Risk early warning method, device and electronic equipment for high-altitude operation

Abstract

The application discloses a high-altitude operation risk early warning method and device and electronic equipment. It relates to the field of power systems. The method comprises the following steps: monitoring M kinds of characteristic information of a target object at a target position, obtaining a characteristic value of each kind of characteristic information, and obtaining M characteristic values; identifying the current motion state of the target object, determining the threshold value corresponding to each kind of characteristic information according to the current motion state, and obtaining M threshold values; calculating the characteristic value of each kind of characteristic information and the corresponding threshold value, and obtaining M risk assessment coefficients; determining the risk level of the target object at the target position according to the M risk assessment coefficients, and sending the risk level to the target object. Through the application, the problem of low accuracy of identifying operation risks by monitoring the operation process of high-altitude operation personnel in related technologies is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power systems, in particular, to a risk early warning method and device for aerial work and an electronic device. BACKGROUND

[0002] With the laying range of power lines becoming wider and wider, the cable height is constantly increasing, and the frequency of aerial work is also increasing. With the increase of the frequency of aerial work, the safety problem of aerial work has also been widely concerned. According to the standard, work at a height of 2 meters or more (including 2 meters) above the reference surface of falling height, and the possibility of falling accident is called high work, and aerial work is also one of high work, which generally refers to the case where the height is more than 20 meters. Aerial work has the following characteristics: more open-air work, high work intensity, complex and variable work scene, frequent personnel change, etc. These characteristics are prone to cause personnel casualties, of which the most common is the casualties caused by aerial falling.

[0003] However, when ensuring the safety of aerial workers, the method usually adopted is to judge whether the operation process of the aerial worker has a violation behavior, so as to ensure the standardization of the operation process, and thus to avoid safety accidents. However, the safety problem of aerial work cannot be completely covered by standardized operation, and various unexpected situations may occur, resulting in safety hazards of aerial work.

[0004] At present, there is no effective solution to the problem that the method of monitoring the operation process of the aerial worker in the related art has low accuracy in identifying work risks. SUMMARY

[0005] The present application provides a risk early warning method and device for aerial work and an electronic device to solve the problem that the method of monitoring the operation process of the aerial worker in the related art has low accuracy in identifying work risks.

[0006] According to one aspect of the present application, a risk early warning method for aerial work is provided. The method comprises: monitoring M kinds of feature information of a target object at a target position, and obtaining a feature value of each kind of feature information to obtain M feature values, wherein M is a positive integer; identifying a current motion state of the target object, and determining a threshold value corresponding to each kind of feature information according to the current motion state to obtain M threshold values; calculating the feature value of each kind of feature information with the corresponding threshold value to obtain M risk assessment coefficients; determining a risk level of the target object at the target position according to the M risk assessment coefficients, and sending the risk level to the target object according to the risk level.

[0007] Optionally, M kinds of feature information of the target object at the target position are monitored, and a feature value of each kind of feature information is obtained, to obtain M feature values, wherein the M feature values at least include one of the following: image information of the target object is obtained, and the image information is input into a posture risk identification model to obtain a posture feature value, wherein the posture risk identification model is trained by a plurality of sample data, and each sample data is composed of a sample posture image and a corresponding risk score; whether the wearable device of the target object is located at a preset position is determined according to the image information, and a position feature value is obtained according to a determination result; a tension value and an acceleration value collected by a sensor are obtained, to obtain an acting force feature value, wherein the sensor is arranged in the wearable device of the target object; environment data of the target position where the target object is located is obtained, and the environment data is determined as an environment feature value.

[0008] Optionally, in the case that the M feature values include the posture feature value, the position feature value, the acting force feature value and the environment feature value, a current motion state of the target object is identified, and a threshold value corresponding to each kind of feature information is determined according to the current motion state, to obtain M threshold values, including: in the case that the current motion state of the target object is a static state, a threshold value corresponding to the posture feature value is determined as a first threshold value, a threshold value corresponding to the position feature value is determined as a second threshold value, a threshold value corresponding to the acting force feature value is determined as a third threshold value, and a threshold value corresponding to the environment feature value is determined as a fourth threshold value; in the case that the current motion state of the target object is a moving state, a threshold value corresponding to the posture feature value is determined as the first threshold value, a threshold value corresponding to the position feature value is determined as the second threshold value, a threshold value corresponding to the acting force feature value is determined as a fifth threshold value, and a threshold value corresponding to the environment feature value is determined as the fourth threshold value, wherein the fifth threshold value is greater than the third threshold value.

[0009] Optionally, whether the wearable device of the target object is located at the preset position is determined according to the image information, and a position feature value is obtained according to a determination result, including: in the case that the wearable device of the target object is located at the preset position, the determination result is determined as no risk, and the position feature value is determined as a first target value, wherein the first target value is less than the second threshold value; in the case that the wearable device of the target object is not located at the preset position, the determination result is determined as risk, and the position feature value is determined as a second target value, wherein the second target value is greater than the second threshold value.

[0010] Optionally, the feature value of each kind of feature information is calculated with the corresponding threshold value, to obtain M risk assessment coefficients, including: each feature value is divided by the corresponding threshold value, to obtain a risk assessment coefficient of each feature value.

[0011] Optionally, determining the risk level of the target object at the target position according to the M risk evaluation coefficients comprises: obtaining a risk evaluation standard, wherein the risk evaluation standard comprises a plurality of standard values and feature information corresponding to each standard value, and the standard value is used to determine the risk level of the feature information; determining whether there is target feature information with a risk evaluation coefficient greater than the corresponding standard value; in the case that there is target feature information, obtaining the number of target feature information and determining whether the number of target feature information is greater than a preset number; in the case that the number of target feature information is greater than the preset number, determining that the risk level is a first risk level; in the case that the number of target feature information is less than or equal to the preset number, determining that the risk level is a second risk level, wherein the second risk level is less than the first risk level; in the case that there is no target feature information, determining that the risk level is a third risk level, wherein the third risk level is less than the second risk level.

[0012] Optionally, in the case that there is no target feature information, the method further comprises: identifying the current motion state of the target object, and obtaining the weight of each kind of feature information according to the current motion state to obtain M weights; performing weighted summation on the M risk evaluation coefficients according to the M weights to obtain a target risk coefficient; obtaining a risk coefficient threshold under the current motion state, and determining whether the target risk coefficient is greater than the risk coefficient threshold; in the case that the target risk coefficient is greater than the risk coefficient threshold, determining that the risk level is the second risk level; in the case that the target risk coefficient is less than or equal to the risk coefficient threshold, determining that the risk level is the third risk level.

[0013] According to another aspect of the present application, a risk warning device for high-altitude operation is provided. The device comprises: a first obtaining unit for monitoring M kinds of feature information of a target object at a target position and obtaining a feature value of each kind of feature information to obtain M feature values, wherein M is a positive integer; a first determining unit for identifying the current motion state of the target object and determining a threshold value corresponding to each kind of feature information according to the current motion state to obtain M threshold values; a first calculating unit for calculating the feature value of each kind of feature information and the corresponding threshold value to obtain M risk evaluation coefficients; and a second determining unit for determining the risk level of the target object at the target position according to the M risk evaluation coefficients and sending the risk level to the target object.

[0014] According to another aspect of the present application, a computer program product is also provided, comprising a computer program which, when executed by a processor, implements a risk warning method for high-altitude operation.

[0015] According to another aspect of the present application, an electronic device is also provided, comprising one or more processors and a memory; the memory stores computer readable instructions, and the processor is configured to execute the computer readable instructions, wherein the computer readable instructions perform a risk warning method for high-altitude operation when executed.

[0016] According to the present application, the following steps are adopted: M kinds of feature information of a target object at a target position are monitored, and a feature value of each kind of feature information is obtained, to obtain M feature values, wherein M is a positive integer; a current motion state of the target object is identified, and a threshold value corresponding to each kind of feature information is determined according to the current motion state, to obtain M threshold values; a feature value of each kind of feature information is calculated with the corresponding threshold value, to obtain M risk assessment coefficients; a risk level of the target object at the target position is determined according to the M risk assessment coefficients, and is sent to the target object according to the risk level. The problem that the accuracy of identifying operation risk by monitoring the operation process of a high-altitude operation personnel in the related art is low is solved. By monitoring the feature information of the target object in multiple dimensions, the safety situation of the high-altitude operation personnel is comprehensively monitored, and in the case that a high risk level is monitored, the falling situation that is about to occur or has occurred is warned or alarmed in a timely manner according to the risk level, so as to facilitate the safety supervisor to issue relevant instructions or carry out rescue actions in the first time, reduce the risk of accidents and the damage caused by accidents, and thus the effect of improving the accuracy and timeliness of operation risk warning is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings, which form a part of the present application, are intended to provide further understanding of the present application, and the illustrative embodiments of the present application and their description serve the purpose of explaining the present application. The accompanying drawings should not be construed as an inappropriate limitation on the present application. In the drawings:

[0018] Figure 1 is a flowchart of a risk warning method for high-altitude operation provided according to an embodiment of the present application;

[0019] Figure 2 is a schematic diagram of a risk warning system for high-altitude operation provided according to an embodiment of the present application;

[0020] Figure 3 is a schematic diagram of a risk warning device for high-altitude operation provided according to an embodiment of the present application;

[0021] Figure 4 is a schematic diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0023] In order to enable persons skilled in the art to better understand the technical scheme of the present application, the technical scheme in the embodiments of the present application will be clearly and completely described 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, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second" and the like in the description and claims of the present application and the above 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 addition, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0025] It should be noted that the high-altitude operation risk early warning method, device and electronic equipment determined by the present disclosure can be used in the field of power systems, and can also be used in any field other than the field of power systems. The application field of the high-altitude operation risk early warning method, device and electronic equipment determined by the present disclosure is not limited.

[0026] It should be noted that the information, user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) collected in the present application are all information and data authorized by the user or authorized by all parties. The collection, storage, use, processing, transmission, provision, disclosure and application of related data comply with relevant laws, regulations and standards in relevant regions, take necessary security measures, do not violate public order and good customs, and provide corresponding operation portal for users to choose authorized use or refuse to use. For example, the system and related users or institutions are provided with an interface. Before obtaining the relevant information, the interface needs to send a request to the aforementioned user or institution, and after receiving the consent information feedback from the aforementioned user or institution, the relevant information is obtained.

[0027] Embodiment 1

[0028] According to the embodiments of the present application, a high-altitude operation risk early warning method is provided.

[0029] Figure 1is a flowchart of a risk early warning method for high-altitude operation provided by an embodiment of the present application. As shown in Figure 1 the method comprises the following steps:

[0030] In step S101, M kinds of characteristic information of a target object at a target position are monitored, and a characteristic value of each kind of characteristic information is obtained, thereby obtaining M characteristic values, wherein M is a positive integer.

[0031] Specifically, the target object can be a person performing high-altitude operation, and the target position can be a position where the target object is located, such as a high-altitude position. When the target object is at the high-altitude position to perform high-altitude operation, the current operation risk of the target object can be determined by monitoring multiple characteristic information of the target object according to the characteristic value of each characteristic information, so as to feed back alarm information to the target object according to the operation risk, thereby ensuring the operation safety of the target object.

[0032] It should be noted that the characteristic information can include human posture of the operator, tension of the safety auxiliary equipment of the operator, such as a safety belt, high-altitude acceleration change of the operator, high-altitude wind force, high-altitude body temperature of the operator, and environmental temperature, and other multi-dimensional characteristic information, so as to determine the operation risk degree of the operator according to the characteristic value of the characteristic information in different dimensions.

[0033] In step S102, a current motion state of the target object is identified, and a threshold value corresponding to each kind of characteristic information is determined according to the current motion state, thereby obtaining M threshold values.

[0034] Specifically, when determining whether the target object has a safety risk at the current time, the determination can be made by determining whether the characteristic value of the target object is abnormal. Therefore, a threshold value needs to be set for each characteristic information, so that whether the characteristic value is abnormal can be determined by comparing the characteristic value with the threshold value, and whether the target object has a safety risk can be determined.

[0035] It should be noted that the characteristic value of the same characteristic information under different motion states of the target object will be different. Therefore, when setting the threshold value, different threshold values need to be set for the same characteristic information according to different motion states, so as to ensure the accuracy when comparing the characteristic value with the threshold value.

[0036] For example, in the case where the motion state is two states of static and moving, the threshold value of each characteristic information is different, thereby obtaining M threshold values under the static state and M threshold values under the moving state.

[0037] It should be noted that the static and moving in the present application refer to the overall operation of the target object, such as overall moving to the left, overall moving downward, etc. The action of the target object itself does not belong to the motion state.

[0038] Step S103, calculate the feature value of each feature information and the corresponding threshold value to obtain M risk assessment coefficients.

[0039] Specifically, after obtaining the threshold value and the feature value, the feature value and the threshold value can be calculated to obtain the risk assessment coefficient between the feature value and the threshold value, and then the risk level can be determined according to the risk assessment coefficient.

[0040] For example, in the case of wind force as the feature information, the feature value can be: current wind force 1 level, and in the case of threshold value 5 levels, the risk assessment coefficient can be 1 / 5=20%, at this time, it can be determined that the risk under the wind force dimension is low risk.

[0041] Step S104, determine the risk level of the target object at the target location according to the M risk assessment coefficients, and send the risk level to the target object.

[0042] Specifically, after comparing the feature value of each feature information with the threshold value, M risk assessment coefficients can be obtained, at this time, the risk level can be determined according to the M risk assessment coefficients, so that different alarm information can be sent to the target object according to the risk level, so as to timely warn or alarm the falling situation that will occur or has occurred, so as to facilitate the safety supervisor to issue relevant instructions or carry out rescue action in the first time, reduce the risk of accident and the damage caused by accident.

[0043] For example, in the case that the risk assessment coefficient corresponding to the wind force is 20%, the risk assessment coefficient corresponding to the temperature is 10%, and the risk assessment coefficient corresponding to the posture is 30%, it can be determined that the risk level is low risk, and a green light is displayed on the equipment of the target object, so as to inform the target object that the current safety risk is low risk, and the high-altitude work can continue.

[0044] The high-altitude operation risk early warning method provided in the embodiments of the present application monitors M types of feature information of a target object at a target position, and obtains a feature value of each type of feature information, to obtain M feature values, wherein M is a positive integer; a current motion state of the target object is recognized, and a threshold value corresponding to each type of feature information is determined according to the current motion state, to obtain M threshold values; the feature value of each type of feature information is calculated with the corresponding threshold value, to obtain M risk assessment coefficients; a risk level of the target object at the target position is determined according to the M risk assessment coefficients, and is sent to the target object according to the risk level. The problem that the accuracy of identifying operation risks by monitoring the operation process of a high-altitude operation personnel in the related art is low is solved. The feature information of the target object in multiple dimensions is monitored, to comprehensively monitor the safety of the high-altitude operation personnel, and in the case that a high risk level is monitored, the falling situation that is about to occur or has occurred is early warned or alarmed in a timely manner according to the risk level, so as to facilitate a safety supervisor to issue relevant instructions or carry out rescue actions at the first time, reduce the risk of accidents and the damage caused by accidents, and thus the effect of improving the accuracy and timeliness of operation risk early warning is achieved.

[0045] Optionally, in the high-altitude operation risk early warning method provided in the embodiments of the present application, M types of feature information of a target object at a target position are monitored, and a feature value of each type of feature information is obtained, to obtain M feature values, wherein the M feature values at least include one of the following: image information of the target object is obtained, and the image information is input into a posture risk identification model to obtain a posture feature value, wherein the posture risk identification model is obtained by training a plurality of sample data, and each sample data is composed of a sample posture image and a corresponding risk score; whether a wearable device of the target object is located at a preset position is determined according to the image information, and a position feature value is obtained according to the determination result; a tension value and an acceleration value collected by a sensor are obtained, to obtain an acting force feature value, wherein the sensor is arranged in the wearable device of the target object; environment data of the target position where the target object is located is obtained, and the environment data is determined as an environment feature value.

[0046] Specifically, when the feature values are obtained, the feature values of the following several types of feature information can be obtained, so as to determine the risk assessment coefficients according to the feature values:

[0047] Firstly, the image information of the target object can be obtained, and the feature value corresponding to the current posture of the target object is determined through the posture risk identification model, wherein in the present embodiment, the image information can be obtained by obtaining the image information of the high-altitude operation personnel, after the image information is obtained, the image information can be input into the pre-trained posture risk identification model for identification, so as to output the corresponding posture risk index (i.e. the posture feature value), wherein the posture risk index can be the dangerous degree of the posture, for example, the posture risk index can be 50%.

[0048] It should be noted that the algorithm in the posture risk identification model can be a YOLOv5 algorithm model (YOLOv5 is a real-time target detection algorithm, which is one of the YOLO (You Only Look Once) series algorithms), so as to identify the human body image information through the YOLOv5 algorithm model, classify the posture of the target object at the current moment, obtain the corresponding posture risk index according to the specific category after classification, and determine the posture risk index as the posture feature value.

[0049] Further, it is also necessary to determine whether the wearable device of the target object is located at a preset position according to the image information, so as to determine whether the wearable device is normally worn. The wearable device in the embodiment mainly includes safety helmets, safety clothes, safety gloves, safety belts and other safety wearable devices. By obtaining the work image of the high-altitude worker, it is determined whether the high-altitude worker correctly and completely wears the auxiliary safety wearable devices including safety helmets, safety clothes, safety gloves and safety belts. If it is found that any safety auxiliary wearable device is not worn according to the regulation, the position feature value is determined as 100. In the case that all wearable devices are normally worn, the position feature value is determined as 0, so as to determine the position feature value according to the wearable device.

[0050] It should be noted that after obtaining the position feature value, the threshold value corresponding to the position feature value can be set as 1, that is, in the case that all wearable devices are normal, the risk coefficient is 0, that is, no risk, and in the case that there is an abnormality in the wearable device, the risk coefficient is 100, that is, high risk, so as to determine the risk prompt according to the state of the wearable device.

[0051] Further, in the wearable device of the target object, a sensor can also be arranged. The sensor can collect tension, pressure, acceleration, etc., that is, the force acting on the target object is detected and collected, so as to obtain the force feature value, and further analyze whether the target object has a safety risk from the perspective of the force acting on the target object.

[0052] It should be noted that the sensor can be arranged in various safety auxiliary wearable devices, for example, a tension sensor is arranged at each connecting buckle in the safety belt, so as to detect the tension of each part of the safety belt, and determine whether the high-altitude worker is in a dangerous state through the change of the tension, for example, the safety belt suddenly tightens, which may be that the worker steps on nothing, so it is necessary to alarm in time.

[0053] Similarly, the triaxial sensor can also be arranged in the wearable device to obtain the instantaneous triaxial acceleration of the high-altitude worker, and output the corresponding acceleration risk index by judging the specific condition of the instantaneous acceleration. Generally, the acceleration of the high-altitude worker is relatively small when the high-altitude worker works in the high-altitude working scene. When the acceleration suddenly increases, it may be due to the sudden occurrence of the worker stepping on the empty or even falling and other accidents. Therefore, the real-time acceleration of the target object needs to be obtained, and the acceleration value is taken as one of the characteristic values in the force characteristic value, so as to determine the safety risk of the target object according to the acceleration value.

[0054] It should be noted that, since the acceleration and the acceleration risk index are in a positive proportional relationship, that is, during the high-altitude work, the worker should be cautious to move to avoid increasing the risk of accidents due to rapid movement. When the acceleration suddenly increases to an acceleration value that cannot be reached by normal movement of the worker, the risk level is directly determined as high risk regardless of the acceleration value at this time. That is, below the upper limit value of the acceleration, the risk assessment coefficient is determined according to the acceleration value and the corresponding threshold value. In the case of exceeding the upper limit value of the acceleration, the risk assessment coefficient does not need to be obtained, and the risk level is directly determined as high risk, so as to ensure that the security personnel can respond in time. Optionally, the triaxial sensor used to measure the acceleration can be arranged on the waist of the high-altitude worker to avoid the acceleration change noise caused by lifting the hands and feet from misjudging.

[0055] Optionally, the environmental data of the target position where the target object is located also needs to be obtained, and the environmental data is determined as an environmental characteristic value, so as to judge whether the target object is in a dangerous working environment according to the environmental characteristic value, and further timely display the corresponding risk level to inform the target object to terminate the work in time and ensure the safety of the worker when it is detected that the target object is in a dangerous working environment.

[0056] For example, in this embodiment, the wind speed monitoring device can be arranged on the front chest, back and left and right shoulders of the high-altitude worker to monitor the wind speed during high-altitude operation. When the wind speed during high-altitude operation is too large, the wind force exerted on the high-altitude worker is larger, the energy consumed by the high-altitude worker is more, and the risk of accident is higher. Similarly, the temperature monitoring device can be arranged to monitor the body temperature of the high-altitude worker and the ambient temperature. The temperature monitoring device takes the temperature difference between the current body temperature and the normal body temperature as the first temperature characteristic value, takes the temperature difference between the current ambient temperature and the normal ambient temperature as the second temperature characteristic value, and determines the risk assessment coefficient of the temperature dimension in the environmental dimension by the first temperature characteristic value and the second temperature characteristic value. The risk assessment coefficient is obtained by weighting. By detecting the temperature, the physical condition of the high-altitude worker during high-altitude operation can be determined to avoid the situation that the high-altitude worker accidentally falls due to hypothermia or heat stroke.

[0057] In this embodiment, the feature information and the characteristic value in different dimensions are collected, so that the risk assessment coefficient in different dimensions can be determined according to the characteristic value, and the risk level can be determined according to the risk assessment coefficient, thereby ensuring the accuracy of the risk level evaluation.

[0058] Optionally, in the high-altitude operation risk early warning method provided in the embodiment of the application, in the case that the M characteristic values include the posture characteristic value, the position characteristic value, the force characteristic value and the environmental characteristic value, the current motion state of the target object is identified, and the threshold value corresponding to each kind of feature information is determined according to the current motion state, to obtain M threshold values, including: in the case that the current motion state of the target object is a static state, the threshold value corresponding to the posture characteristic value is determined as a first threshold value, the threshold value corresponding to the position characteristic value is determined as a second threshold value, the threshold value corresponding to the force characteristic value is determined as a third threshold value, and the threshold value corresponding to the environmental characteristic value is determined as a fourth threshold value; in the case that the current motion state of the target object is a moving state, the threshold value corresponding to the posture characteristic value is determined as the first threshold value, the threshold value corresponding to the position characteristic value is determined as the second threshold value, the threshold value corresponding to the force characteristic value is determined as a fifth threshold value, and the threshold value corresponding to the environmental characteristic value is determined as the fourth threshold value, wherein the fifth threshold value is greater than the third threshold value.

[0059] Specifically, since the threshold values corresponding to different characteristic values are different in different motion states of the target object, the threshold values in the static state and the threshold values in the moving state need to be set respectively when setting the threshold values, so as to avoid the phenomenon that the risk level is wrongly predicted and the alarm information is wrongly reported due to the too high characteristic values in the moving state.

[0060] It should be noted that the posture feature value of the target object, the position feature value of the wearable device and the environment feature value will not change greatly with the motion state of the target object, so the threshold value can be kept unchanged, that is, the threshold value is consistent in the static state and the moving state.

[0061] Further, when the motion state of the target object changes, the change amount of the force feature value is large, therefore, different thresholds need to be set for the force feature value in different motion states, so as to reduce the influence of the force generated by the target object in the moving state on the judgment of the force feature value, thereby avoiding the phenomenon that the force feature value exceeds the threshold value in the case that the target object is moving, and affecting the accuracy of the risk level assessment.

[0062] The present application sets different thresholds according to the motion state of the target object, thereby reducing the influence of the motion state on the judgment of whether the feature value is abnormal, and improving the accuracy of the risk level assessment.

[0063] Optionally, in the high-altitude operation risk early warning method provided in the embodiment of the present application, the method of judging whether the wearable device of the target object is located at the preset position according to the image information and obtaining the position feature value according to the judgment result comprises: in the case that the wearable device of the target object is located at the preset position, determining the judgment result as no risk, and determining the position feature value as a first target value, wherein the first target value is less than a second threshold value; in the case that the wearable device of the target object is not located at the preset position, determining the judgment result as risk, and determining the position feature value as a second target value, wherein the second target value is greater than the second threshold value.

[0064] Specifically, since whether the wearable device is normally worn cannot be collected corresponding numerical value, when the position feature value is obtained, the feature value needs to be set as a preset fixed value according to the wearing condition of the wearable device, and the threshold value is set as a fixed value, so as to determine the state of the wearable device as normal wearing and abnormal wearing, wherein the risk assessment coefficient is 0 in the case of normal wearing, and the risk assessment coefficient is the maximum value in the case of abnormal wearing, therefore, the position feature value can be set as 0 in the case of normal wearing, and the position feature value can be set as 100 in the case of abnormal wearing, and the threshold value can be set as 1, so that the risk assessment coefficient is 0 / 1=0 in the case of normal wearing, and the risk assessment coefficient is 100 / 1=100 in the case of abnormal wearing, so that in the case of abnormal wearing, no matter what the risk assessment coefficient of other feature values is, the risk level is high risk, so as to ensure that the target object can be timely informed to adjust the wearable device in the case of abnormal wearing, and improve the safety of high-altitude operation.

[0065] It should be noted that the first target value represents no risk, and the second target value represents high risk, so the first target value should be much smaller than the second target value.

[0066] Optionally, in the high-altitude operation risk early warning method provided in the embodiments of the present application, the calculation of the feature value of each feature information and the corresponding threshold value to obtain M risk assessment coefficients includes: dividing each feature value by the corresponding threshold value to obtain the risk assessment coefficient of each feature value.

[0067] Specifically, when calculating the risk assessment coefficient, the feature value can be obtained, and the feature value is divided by the corresponding threshold value to determine the risk assessment coefficient.

[0068] It should be noted that in the case where the feature value includes multiple sub-feature values, the risk assessment coefficient of each sub-feature value can be obtained, and the risk assessment coefficients of the multiple sub-feature values are weighted and summed to obtain the risk assessment coefficient of the feature value.

[0069] For example, in the case where the temperature risk assessment coefficient in the environmental feature value is 50%, and the wind power risk assessment coefficient is 40%, in the case where the weights of temperature and wind power are 1:1, the risk assessment coefficient of the environmental feature value is 45%, thereby accurately synthesizing the risk assessment coefficients of the feature values under multiple feature information, and improving the accuracy of the risk assessment coefficients under each feature information.

[0070] Optionally, in the high-altitude operation risk early warning method provided in the embodiments of the present application, determining the risk level of the target object at the target position according to the M risk assessment coefficients includes: obtaining a risk assessment standard, wherein the risk assessment standard includes multiple standard values and feature information corresponding to each standard value, and the standard value is used to determine the risk level of the feature information; determining whether there is target feature information whose risk assessment coefficient is greater than the corresponding standard value; in the case where there is target feature information, obtaining the number of target feature information, and determining whether the number of target feature information is greater than a preset number; in the case where the number of target feature information is greater than the preset number, determining that the risk level is a first risk level; in the case where the number of target feature information is less than or equal to the preset number, determining that the risk level is a second risk level, wherein the second risk level is less than the first risk level; in the case where there is no target feature information, determining that the risk level is a third risk level, wherein the third risk level is less than the second risk level.

[0071] Specifically, when determining the risk level, three risk levels, i.e., low risk, medium risk and high risk, can be determined. When determining the risk level, the risk assessment coefficient also needs to be compared with the standard value. In the case that the risk assessment coefficient is greater than the standard value, it is determined that the feature information has risks. In the case that the risk assessment coefficient is less than or equal to the standard value, it is determined that the feature information has no risks. Therefore, the number of target feature information having risks in the plurality of feature information can be determined according to the size relationship between each risk assessment coefficient and the corresponding standard value, and the safety risk of the target object in the current state can be determined according to the number of target feature information.

[0072] Further, in the case that the number of target feature information is 0, it is indicated that there is no target feature information. At this time, it can be determined that the risk level is low risk, i.e., the third risk level. In the case that the number of target feature information is less than or equal to the preset number, it is indicated that there is a certain feature information having risks, but the number is small. At this time, it can be determined that the risk level is medium risk, i.e., the second risk level. In the case that the number of target feature information is greater than the preset number, it is indicated that there are currently a plurality of feature information having risks. At this time, it can be determined that the risk level is high risk, i.e., the first risk level. Thus, the technical effect of accurately determining the risk level of the target object is achieved.

[0073] It should be noted that, in the case that the position feature value has risks, the risk level is directly determined as high risk, and it is not necessary to determine whether other feature information has risks.

[0074] It should be noted that, in order to enable the target object to clearly determine the current risk level, different risk levels can be corresponded to different colors. For example, in the case that the risk level is low risk, a green light is displayed on the alarm device worn by the target object. In the case that the risk level is medium risk, a yellow light is displayed on the alarm device worn by the target object. In the case that the risk level is high risk, a red light is displayed on the alarm device worn by the target object. Thus, the technical effect of obviously and accurately conveying the risk level to the target object is achieved.

[0075] Optionally, in the high-altitude operation risk early warning method provided in the embodiments of the present application, in the case that there is no target feature information, the method further comprises: identifying the current motion state of the target object, and obtaining the weight value of each kind of feature information according to the current motion state to obtain M weight values; performing weighted summation on the M risk assessment coefficients according to the M weight values to obtain a target risk coefficient; obtaining a risk coefficient threshold value in the current motion state, and determining whether the target risk coefficient is greater than the risk coefficient threshold value; in the case that the target risk coefficient is greater than the risk coefficient threshold value, determining that the risk level is the second risk level; in the case that the target risk coefficient is less than or equal to the risk coefficient threshold value, determining that the risk level is the third risk level.

[0076] It should be noted that in some cases, the risk assessment coefficient of each feature information may not exceed the corresponding feature value, but the risk assessment coefficient of each feature information is close to the feature value, at this time, the risk level obtained through the above judgment process is low risk, but it may be a case of medium risk or high risk, therefore, in the case of low risk, the risk level needs to be further confirmed, so as to ensure the accuracy of the prediction of the risk level.

[0077] Specifically, in the case of low risk, the weight of each feature information can be obtained, M weights are obtained, and M risk assessment coefficients are weighted and summed according to the M weights to obtain a target risk coefficient, the target risk coefficient at this time is a comprehensive risk coefficient, and the target risk coefficient and the risk coefficient threshold are compared, in the case that the target risk coefficient is greater than the risk coefficient threshold, it is determined that the current risk level is medium risk, in the case that the target risk coefficient is less than or equal to the risk coefficient threshold, it is determined that the current risk level is low risk, thereby ensuring the accuracy of the risk level prediction.

[0078] It should be noted that since the weight of the feature information changes when the target object is in different motion states, when obtaining the weight of the feature information, the current motion state needs to be determined, and the weight and the risk coefficient threshold in the current motion state are obtained, thereby ensuring the accuracy of the risk level prediction in different motion states.

[0079] Embodiment 2

[0080] Figure 2 is a schematic diagram of a risk early warning system for high-altitude operation provided by the embodiments of the present application, as Figure 2 shown, the foregoing risk early warning method for high-altitude operation is executed by an optional risk early warning system for high-altitude operation as an execution subject, and the risk early warning system for high-altitude operation at least includes: an analysis processing module 21, a posture monitoring module 22, a safety auxiliary equipment monitoring module 23, an acceleration monitoring module 24, a wind speed monitoring module 25, a temperature monitoring module 26, a database storage module 27, and a warning module 28.

[0081] The posture monitoring module 22 is configured to identify posture information of the high-altitude operation personnel, give a posture risk index of the high-altitude operation personnel in combination with the posture information, and transmit the obtained posture information to the database storage module 27 for storage and transmit the posture risk index to the analysis processing module 21 for analysis and processing.

[0082] The safety auxiliary equipment monitoring module 23 is configured to acquire state information of safety auxiliary equipment including safety helmets, safety ropes, etc., give a safety auxiliary equipment risk index in combination with the state information of each safety auxiliary equipment, transmit the state information of each safety auxiliary equipment to the database storage module 27 in a time sequence for storage, and transmit the safety auxiliary equipment risk index to the analysis processing module 21 for analysis and processing.

[0083] The acceleration monitoring module 24 is configured to monitor the acceleration of the high-altitude worker when moving, give an acceleration risk index in combination with the acceleration when moving, transmit the acceleration information when moving to the database storage module 27 in a time sequence for storage, and transmit the acceleration risk index to the analysis processing module 21 for analysis and processing.

[0084] The wind speed monitoring module 25 is configured to monitor the wind force borne by the high-altitude worker, give a wind speed risk index in combination with the wind force, transmit the wind force borne by the high-altitude worker to the database storage module 27 in a time sequence for storage, and transmit the risk index to the analysis processing module 21 for analysis and processing.

[0085] The temperature monitoring module 26 is configured to monitor the body temperature of the high-altitude worker and the environmental temperature in real time, give a temperature risk index according to the body temperature information and the environmental temperature information, and transmit the body temperature and the environmental temperature information to the database storage module 27 in a time sequence for storage, and transmit the temperature risk index to the analysis processing module 21 for analysis and processing.

[0086] The analysis processing module 21 is configured to receive the risk indexes of each monitoring module, and after receiving the risk indexes of each monitoring module, first perform a threshold judgment corresponding to each risk index, when it is judged that there is a risk, directly issue a warning instruction to the warning module 28, wherein the warning instruction contains the alarm information corresponding to the risk, when the threshold judgment is no risk, the analysis processing module 21 further performs a normalization processing on each risk index to obtain a total risk index, and then performs a threshold judgment on the total risk index, if the threshold judgment of the total risk index still meets the regulation, it means that the high-altitude worker has no falling wind direction, if the total risk index exceeds the threshold, it still immediately issues a warning instruction to the warning module 28.

[0087] The normalization method can be Min-Max Scaling, which is used to scale all risk indexes to the interval [0, 1], and assign a weight to each risk index, and then perform a weighted summation to obtain a total risk index, wherein the assignment of the weight should be based on the importance of each index to the total risk, so as to ensure the accuracy of the total risk index.

[0088] Optionally, the posture monitoring module 22 outputs the corresponding posture risk index by acquiring image information of the high-altitude worker and inputting the image information into a pre-trained posture recognition model for recognition. Specifically, the posture monitoring module 22 obtains human body image information during high-altitude work through an image acquisition device, and identifies the human body image information through a YOLOv5 algorithm model to classify the posture at the current time, and obtains the corresponding posture risk index according to the specific category after classification.

[0089] Optionally, the safety auxiliary equipment monitoring module 23 mainly monitors safety helmets, safety clothes, safety gloves, and safety belts. The safety auxiliary equipment monitoring module 23 also acquires work images of the high-altitude worker, and determines from the images whether the high-altitude worker correctly and completely wears the safety auxiliary equipment including safety helmets, safety clothes, safety gloves, and safety belts. If any safety auxiliary equipment is found not to be worn according to the regulations, the highest safety auxiliary equipment risk index is immediately given, and transmitted to the analysis processing module 21, which immediately issues a warning to the warning module 28. In the case where there is no problem with the wearing of various safety auxiliary equipment, the tension sensors arranged at each connection buckle of the safety belt detect the tension of each part of the safety belt. The change in tension can to some extent determine whether the high-altitude worker is in a dangerous state. For example, if the safety belt suddenly tightens, it may be that the worker has stepped on nothing, and therefore needs to be warned in time.

[0090] Optionally, the acceleration monitoring module 24 mainly acquires the instantaneous three-axis acceleration of the high-altitude worker through a three-axis sensor, and outputs the corresponding acceleration risk index by judging the specific situation of the instantaneous acceleration. Generally, the acceleration of the high-altitude worker is relatively small when moving in a high-altitude work scene. When the acceleration suddenly increases, it may be due to the sudden stepping on nothing or even falling of the worker, and therefore the corresponding acceleration risk index needs to be immediately sent to the analysis processing module 21, which immediately sends a warning instruction to the warning module 28, so that the safety supervisor can learn about the situation in time. Optionally, the acceleration and the acceleration risk index are in a positive proportional relationship, that is, the worker should move cautiously during high-altitude work to avoid increasing the risk of accidents due to rapid movement. When the acceleration suddenly increases to a threshold value that cannot be reached by normal movement of the worker, the acceleration risk index also suddenly changes to the highest value, that is, below the threshold value, the acceleration risk index is in a positive proportional relationship with the acceleration, and above the threshold value, the acceleration risk index immediately changes to the maximum value. Optionally, the three-axis sensor of the acceleration monitoring module 24 is arranged at the waist of the high-altitude worker to avoid misjudgment due to acceleration changes caused by lifting hands and feet.

[0091] Optionally, the wind speed monitoring module 25 mainly monitors the wind force during aerial work through pressure sensors arranged on the front chest, back and left and right shoulders of the aerial work personnel, so as to monitor the wind force in each direction. When the wind speed during aerial work is too large, the wind force exerted on the aerial work personnel is larger, the aerial work personnel consumes more energy, and the risk of accidents is higher. Therefore, the wind speed monitoring module 25 dynamically adjusts the wind speed risk index according to the pressure change on the pressure sensor.

[0092] Optionally, the temperature monitoring module 26 mainly monitors the body temperature of the aerial work personnel and the ambient temperature through two temperature sensors. The temperature monitoring module 26 takes the temperature difference between the current body temperature and the normal body temperature as the main temperature risk index judgment factor, and takes the temperature difference between the current ambient temperature and the normal ambient temperature as the secondary temperature risk index judgment factor, and performs weighted processing to obtain the temperature risk index of the two. Through the index, the body condition of the aerial work personnel during aerial work can be judged, and the situation of the aerial work personnel falling accidentally due to hypothermia and heatstroke can be avoided.

[0093] Optionally, the communication between the analysis processing module 21 and each monitoring module can be wired or wireless communication, and the wireless communication is preferred. The analysis processing module 21 can be set as a back-end server, and the back-end server communicates with each monitoring module on site through a plurality of base stations arranged. At the same time, the database storage module 27 is also set as a data server, which is scheduled by the back-end server, i.e. the analysis processing module 21, so as to store data. The large amount of data stored in the database storage module 27 can be used for reverse optimization training of the risk index output process of each monitoring module in the later stage, so as to continuously improve the monitoring accuracy of each monitoring module and reduce the probability of false alarm.

[0094] Optionally, the early warning module 28 in the embodiment is designed as at least various intelligent devices, which can be worn on the work personnel, and at least has a display interface and a warning unit. The warning unit can include warning modes such as light, sound and vibration. The display interface is a comprehensive query interface of alarm information, which can view all the detailed information of the alarm stored in the database storage module 27, query the historical alarm information, and display information including the location of the alarm information, the name of the camera, the alarm category, the alarm time and the video viewing at the time of alarm. The information can also be filtered according to the concerned points, such as filtering specific alarm information for viewing by alarm time and alarm category keywords. The interface also supports video playback function corresponding to the alarm information. In addition, the specified alarm information can be checked and exported.

[0095] The high-altitude operation risk early warning system in the application monitors the safety situation of the high-altitude operation personnel from the human posture, the tension of the safety auxiliary equipment especially the safety belt of the operation personnel, the high-altitude acceleration change situation of the operation personnel, the high-altitude wind force situation, the body temperature and the environmental temperature situation of the high-altitude operation personnel, and the like, so as to comprehensively monitor the safety situation of the high-altitude operation personnel, facilitate the safety supervisor to issue relevant instructions or carry out rescue actions in the first time, reduce the risk of accidents and the damage caused by accidents, and thus the effect of improving the accuracy and timeliness of operation risk early warning is achieved.

[0096] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0097] Embodiment 3

[0098] The embodiment of the application also provides a high-altitude operation risk early warning device. It should be noted that the high-altitude operation risk early warning device of the embodiment of the application can be used to execute the high-altitude operation risk early warning method provided by the embodiment of the application. The high-altitude operation risk early warning device provided by the embodiment of the application is introduced as follows.

[0099] Figure 3 is a schematic diagram of the high-altitude operation risk early warning device provided by the embodiment of the application. As shown in Figure 3 , the device comprises: a first acquisition unit 31, a first determination unit 32, a first calculation unit 33, and a second determination unit 34.

[0100] The first acquisition unit 31 is configured to monitor M kinds of feature information of a target object at a target position, and acquire a feature value of each kind of feature information, thereby obtaining M feature values, wherein M is a positive integer.

[0101] The first determination unit 32 is configured to identify a current motion state of the target object, and determine a threshold value corresponding to each kind of feature information according to the current motion state, thereby obtaining M threshold values.

[0102] The first calculation unit 33 is configured to calculate the feature value of each kind of feature information and the corresponding threshold value, thereby obtaining M risk assessment coefficients.

[0103] The second determination unit 34 is configured to determine a risk level of the target object at the target position according to the M risk assessment coefficients, and send the risk level to the target object.

[0104] The high-altitude operation risk early warning device provided by the embodiment of the application monitors M kinds of feature information of the target object at the target position through the first acquisition unit 31, and obtains a feature value of each kind of feature information to obtain M feature values, wherein M is a positive integer; the first determination unit 32 identifies the current motion state of the target object, and determines a threshold value corresponding to each kind of feature information according to the current motion state to obtain M threshold values; the first calculation unit 33 calculates the feature value of each kind of feature information and the corresponding threshold value to obtain M risk assessment coefficients; and the second determination unit 34 determines the risk level of the target object at the target position according to the M risk assessment coefficients, and sends the risk level to the target object. The problem that the accuracy of identifying operation risks by monitoring the operation process of high-altitude operation personnel in the related art is low is solved. The feature information of the target object in multiple dimensions is monitored to comprehensively monitor the safety of the high-altitude operation personnel. When a high risk level is monitored, the falling situation that is about to occur or has occurred is early warned or alarmed in a timely manner according to the risk level, so that the safety supervisor can issue relevant instructions or carry out rescue actions in the first time, reduce the risk of accidents and the damage caused by accidents, and thus the effect of improving the accuracy and timeliness of operation risk early warning is achieved.

[0105] Optionally, in the high-altitude operation risk early warning device provided by the embodiment of the application, the first acquisition unit 31 comprises: a first acquisition module, configured to acquire image information of the target object, and input the image information into a posture risk identification model to obtain a posture feature value, wherein the posture risk identification model is obtained by training a plurality of sample data, and each sample data is composed of a sample posture image and a corresponding risk score; a first judgment module, configured to judge whether the wearable device of the target object is located at a preset position according to the image information, and obtain a position feature value according to the judgment result; a second acquisition module, configured to acquire a tension value and an acceleration value collected by a sensor to obtain an acting force feature value, wherein the sensor is arranged in the wearable device of the target object; and a third acquisition module, configured to acquire environmental data of the target position where the target object is located, and determine the environmental data as an environmental feature value.

[0106] Optionally, in the high-altitude operation risk early warning device provided by the embodiment of the application, in the case that the M characteristic values include the posture characteristic value, the position characteristic value, the force characteristic value and the environment characteristic value, the first determination unit 32 comprises: a first determination module, configured to, in the case that the current motion state of the target object is a static state, determine the threshold value corresponding to the posture characteristic value as a first threshold value, determine the threshold value corresponding to the position characteristic value as a second threshold value, determine the threshold value corresponding to the force characteristic value as a third threshold value, and determine the threshold value corresponding to the environment characteristic value as a fourth threshold value; and a second determination module, configured to, in the case that the current motion state of the target object is a moving state, determine the threshold value corresponding to the posture characteristic value as the first threshold value, determine the threshold value corresponding to the position characteristic value as the second threshold value, determine the threshold value corresponding to the force characteristic value as a fifth threshold value, and determine the threshold value corresponding to the environment characteristic value as the fourth threshold value, wherein the fifth threshold value is greater than the third threshold value.

[0107] Optionally, in the high-altitude operation risk early warning device provided by the embodiment of the application, the first determination unit 32 comprises: a first determination sub-module, configured to, in the case that the wearable device of the target object is located at the preset position, determine the judgment result as no risk, and determine the position characteristic value as a first target value, wherein the first target value is less than the second threshold value; and a second determination sub-module, configured to, in the case that the wearable device of the target object is not located at the preset position, determine the judgment result as a risk, and determine the position characteristic value as a second target value, wherein the second target value is greater than the second threshold value.

[0108] Optionally, in the high-altitude operation risk early warning device provided by the embodiment of the application, the first calculation unit 33 comprises: a calculation module, configured to divide each characteristic value by the corresponding threshold value to obtain a risk evaluation coefficient of each characteristic value.

[0109] Optionally, in the high-altitude operation risk early warning device provided by the embodiment of the application, the second determination unit 34 comprises: a fourth acquisition module, configured to acquire risk assessment criteria, wherein the risk assessment criteria comprise a plurality of standard values and feature information corresponding to each standard value, and the standard value is used to determine the risk level of the feature information; a second judgment module, configured to determine whether there is target feature information with a risk assessment coefficient greater than the corresponding standard value; a third judgment module, configured to acquire the number of target feature information in the case of target feature information, and determine whether the number of target feature information is greater than a preset number; a third determination module, configured to determine that the risk level is a first risk level in the case that the number of target feature information is greater than the preset number; a fourth determination module, configured to determine that the risk level is a second risk level in the case that the number of target feature information is less than or equal to the preset number, wherein the second risk level is less than the first risk level; and a fifth determination module, configured to determine that the risk level is a third risk level in the case that there is no target feature information, wherein the third risk level is less than the second risk level.

[0110] Optionally, in the high-altitude operation risk early warning device provided by the embodiment of the application, in the case that there is no target feature information, the device further comprises: an identification unit, configured to identify the current motion state of the target object, and acquire the weight of each kind of feature information according to the current motion state to obtain M weights; a second calculation unit, configured to perform weighted summation on the M risk assessment coefficients according to the M weights to obtain a target risk coefficient; a second acquisition unit, configured to acquire a risk coefficient threshold under the current motion state, and determine whether the target risk coefficient is greater than the risk coefficient threshold; a third determination unit, configured to determine that the risk level is the second risk level in the case that the target risk coefficient is greater than the risk coefficient threshold; and a fourth determination unit, configured to determine that the risk level is the third risk level in the case that the target risk coefficient is less than or equal to the risk coefficient threshold.

[0111] The high-altitude operation risk early warning device comprises a processor and a memory, the first acquisition unit 31, the first determination unit 32, the first calculation unit 33, the second determination unit 34, and the like are stored in the memory as program units, and the corresponding functions are realized by the processor executing the program units stored in the memory.

[0112] The processor comprises a core, and the core calls the corresponding program units from the memory. The core can be set to one or more, and the accuracy of identifying operation risks by monitoring the operation process of high-altitude operation personnel in the related art is improved by adjusting the core parameters.

[0113] The memory can include non-persistent memory in a computer readable medium, random access memory (RAM), and / or non-volatile memory such as read only memory (ROM) or flash memory, and the memory includes at least one memory chip.

[0114] The embodiment of the present application provides a computer readable storage medium, which stores a program, and the program is executed by a processor to realize the risk early warning method for high-altitude operation.

[0115] The embodiment of the present application provides a processor, which is used for running a program, and the program is executed to realize the risk early warning method for high-altitude operation.

[0116] Figure 4 is a schematic diagram of an electronic device provided by the embodiment of the present application, as shown in the figure, the embodiment of the present application provides an electronic device, and the electronic device 40 includes a processor, a memory, and a program stored in the memory and capable of running on the processor, and the processor realizes the steps of the risk early warning method for high-altitude operation when executing the program. The device in the present embodiment can be a server, a PC, a PAD, a mobile phone, etc. Figure 4

[0117] The present application further provides a computer program product, which is suitable for executing the program of the steps of the risk early warning method for high-altitude operation when being executed on a data processing device.

[0118] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a computer program product in the form of being implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0119] The present application is described with reference to the flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks can be realized by computer program instructions. These computer program instructions can be provided to a general purpose computer, a special purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one flow or multiple flows and / or blocks Figure 1 The functions specified in one flow or multiple flows and / or blocks ​

[0120] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0122] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0123] The memory can include non-persistent memory and / or volatile memory, such as a random access memory (RAM) including a cache area for the temporary storage of data. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, or a combination of non-volatile memories in different forms. The memory is an example of computer readable storage media.

[0124] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0125] It should also be noted that the terms "comprising", "comprises" or other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0126] The above embodiments are only used to illustrate the present application, but not to limit it. Instead of the above, various modifications and changes can be made to the application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall fall into the scope of the claims of the application.

Claims

1. A risk early warning method for aerial work, characterized in that, The method comprises the following steps: monitoring M kinds of characteristic information of a target object at a target position, and obtaining a characteristic value of each kind of characteristic information, to obtain M characteristic values, wherein M is a positive integer; identifying a current motion state of the target object, and determining a threshold value corresponding to each kind of characteristic information according to the current motion state, to obtain M threshold values; calculating the characteristic value of each kind of characteristic information and the corresponding threshold value, to obtain M risk assessment coefficients; determining a risk level of the target object at the target position according to the M risk assessment coefficients, and sending the risk level to the target object; monitoring M kinds of characteristic information of a target object at a target position, and obtaining a characteristic value of each kind of characteristic information, to obtain M characteristic values, wherein the M characteristic values at least include two of the following: obtaining image information of the target object, and inputting the image information into a posture risk identification model to obtain a posture characteristic value, wherein the posture risk identification model is trained by a plurality of sample data, and each sample data is composed of a sample posture image and a corresponding risk score; judging whether a wearable device of the target object is located at a preset position according to the image information, and obtaining a position characteristic value according to the judgment result; obtaining a tension value and an acceleration value collected by a sensor, to obtain an acting force characteristic value, wherein the sensor is arranged in the wearable device of the target object; obtaining environmental data of the target position where the target object is located, and determining the environmental data as an environmental characteristic value; determining a risk level of the target object at the target position according to the M risk assessment coefficients comprises: obtaining a risk assessment standard, wherein the risk assessment standard includes a plurality of standard values and characteristic information corresponding to each standard value, and the standard value is used to determine the risk level of the characteristic information; judging whether there is target characteristic information with a risk assessment coefficient greater than the corresponding standard value; in the case that there is the target characteristic information, obtaining the number of the target characteristic information, and judging whether the number of the target characteristic information is greater than a preset number; in the case that the number of the target characteristic information is greater than the preset number, determining that the risk level is a first risk level; in the case that the number of the target characteristic information is less than or equal to the preset number, determining that the risk level is a second risk level, wherein the second risk level is less than the first risk level; in the case that there is no target characteristic information, determining that the risk level is a third risk level, wherein the third risk level is less than the second risk level.

2. The method of claim 1, wherein, In the case that the M characteristic values include the posture characteristic value, the position characteristic value, the acting force characteristic value and the environmental characteristic value, identifying a current motion state of the target object, and determining a threshold value corresponding to each kind of characteristic information according to the current motion state, to obtain M threshold values comprises: In a case where the current motion state of the target object is a static state, a threshold value corresponding to the posture feature value is determined as a first threshold value, a threshold value corresponding to the position feature value is determined as a second threshold value, a threshold value corresponding to the force feature value is determined as a third threshold value, and a threshold value corresponding to the environment feature value is determined as a fourth threshold value. In a case where the current motion state of the target object is a moving state, the threshold value corresponding to the posture feature value is determined as the first threshold value, the threshold value corresponding to the position feature value is determined as the second threshold value, a threshold value corresponding to the force feature value is determined as a fifth threshold value, and the threshold value corresponding to the environment feature value is determined as the fourth threshold value, where the fifth threshold value is greater than the third threshold value.

3. The method of claim 2, wherein, According to the image information, it is determined whether the wearable device of the target object is located at a preset position, and a position feature value is obtained according to a determination result, comprising: In a case where the wearable device of the target object is located at the preset position, the determination result is determined as no risk, and the position feature value is determined as a first target value, where the first target value is less than the second threshold value; In a case where the wearable device of the target object is not located at the preset position, the determination result is determined as a risk, and the position feature value is determined as a second target value, where the second target value is greater than the second threshold value.

4. The method of claim 1, wherein, The feature value of each feature information is calculated with the corresponding threshold value to obtain M risk assessment coefficients, comprising: Each feature value is divided by the corresponding threshold value to obtain the risk assessment coefficient of each feature value.

5. The method of claim 1, wherein, In the absence of the target feature information, the method further comprises: identifying the current motion state of the target object, and obtaining the weight of each feature information according to the current motion state to obtain M weights; weighting and summing the M risk assessment coefficients according to the M weights to obtain a target risk coefficient; obtaining a risk coefficient threshold value in the current motion state, and determining whether the target risk coefficient is greater than the risk coefficient threshold value; in a case where the target risk coefficient is greater than the risk coefficient threshold value, the risk level is determined as the second risk level; in a case where the target risk coefficient is less than or equal to the risk coefficient threshold value, the risk level is determined as the third risk level.

6. A risk warning device for aerial work, characterized in that, comprising: a first acquisition unit for monitoring M kinds of feature information of a target object at a target position, and acquiring a feature value of each feature information to obtain M feature values, where M is a positive integer; a first determination unit for identifying the current motion state of the target object, and determining the threshold value corresponding to each feature information according to the current motion state to obtain M threshold values; a first calculation unit for calculating the feature value of each feature information with the corresponding threshold value to obtain M risk assessment coefficients; a second determination unit for determining the risk level of the target object at the target position according to the M risk assessment coefficients, and sending the risk level to the target object; The first obtaining unit comprises: a first obtaining module, configured to obtain image information of a target object, and input the image information into a posture risk identification model to obtain a posture feature value, wherein the posture risk identification model is trained by a plurality of sample data, and each sample data is composed of a sample posture image and a corresponding risk score; a first judging module, configured to determine whether a wearable device of the target object is located at a preset position according to the image information, and obtain a position feature value according to a result of the determination; a second obtaining module, configured to obtain a pulling force value and an acceleration value collected by a sensor, and obtain an acting force feature value, wherein the sensor is arranged in the wearable device of the target object; and a third obtaining module, configured to obtain environmental data of a target position where the target object is located, and determine the environmental data as an environmental feature value. The second determining unit comprises: a fourth obtaining module, configured to obtain a risk assessment standard, wherein the risk assessment standard comprises a plurality of standard values and corresponding feature information of each standard value, and the standard value is used to determine a risk level of the feature information; a second judging module, configured to determine whether there is target feature information with a risk assessment coefficient greater than a corresponding standard value; a third judging module, configured to obtain a quantity of the target feature information in the case that there is the target feature information, and determine whether the quantity of the target feature information is greater than a preset quantity; a third determining module, configured to determine that the risk level is a first risk level in the case that the quantity of the target feature information is greater than the preset quantity; a fourth determining module, configured to determine that the risk level is a second risk level in the case that the quantity of the target feature information is less than or equal to the preset quantity, wherein the second risk level is less than the first risk level; and a fifth determining module, configured to determine that the risk level is a third risk level in the case that there is no target feature information, wherein the third risk level is less than the second risk level.

7. A computer program product comprising a computer program, characterized in that, The computer program is executed by a processor to implement the risk early warning method for high-altitude operation in any one of claims 1 to 5.

8. An electronic device, comprising: The computer program is executed by a processor to implement the risk early warning method for high-altitude operation in any one of claims 1 to 5.

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