characterized in that: The method comprises: In response to position information fed back by a plurality of human parameter monitoring devices, and based on the position information, marking the human parameter monitoring devices located in a dangerous radiation zone as risky devices; Retrieving health data of the wearer corresponding to the risky device, and screening out high-risk devices from multiple risky devices based on the risk level corresponding to the dangerous radiation area; Based on the running speed and vector of the high-risk equipment, an advancing range is constructed, and when the advancing range intersects with the high-risk area, a focusing probability is determined based on the historical motion trajectory of the high-risk equipment, and when the focusing probability is greater than a first threshold, a first focusing instruction is generated based on the position code of the high-risk area, so that the corresponding surveillance camera focuses on the high-risk area according to a preset focal length and feeds back a surveillance video; The body posture of the wearer of the high-risk equipment passing through the high-risk area is determined based on the surveillance video, and when the body posture is abnormal, an emergency call is made to the corresponding service personnel.

2. The artificial intelligence-based emergency call method according to claim 1, characterized in that: The determining of the focusing probability based on the historical motion trajectory of the high-risk device includes: The historical running trajectory is divided into a first running trajectory and a second running trajectory based on a time limit, wherein the first running trajectory is the running trajectory within the dangerous radiation zone today, and the second running trajectory is the activity trajectory within the past 1-2 weeks; Marking a plurality of reference points on the first running track based on a preset time interval, generating a forward range corresponding to each reference point, and calculating a proportion of reference points that intersect the forward range with the high-risk area; The number of times the second running trajectory crosses the high-risk area is counted, and the probability of the current high-risk device crossing the high-risk area is calculated by a weighted algorithm based on the proportion and the number of times, which is the focusing probability.

3. The artificial intelligence-based emergency call method according to claim 1, characterized in that: The method of determining the body posture of the wearer of the high-risk device when passing through the high-risk area based on the surveillance video includes: Construct a key reference plane based on the building characteristics in the high-risk area, and divide the key reference plane into position zones, thereby obtaining the position parameter range corresponding to each position zone; The PAF algorithm is used to construct the joint points of multiple characters in the surveillance video, and the shoulder joint points and foot joint points of each character are extracted; Based on the inclusion relationship between the foot joint point of each character and the key reference plane, characters that are not in the key reference plane are eliminated, thereby obtaining a plurality of first shoulder joint points and first foot joint points corresponding to the characters in the key reference plane; Determine the position partition where the wearer of the high-risk device is located based on the position information fed back by the high-risk device and the position parameter range corresponding to each position partition, and then find the second shoulder joint point and the second foot joint point corresponding to the wearer of the high-risk device among the multiple characters located in the key reference plane; The body posture of the wearer of the high-risk device when passing through the high-risk area is determined based on the position changes of the second shoulder joint point and the second foot joint point when passing through the high-risk area.

4. The artificial intelligence-based emergency call method according to claim 3, characterized in that: The method of determining the body posture of the wearer of the high-risk device when passing through the high-risk area based on the position changes of the second shoulder joint point and the second foot joint point when passing through the high-risk area includes: Detect the integrity of the joint points corresponding to the wearer of the high-risk equipment in real time, and identify the second shoulder joint point and the second foot joint point in the case of integrity, and monitor the height difference between the two in real time, and when the height difference is less than a first threshold, calculate the time for the height difference to collapse to the first threshold, recorded as the first time, and identify the head pixel point coordinate set and the foot pixel point coordinate set of the suspected wrestling figure through a shape-based image feature extraction algorithm after the height difference is less than the first threshold; Determine the key reference plane where the feet of the suspected wrestling figure are located based on the set of foot pixel coordinates, and record it as the wrestling reference plane; The estimated collapse degree of the suspected wrestling figure is determined based on the wrestling reference surface, the foot pixel point coordinate set and the head pixel point coordinate set, and then based on the estimated collapse degree and the first duration weighted determination, whether the suspected wrestling figure is in a falling state or a sitting state.

5. The artificial intelligence-based emergency call method according to claim 4, characterized in that: The method of determining the estimated collapse degree of the suspected wrestling figure based on the wrestling reference surface, the foot pixel point coordinate set, and the head pixel point coordinate set includes: Determine the first center coordinates based on the set of pixel coordinates of the lower part, and determine the second center coordinates based on the set of pixel coordinates of the head; The corresponding collapse degree estimation is evaluated based on the longitudinal arrangement order of the first center coordinate, the second center coordinate and the wrestling reference surface and the height difference between them. The greater the difference between the longitudinal arrangement order and the preset arrangement order, the higher the collapse degree estimation is. The greater the difference between the height difference between the first center coordinate, the second center coordinate and the wrestling reference surface and the preset threshold range, the higher the collapse degree estimation is. The collapse degree estimation is used to characterize the degree of aggregation between joint points. The greater the collapse degree estimation is, the greater the probability of the target person falling is.

6. An emergency call device based on artificial intelligence, characterized in that: The device comprises: A first calculation module is used to respond to the position information fed back by the multiple human parameter monitoring devices, and mark the human parameter monitoring devices located in the dangerous radiation zone as risky devices based on the position information; A second calculation module is used to retrieve the wearer's health data corresponding to the risky device, and screen out high-risk devices from multiple risky devices based on the risk level corresponding to the dangerous radiation area; A third calculation module is used to construct an advance range based on the running speed and vector of the high-risk device, and when the advance range intersects with the high-risk area, determine the focus probability based on the historical motion trajectory of the high-risk device, and when the focus probability is greater than a first threshold, generate a first focus instruction based on the position code of the high-risk area, so that the corresponding surveillance camera focuses on the high-risk area according to a preset focal length, and feeds back the surveillance video; The early warning module is used to determine the body posture of the wearer of the high-risk equipment when passing through the high-risk area based on the surveillance video, and to urgently call the corresponding service personnel when the body posture is abnormal.

7. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, used to implement the method steps described in any one of claims 1-5 when executing a program stored in a memory.

8. A medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method steps described in any one of claims 1 to 5 are implemented.

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