Lifting operation hazard source identification method and system based on movable device

Abstract

The invention provides a lifting operation hazard source identification method and system based on a movable device, and the method comprises the steps: determining the motion state of a lifting device in a first space coordinate system based on the lifting motion feature data of the lifting device, thereby determining the interference region distribution features of the lifting device in a working process; based on the relation between a second space coordinate system and a first space coordinate system corresponding to visual acquisition of the movable device, the spatial distribution characteristics of the interference area distribution characteristics in the second space coordinate system are determined, so that the visual acquisition blind area characteristics of the movable device are determined, visual acquisition parameters are adjusted, and a lifting action image is obtained. The blind area interference is effectively reduced; and the lifting action image is identified to obtain the dynamic characteristics of the dangerous source in the operation area of the lifting device, so that the operation state of the lifting device is adjusted and / or alarm notification is performed, the dependence of the lifting operation on manual safety supervision is reduced, the automatic real-time identification of the dangerous source is realized, and the safety accident of the lifting operation is effectively avoided.

Description

Technical Field

[0001] The present invention relates to the field of hoisting operation monitoring, and in particular to a method and system for identifying hazard sources of hoisting operations based on a movable device. Background Art

[0002] As a heavy machine, cranes are widely used in construction. Cranes usually have an operating radius area. When the crane is lifting, non-staff members are not allowed to be inside the operating radius area. Since the crane's cab is high, the driver inside the cab has a large visual blind spot. In order to ensure the normal operation of the crane, an assistant is configured for the crane. The assistant observes the working status of the crane on the ground, promptly discovers the operating problems of the crane and gives feedback to the driver, so as to ensure that the crane can work safely and efficiently. However, the assistant can easily enter the operating radius area of ​​the crane during the ground observation process, which puts the assistant in a dangerous state. At the same time, the assistant cannot make a comprehensive and accurate observation of the crane's work, which is prone to human errors. It can be seen that it is of great significance to automatically identify and remind the hazard sources of the crane in real time, and quickly adjust the working status of the crane to avoid safety accidents. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for identifying hazard sources in lifting operations based on a movable device. Based on the lifting action characteristic data of the lifting device, the action state of the lifting device in the first spatial coordinate system is determined, so as to determine the distribution characteristics of the interference area of ​​the lifting device during the working process, and provide a basis for avoiding the obstruction of the lifting device during the subsequent visual acquisition process; based on the relationship between the second spatial coordinate system and the first spatial coordinate system corresponding to the visual acquisition of the movable device, the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system are determined, so as to determine the blind area characteristics of the visual acquisition of the movable device, adjust the visual acquisition parameters, obtain the lifting action image, comprehensively shoot the working process of the lifting device, and effectively reduce the interference of the blind area; also identify the lifting action image, obtain the dynamic characteristics of the hazard source inside the operating area of ​​the lifting device, so as to adjust the operating state of the lifting device and / or issue an alarm notification, reduce the dependence of the lifting operation on manual safety supervision, realize the automatic real-time identification of the hazard source, and effectively avoid the occurrence of safety accidents in the lifting operation.

[0004] The present invention is achieved through the following technical solutions:

[0005] The method for identifying the hazard sources of lifting operations based on movable devices includes:

[0006] Acquire lifting action characteristic data of the lifting device, analyze the lifting action characteristic data, and determine the action state of the lifting device in the first space coordinate system; based on the action state, determine the interference area distribution characteristics of the lifting device during the working process;

[0007] Based on the relationship between the second spatial coordinate system corresponding to the visual acquisition of the movable device and the first spatial coordinate system, determine the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system; based on the spatial distribution characteristics, determine the visual acquisition blind area characteristics of the movable device, thereby adjusting the visual acquisition parameters of the movable device, thereby obtaining the lifting action image of the lifting device;

[0008] The lifting action image is identified to determine the dynamic characteristics of the hazard source inside the operating area of ​​the lifting device; based on the dynamic characteristics of the hazard source, the operating state of the lifting device is adjusted and / or an alarm notification operation is performed.

[0009] Optionally, acquiring lifting action characteristic data of the lifting device, analyzing the lifting action characteristic data, and determining the action state of the lifting device in the first space coordinate system; and determining the interference area distribution characteristics of the lifting device during the working process based on the action state, comprises:

[0010] Performing motion detection on the lifting components of the lifting device to obtain six-degree-of-freedom characteristic data and motion speed characteristic data of the lifting action of the lifting component; performing modeling analysis on the six-degree-of-freedom characteristic data and motion speed characteristic data of the lifting action to determine the motion path change trend of the lifting component in the world space coordinate system;

[0011] Based on the changing trend of the motion path and the three-dimensional shape characteristics of the lifting component, the three-dimensional space occupied by the lifting component in the world space coordinate system during its operation is identified to obtain the interference area distribution characteristics corresponding to the lifting component; wherein the interference area distribution characteristics include the continuous area distribution position information formed by the three-dimensional space occupied by the lifting component in the world space coordinate system during its operation.

[0012] Optionally, based on the relationship between the second spatial coordinate system corresponding to the visual acquisition of the movable device and the first spatial coordinate system, the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system are determined; based on the spatial distribution characteristics, the visual acquisition blind area characteristics of the movable device are determined, so as to adjust the visual acquisition parameters of the movable device, thereby obtaining the lifting action image of the lifting device, including:

[0013] Based on the three-dimensional spatial position information of the camera inside the mobile device, determine the second spatial coordinate system corresponding to the visual acquisition of the mobile device; obtain the spatial transformation relationship between the second spatial coordinate system and the first spatial coordinate system, and based on the spatial transformation relationship, perform spatial transformation processing on the interference area distribution characteristics to obtain the spatial distribution characteristics of the interference area distribution characteristics inside the second spatial coordinate system;

[0014] Based on the spatial distribution characteristics, determine the spatial distribution characteristics of the shooting field of view occlusion formed by the lifting device on the camera during operation; based on the spatial distribution characteristics of the shooting field of view occlusion and the shooting field of view range of the camera, determine the visual acquisition blind area characteristics of the camera; wherein the visual acquisition blind area characteristics include the size of the visual acquisition blind area range;

[0015] Based on the visual acquisition blind spot characteristics, the shooting direction of the camera is adjusted to obtain the lifting action image of the lifting device.

[0016] Optionally, the lifting action image is identified to determine the dynamic characteristics of the hazard source inside the operation area of ​​the lifting device; based on the dynamic characteristics of the hazard source, the operation state of the lifting device is adjusted and / or an alarm notification operation is performed, including:

[0017] Recognize the lifting action image to determine the relative position and orientation change characteristics between the lifting components and personnel within the lifting device operation area, and use this as the dynamic characteristics of the hazard source;

[0018] Based on the relative position and orientation change characteristics, predict the time information of the collision event between the lifting component and the personnel; based on the time information and the movement speed of the lifting component, judge whether the lifting component can switch to a static state within a preset time limit; if so, adjust the deceleration of the lifting component and perform a voice alarm notification operation on the personnel; if not, perform a voice alarm notification operation on the personnel.

[0019] Before recognizing the lifting action image, image enhancement adjustment is performed on the lifting action image, including:

[0020] Extract the gray value of each pixel contained in the lifting action image;

[0021] Obtaining the grayscale value difference between every two adjacent pixels according to the grayscale value of each pixel contained in the lifting action image;

[0022] Comparing the grayscale value difference with a preset grayscale value difference threshold;

[0023] Extracting the pixel points whose gray value difference is less than a preset gray value difference threshold as the first target pixel points;

[0024] Obtaining a brightness adjustment coefficient using the grayscale value corresponding to the first target pixel;

[0025] The brightness adjustment coefficient is obtained by the following formula:

[0026]

[0027] Wherein, Q represents the brightness adjustment coefficient; n represents the number of the first target pixel points; H mci represents the average gray value difference corresponding to the i-th first target pixel; H y Indicates the preset gray value difference threshold; H m01b represents the standard deviation of the gray values ​​corresponding to the n first target pixels; H m01bi+1 represents the standard deviation of the gray value corresponding to the i+1th first target pixel; H m01i+1 represents the gray value corresponding to the i+1th first target pixel; H m01i Represents the gray value corresponding to the i-th first target pixel;

[0028] The brightness of the lifting action image is adjusted using the brightness adjustment coefficient, wherein the adjusted brightness is obtained by the following formula:

[0029] L=[1+exp(-Q)]·L c

[0030] Wherein, L represents the brightness value after adjustment; L c Indicates the brightness value before adjustment; Q indicates the brightness adjustment coefficient;

[0031] After brightness adjustment, the gray value difference between each two adjacent pixels of the lifting action image is extracted;

[0032] Comparing the grayscale value difference with a preset grayscale value difference threshold;

[0033] Extracting the pixel points whose gray value difference is less than a preset gray value difference threshold as the second target pixel points;

[0034] Obtaining a contrast adjustment coefficient using the grayscale value corresponding to the second target pixel;

[0035] The contrast adjustment coefficient is obtained by the following formula:

[0036]

[0037] Where R represents the contrast adjustment coefficient; m represents the number of the second target pixel points; Q represents the brightness adjustment coefficient; H xci represents the difference in grayscale value before and after brightness adjustment of the i-th second target pixel; H y01ci represents the average value of the grayscale value difference between the i-th second target pixel and its adjacent pixels before brightness adjustment; H y02ci represents the average value of the grayscale value difference between the ith second target pixel and its adjacent pixels after brightness adjustment;

[0038] The contrast adjustment coefficient is used to adjust the contrast of the lifting action image after brightness adjustment, wherein the adjusted contrast value is obtained by the following formula:

[0039] D=[1+ln(1+exp(-R))]·D c

[0040] Where D represents the adjusted contrast value; D c It indicates the contrast value before adjustment; R indicates the contrast adjustment coefficient.

[0041] The hazard identification system for lifting operations based on mobile devices includes:

[0042] an action state recognition module, used for acquiring lifting action characteristic data of the lifting device, analyzing the lifting action characteristic data, and determining the action state of the lifting device in the first space coordinate system;

[0043] An interference region distribution determination module, used to determine the interference region distribution characteristics of the lifting device during operation based on the action state;

[0044] A visual acquisition preparation module, configured to determine the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system based on the relationship between the second spatial coordinate system corresponding to the visual acquisition by the mobile device and the first spatial coordinate system;

[0045] A visual acquisition adjustment module, used to determine the visual acquisition blind area characteristics of the movable device based on the spatial distribution characteristics, so as to adjust the visual acquisition parameters of the movable device, thereby obtaining the lifting action image of the lifting device;

[0046] A hazard source dynamic identification module, used to identify the lifting action image and determine the dynamic characteristics of the hazard source inside the lifting device operation area;

[0047] The lifting operation adjustment and alarm notification module is used to adjust the operating state of the lifting device and / or perform alarm notification operations based on the dynamic characteristics of the hazard source.

[0048] Optionally, the action state recognition module is used to obtain lifting action characteristic data of the lifting device, analyze the lifting action characteristic data, and determine the action state of the lifting device in the first space coordinate system, including:

[0049] Performing motion detection on the lifting components of the lifting device to obtain six-degree-of-freedom characteristic data and motion speed characteristic data of the lifting action of the lifting component; performing modeling analysis on the six-degree-of-freedom characteristic data and motion speed characteristic data of the lifting action to determine the motion path change trend of the lifting component in the world space coordinate system;

[0050] The interference area distribution determination module is used to determine the interference area distribution characteristics of the lifting device during the working process based on the action state, including:

[0051] Based on the changing trend of the motion path and the three-dimensional shape characteristics of the lifting component, the three-dimensional space occupied by the lifting component in the world space coordinate system during its operation is identified to obtain the interference area distribution characteristics corresponding to the lifting component; wherein the interference area distribution characteristics include the continuous area distribution position information formed by the three-dimensional space occupied by the lifting component in the world space coordinate system during its operation.

[0052] Optionally, the visual acquisition preparation module is used to determine the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system based on the relationship between the second spatial coordinate system corresponding to the visual acquisition of the mobile device and the first spatial coordinate system, including:

[0053] Based on the three-dimensional spatial position information of the camera inside the mobile device, determine the second spatial coordinate system corresponding to the visual acquisition of the mobile device; obtain the spatial transformation relationship between the second spatial coordinate system and the first spatial coordinate system, and based on the spatial transformation relationship, perform spatial transformation processing on the interference area distribution characteristics to obtain the spatial distribution characteristics of the interference area distribution characteristics inside the second spatial coordinate system;

[0054] The visual acquisition adjustment module is used to determine the visual acquisition blind area characteristics of the movable device based on the spatial distribution characteristics, so as to adjust the visual acquisition parameters of the movable device to obtain the lifting action image of the lifting device, including:

[0055] Based on the spatial distribution characteristics, determine the spatial distribution characteristics of the shooting field of view occlusion formed by the lifting device on the camera during operation; based on the spatial distribution characteristics of the shooting field of view occlusion and the shooting field of view range of the camera, determine the visual acquisition blind area characteristics of the camera; wherein the visual acquisition blind area characteristics include the size of the visual acquisition blind area range;

[0056] Based on the visual acquisition blind spot characteristics, the shooting direction of the camera is adjusted to obtain the lifting action image of the lifting device.

[0057] Optionally, the hazard source dynamic identification module is used to identify the lifting action image and determine the dynamic characteristics of the hazard source inside the lifting device operation area, including:

[0058] Recognize the lifting action image to determine the relative position and orientation change characteristics between the lifting components and personnel within the lifting device operation area, and use this as the dynamic characteristics of the hazard source;

[0059] The lifting operation adjustment and alarm notification module is used to adjust the operation state of the lifting device and / or perform an alarm notification operation based on the dynamic characteristics of the hazard source, including:

[0060] Based on the relative position and orientation change characteristics, predict the time information of the collision event between the lifting component and the personnel; based on the time information and the movement speed of the lifting component, judge whether the lifting component can switch to a static state within a preset time limit; if so, adjust the deceleration of the lifting component and perform a voice alarm notification operation on the personnel; if not, perform a voice alarm notification operation on the personnel.

[0061] Before recognizing the lifting action image, image enhancement adjustment is performed on the lifting action image, including:

[0062] Extract the gray value of each pixel contained in the lifting action image;

[0063] Obtaining the grayscale value difference between every two adjacent pixels according to the grayscale value of each pixel contained in the lifting action image;

[0064] Comparing the grayscale value difference with a preset grayscale value difference threshold;

[0065] Extracting the pixel points whose gray value difference is less than a preset gray value difference threshold as the first target pixel points;

[0066] Obtaining a brightness adjustment coefficient using the grayscale value corresponding to the first target pixel;

[0067] The brightness adjustment coefficient is obtained by the following formula:

[0068]

[0069] Wherein, Q represents the brightness adjustment coefficient; n represents the number of the first target pixel points; H mci represents the average gray value difference corresponding to the i-th first target pixel; Hy Indicates the preset gray value difference threshold; H m01b represents the standard deviation of the gray values ​​corresponding to the n first target pixels; H m01bi+1 represents the standard deviation of the gray value corresponding to the i+1th first target pixel; H m01i+1 represents the gray value corresponding to the i+1th first target pixel; H m01i Represents the gray value corresponding to the i-th first target pixel;

[0070] The brightness of the lifting action image is adjusted using the brightness adjustment coefficient, wherein the adjusted brightness is obtained by the following formula:

[0071] L=[1+exp(-Q)]·L c

[0072] Wherein, L represents the brightness value after adjustment; L c Indicates the brightness value before adjustment; Q indicates the brightness adjustment coefficient;

[0073] After brightness adjustment, the gray value difference between each two adjacent pixels of the lifting action image is extracted;

[0074] Comparing the grayscale value difference with a preset grayscale value difference threshold;

[0075] Extracting the pixel points whose gray value difference is less than a preset gray value difference threshold as the second target pixel points;

[0076] Obtaining a contrast adjustment coefficient using the grayscale value corresponding to the second target pixel;

[0077] The contrast adjustment coefficient is obtained by the following formula:

[0078]

[0079] Where R represents the contrast adjustment coefficient; m represents the number of the second target pixel points; Q represents the brightness adjustment coefficient; H xci represents the difference in grayscale value before and after brightness adjustment of the i-th second target pixel; H y01ci represents the average value of the grayscale value difference between the i-th second target pixel and its adjacent pixels before brightness adjustment; H y02ci represents the average value of the grayscale value difference between the ith second target pixel and its adjacent pixels after brightness adjustment;

[0080] The contrast adjustment coefficient is used to adjust the contrast of the lifting action image after brightness adjustment, wherein the adjusted contrast value is obtained by the following formula:

[0081] D=[1+ln(1+exp(-R))]·D c

[0082] Where D represents the adjusted contrast value; D c It indicates the contrast value before adjustment; R indicates the contrast adjustment coefficient.

[0083] Compared with the prior art, the present invention has the following beneficial effects:

[0084] The method and system for identifying hazard sources in lifting operations based on a movable device provided in the present application determine the action state of the lifting device in a first spatial coordinate system based on the lifting action characteristic data of the lifting device, thereby determining the distribution characteristics of the interference area of ​​the lifting device during the working process, and providing a basis for avoiding the obstruction of the lifting device during the subsequent visual acquisition process; based on the relationship between the second spatial coordinate system and the first spatial coordinate system corresponding to the visual acquisition of the movable device, determine the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system, thereby determining the visual acquisition blind area characteristics of the movable device, adjusting the visual acquisition parameters, obtaining the lifting action image, and comprehensively photographing the working process of the lifting device, effectively reducing blind area interference; also identifying the lifting action image, obtaining the dynamic characteristics of the hazard source within the operating area of ​​the lifting device, thereby adjusting the operating state of the lifting device and / or issuing an alarm notification, reducing the dependence of the lifting operation on manual safety supervision, realizing automatic real-time identification of hazard sources, and effectively avoiding the occurrence of safety accidents in lifting operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0086] Figure 1 A schematic flow chart of a method for identifying hazard sources in lifting operations based on a movable device provided by the present invention.

[0087] Figure 2 A schematic structural diagram of a lifting operation hazard identification system based on a movable device provided by the present invention. DETAILED DESCRIPTION

[0088] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only some structures related to the present application are shown in the accompanying drawings, rather than all structures. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0089] The terms "including" and "having" and any variations thereof in this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0090] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0091] See also Figure 1 As shown, an embodiment of the present application provides a method for identifying a hazard source of a lifting operation based on a mobile device. The method for identifying a hazard source of a lifting operation based on a mobile device includes:

[0092] Acquire lifting action characteristic data of the lifting device, analyze the lifting action characteristic data, and determine the action state of the lifting device in the first space coordinate system; based on the action state, determine the interference area distribution characteristics of the lifting device during the working process;

[0093] Based on the relationship between the second spatial coordinate system corresponding to the visual acquisition of the movable device and the first spatial coordinate system, the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system are determined; based on the spatial distribution characteristics, the visual acquisition blind area characteristics of the movable device are determined, so as to adjust the visual acquisition parameters of the movable device, thereby obtaining the lifting action image of the lifting device;

[0094] The lifting action image is identified to determine the dynamic characteristics of the hazard source within the operating area of ​​the lifting device; based on the dynamic characteristics of the hazard source, the operating state of the lifting device is adjusted and / or an alarm notification operation is performed.

[0095] The beneficial effects of the above embodiments are as follows: the method for identifying hazard sources in lifting operations based on movable devices determines the action state of the lifting device in the first spatial coordinate system based on the lifting action characteristic data of the lifting device, thereby determining the distribution characteristics of the interference area of ​​the lifting device during the working process, and providing a basis for avoiding the obstruction of the lifting device during the subsequent visual acquisition process; based on the relationship between the second spatial coordinate system and the first spatial coordinate system corresponding to the visual acquisition of the movable device, the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system are determined, thereby determining the visual acquisition blind area characteristics of the movable device, adjusting the visual acquisition parameters, obtaining the lifting action image, and comprehensively photographing the working process of the lifting device, effectively reducing blind area interference; it also identifies the lifting action image to obtain the dynamic characteristics of the hazard source within the operating area of ​​the lifting device, thereby adjusting the operating state of the lifting device and / or issuing an alarm notification, reducing the dependence of the lifting operation on manual safety supervision, realizing automatic real-time identification of hazard sources, and effectively avoiding the occurrence of safety accidents in lifting operations.

[0096] In another embodiment, obtaining lifting action characteristic data of a lifting device, analyzing the lifting action characteristic data, and determining an action state of the lifting device in a first spatial coordinate system; and determining interference area distribution characteristics of the lifting device during operation based on the action state, including:

[0097] Performing motion detection on the lifting component of the lifting device to obtain six-degree-of-freedom characteristic data and motion speed characteristic data of the lifting action of the lifting component; performing modeling analysis on the six-degree-of-freedom characteristic data and motion speed characteristic data of the lifting action to determine the motion path change trend of the lifting component in the world space coordinate system;

[0098] Based on the changing trend of the motion path and the three-dimensional shape characteristics of the lifting component, the three-dimensional space occupied by the lifting component in the world space coordinate system during its operation is identified to obtain the interference area distribution characteristics corresponding to the lifting component; wherein the interference area distribution characteristics include the continuous area distribution position information formed by the three-dimensional space occupied by the lifting component in the world space coordinate system during its operation.

[0099] The beneficial effects of the above embodiments are that the lifting device includes a lifting component consisting of a lifting arm and a hook, and a three-axis acceleration sensor and a speed sensor are installed inside the lifting device. When the lifting device performs a lifting operation, the lifting component will perform six-degree-of-freedom lifting actions such as rotation, swinging, and lifting. The corresponding three-axis acceleration sensor and speed sensor synchronously collect the six-degree-of-freedom characteristic data and motion speed characteristic data of the lifting action, thereby performing a comprehensive action state characterization of the lifting action, and providing a reliable data basis for the subsequent determination of the action path change trend of the lifting component in the world space coordinate system (i.e., the first space coordinate system). Then, the six-degree-of-freedom characteristic data and motion speed characteristic data of the lifting action are modeled and analyzed to determine the action path change trend of the lifting component in the world space coordinate system, thereby making a comprehensive and accurate prediction of the action path of the lifting component during the entire lifting operation. In addition, the three-dimensional shape features such as the changing trend of the motion path and the three-dimensional shape and size of the lifting component are used to identify the three-dimensional space occupied by the lifting component in the world space coordinate system during its operation, and obtain the interference area distribution characteristics corresponding to the lifting component, thereby comprehensively and accurately characterizing the continuous area distribution position formed by the three-dimensional space occupied by the lifting component in the world space coordinate system during its operation.

[0100] In another embodiment, based on the relationship between the second spatial coordinate system corresponding to the visual acquisition of the movable device and the first spatial coordinate system, the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system are determined; based on the spatial distribution characteristics, the visual acquisition blind area characteristics of the movable device are determined, so as to adjust the visual acquisition parameters of the movable device, thereby obtaining the lifting action image of the lifting device, including:

[0101] Based on the three-dimensional spatial position information of the camera inside the mobile device, determine the second spatial coordinate system corresponding to the visual acquisition of the mobile device; obtain the spatial transformation relationship between the second spatial coordinate system and the first spatial coordinate system, and based on the spatial transformation relationship, perform spatial transformation processing on the interference area distribution feature to obtain the spatial distribution feature of the interference area distribution feature inside the second spatial coordinate system;

[0102] Based on the spatial distribution characteristics, determine the spatial distribution characteristics of the shooting field of view occlusion formed by the lifting device on the camera during operation; based on the spatial distribution characteristics of the shooting field of view occlusion and the shooting field of view range of the camera, determine the visual acquisition blind area characteristics of the camera; wherein the visual acquisition blind area characteristics include the size of the visual acquisition blind area range;

[0103] Based on the blind spot characteristics of the visual acquisition, the shooting direction of the camera is adjusted to obtain the lifting action image of the lifting device.

[0104] The beneficial effect of the above embodiment is that in order to perform automated visual recognition of the lifting action of the lifting device, a movable device such as a robot with a camera is used to perform visual acquisition of the lifting operation area, and the shooting space coordinate system of the camera inside the movable device (i.e., the second space coordinate system) is different from the first space coordinate system. In order to directly reflect the interference area distribution characteristics of the lifting device during operation to the second space coordinate system corresponding to the camera, based on the three-dimensional spatial position information of the camera inside the movable device, the second space coordinate system corresponding to the visual acquisition of the movable device is determined; the spatial transformation relationship between the second space coordinate system and the first space coordinate system is obtained, and based on the spatial transformation relationship, the interference area distribution characteristics are spatially transformed to obtain the spatial distribution characteristics of the interference area distribution characteristics within the second space coordinate system, so that the camera can be accurately controlled to shoot the lifting operation area. In addition, based on the spatial distribution characteristics, the spatial distribution characteristics of the shooting field of view obstruction formed by the lifting device to the camera during operation are determined, and combined with the shooting field of view of the camera, the visual acquisition blind spot characteristics of the camera are determined, so as to adjust the shooting direction of the camera to obtain the lifting action image of the lifting device, so that the camera can avoid the interference of the visual acquisition blind spot and improve the reliability of the lifting action image.

[0105] In another embodiment, the lifting action image is identified to determine the dynamic characteristics of the hazard source inside the operation area of ​​the lifting device; based on the dynamic characteristics of the hazard source, the operation state of the lifting device is adjusted and / or an alarm notification operation is performed, including:

[0106] Identify the lifting action image, determine the relative position and orientation change characteristics between the lifting components and personnel within the lifting device operation area, and use this as the dynamic characteristics of the hazard source;

[0107] Based on the relative position and orientation change characteristics, the time information of the collision event between the lifting component and the personnel is predicted; based on the time information and the movement speed of the lifting component, it is determined whether the lifting component can switch to a stationary state within a preset time limit; if so, the deceleration of the lifting component is adjusted and a voice alarm notification operation is performed on the personnel; if not, a voice alarm notification operation is performed on the personnel.

[0108] The beneficial effect of the above embodiment is to identify the lifting action image, determine the relative position and orientation change characteristics between the lifting components and personnel in the lifting device operation area, and accurately determine the relative position and orientation change between the lifting components and personnel during the lifting operation in the lifting device operation area. In addition, based on the relative position and orientation change characteristics, the time information of the collision event between the lifting components and personnel is predicted, and combined with the movement speed of the lifting components, it is determined whether the lifting components can switch to a static state within a preset time limit, thereby adjusting the operating state of the lifting device and / or performing an alarm notification operation, effectively braking the lifting device quickly and promptly reminding personnel to avoid risks, and effectively avoiding the occurrence of safety accidents in lifting operations.

[0109] In another embodiment, before the lifting action image is recognized, image enhancement adjustment is performed on the lifting action image, including:

[0110] Extract the gray value of each pixel contained in the lifting action image;

[0111] Obtaining the grayscale value difference between every two adjacent pixels according to the grayscale value of each pixel contained in the lifting action image;

[0112] Comparing the grayscale value difference with a preset grayscale value difference threshold;

[0113] Extracting the pixel points whose gray value difference is less than a preset gray value difference threshold as the first target pixel points;

[0114] Obtaining a brightness adjustment coefficient using the grayscale value corresponding to the first target pixel;

[0115] The brightness adjustment coefficient is obtained by the following formula:

[0116]

[0117] Wherein, Q represents the brightness adjustment coefficient; n represents the number of the first target pixel points; H mci represents the average gray value difference corresponding to the i-th first target pixel; H y Indicates the preset gray value difference threshold; H m01b represents the standard deviation of the gray values ​​corresponding to the n first target pixels; H m01bi+1 represents the standard deviation of the gray value corresponding to the i+1th first target pixel; H m01i+1 represents the gray value corresponding to the i+1th first target pixel; H m01i Represents the gray value corresponding to the i-th first target pixel;

[0118] The brightness of the lifting action image is adjusted using the brightness adjustment coefficient, wherein the adjusted brightness is obtained by the following formula:

[0119] L=[1+exp(-Q)]·L c

[0120] Wherein, L represents the brightness value after adjustment; L c Indicates the brightness value before adjustment; Q indicates the brightness adjustment coefficient;

[0121] After brightness adjustment, the gray value difference between each two adjacent pixels of the lifting action image is extracted;

[0122] Comparing the grayscale value difference with a preset grayscale value difference threshold;

[0123] Extracting the pixel points whose gray value difference is less than a preset gray value difference threshold as the second target pixel points;

[0124] Obtaining a contrast adjustment coefficient using the grayscale value corresponding to the second target pixel;

[0125] The contrast adjustment coefficient is obtained by the following formula:

[0126]

[0127] Where R represents the contrast adjustment coefficient; m represents the number of the second target pixel points; Q represents the brightness adjustment coefficient; H xci represents the difference in grayscale value before and after brightness adjustment of the i-th second target pixel; H y01ci represents the average value of the grayscale value difference between the i-th second target pixel and its adjacent pixels before brightness adjustment; H y02ci represents the average value of the grayscale value difference between the ith second target pixel and its adjacent pixels after brightness adjustment;

[0128] The contrast adjustment coefficient is used to adjust the contrast of the lifting action image after brightness adjustment, wherein the adjusted contrast value is obtained by the following formula:

[0129] D=[1+ln(1+exp(-R))]·D c

[0130] Where D represents the adjusted contrast value; D c It indicates the contrast value before adjustment; R indicates the contrast adjustment coefficient.

[0131] The technical effect of the above technical solution is: by extracting pixels (first target pixel and second target pixel) whose gray value difference is less than a preset threshold, and adjusting the brightness and contrast accordingly, the blurred area in the image can be effectively reduced and the overall clarity can be improved. This is crucial for subsequent image recognition and analysis, because clearer images can provide more accurate information. The contrast adjustment part can more accurately adjust the contrast of the image by calculating the difference in gray values ​​before and after brightness adjustment and the average value of the gray value difference between adjacent pixels. This helps to improve the distinction between different objects in the image, making the details in the image more prominent and facilitating subsequent processing and recognition. The brightness adjustment coefficient is calculated based on information such as the gray value difference, average value and standard deviation of the first target pixel, and can make targeted adjustments to the brightness distribution in the image. This adjustment can reduce the overly bright or dark areas in the image, making the overall brightness distribution more uniform, which is conducive to improving the overall visual effect and recognition accuracy of the image. This technical solution avoids indiscriminate processing of the entire image by first extracting key pixels (first target pixels and second target pixels) and then adjusting the brightness and contrast based on the information of these pixels. This targeted processing method can reduce unnecessary calculations and improve processing efficiency. The lifting action images after the above processing have been optimized in terms of clarity, contrast and brightness distribution, which helps to improve the accuracy of subsequent image recognition. Especially under complex or harsh working conditions, this technical solution can significantly improve image quality and improve the reliability and stability of recognition.

[0132] In summary, this technical solution can significantly improve the image clarity, contrast, brightness distribution uniformity and processing efficiency in terms of the performance indicators of lifting action image processing, thereby enhancing the accuracy of recognition. This is of great significance for the safety monitoring of lifting operations, accident prevention and subsequent data analysis.

[0133] See also Figure 2 As shown, an embodiment of the present application provides a system for identifying hazard sources of lifting operations based on a mobile device. The system for identifying hazard sources of lifting operations based on a mobile device includes:

[0134] An action state recognition module is used to obtain lifting action characteristic data of the lifting device, analyze the lifting action characteristic data, and determine the action state of the lifting device in the first space coordinate system;

[0135] An interference area distribution determination module, used to determine the interference area distribution characteristics of the lifting device during operation based on the action state;

[0136] A visual acquisition preparation module, used to determine the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system based on the relationship between the second spatial coordinate system corresponding to the visual acquisition of the mobile device and the first spatial coordinate system;

[0137] A visual acquisition adjustment module, used to determine the visual acquisition blind area characteristics of the movable device based on the spatial distribution characteristics, so as to adjust the visual acquisition parameters of the movable device, thereby obtaining the lifting action image of the lifting device;

[0138] A hazard source dynamic identification module is used to identify the lifting action image and determine the dynamic characteristics of the hazard source inside the lifting device operation area;

[0139] The lifting operation adjustment and alarm notification module is used to adjust the operating state of the lifting device and / or perform alarm notification operations based on the dynamic characteristics of the hazard source.

[0140] The beneficial effects of the above embodiments are as follows: the system for identifying hazard sources of lifting operations based on movable devices determines the action state of the lifting device in the first spatial coordinate system based on the lifting action characteristic data of the lifting device, thereby determining the distribution characteristics of the interference area of ​​the lifting device during the working process, and providing a basis for avoiding the obstruction of the lifting device during the subsequent visual acquisition process; based on the relationship between the second spatial coordinate system and the first spatial coordinate system corresponding to the visual acquisition of the movable device, the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system are determined, thereby determining the blind area characteristics of the visual acquisition of the movable device, adjusting the visual acquisition parameters, obtaining the lifting action image, and comprehensively photographing the working process of the lifting device, effectively reducing blind area interference; it also identifies the lifting action image to obtain the dynamic characteristics of the hazard source within the operating area of ​​the lifting device, thereby adjusting the operating state of the lifting device and / or issuing an alarm notification, reducing the dependence of the lifting operation on manual safety supervision, realizing automatic real-time identification of hazard sources, and effectively avoiding the occurrence of safety accidents in lifting operations.

[0141] In another embodiment, the action state recognition module is used to obtain lifting action characteristic data of the lifting device, analyze the lifting action characteristic data, and determine the action state of the lifting device in the first space coordinate system, including:

[0142] Performing motion detection on the lifting component of the lifting device to obtain six-degree-of-freedom characteristic data and motion speed characteristic data of the lifting action of the lifting component; performing modeling analysis on the six-degree-of-freedom characteristic data and motion speed characteristic data of the lifting action to determine the motion path change trend of the lifting component in the world space coordinate system;

[0143] The interference area distribution determination module is used to determine the interference area distribution characteristics of the lifting device during the working process based on the action state, including:

[0144] Based on the changing trend of the motion path and the three-dimensional shape characteristics of the lifting component, the three-dimensional space occupied by the lifting component in the world space coordinate system during its operation is identified to obtain the interference area distribution characteristics corresponding to the lifting component; wherein the interference area distribution characteristics include the continuous area distribution position information formed by the three-dimensional space occupied by the lifting component in the world space coordinate system during its operation.

[0145] The beneficial effects of the above embodiments are that the lifting device includes a lifting component consisting of a lifting arm and a hook, and a three-axis acceleration sensor and a speed sensor are installed inside the lifting device. When the lifting device performs a lifting operation, the lifting component will perform six-degree-of-freedom lifting actions such as rotation, swinging, and lifting. The corresponding three-axis acceleration sensor and speed sensor synchronously collect the six-degree-of-freedom characteristic data and motion speed characteristic data of the lifting action, thereby performing a comprehensive action state characterization of the lifting action, and providing a reliable data basis for the subsequent determination of the action path change trend of the lifting component in the world space coordinate system (i.e., the first space coordinate system). Then, the six-degree-of-freedom characteristic data and motion speed characteristic data of the lifting action are modeled and analyzed to determine the action path change trend of the lifting component in the world space coordinate system, thereby making a comprehensive and accurate prediction of the action path of the lifting component during the entire lifting operation. In addition, the three-dimensional shape features such as the changing trend of the motion path and the three-dimensional shape and size of the lifting component are used to identify the three-dimensional space occupied by the lifting component in the world space coordinate system during its operation, and obtain the interference area distribution characteristics corresponding to the lifting component, thereby comprehensively and accurately characterizing the continuous area distribution position formed by the three-dimensional space occupied by the lifting component in the world space coordinate system during its operation.

[0146] In another embodiment, the visual acquisition preparation module is used to determine the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system based on the relationship between the second spatial coordinate system corresponding to the visual acquisition of the mobile device and the first spatial coordinate system, including:

[0147] Based on the three-dimensional spatial position information of the camera inside the mobile device, determine the second spatial coordinate system corresponding to the visual acquisition of the mobile device; obtain the spatial transformation relationship between the second spatial coordinate system and the first spatial coordinate system, and based on the spatial transformation relationship, perform spatial transformation processing on the interference area distribution feature to obtain the spatial distribution feature of the interference area distribution feature inside the second spatial coordinate system;

[0148] The visual acquisition adjustment module is used to determine the visual acquisition blind area characteristics of the movable device based on the spatial distribution characteristics, so as to adjust the visual acquisition parameters of the movable device, thereby obtaining the lifting action image of the lifting device, including:

[0149] Based on the spatial distribution characteristics, determine the spatial distribution characteristics of the shooting field of view occlusion formed by the lifting device on the camera during operation; based on the spatial distribution characteristics of the shooting field of view occlusion and the shooting field of view range of the camera, determine the visual acquisition blind area characteristics of the camera; wherein the visual acquisition blind area characteristics include the size of the visual acquisition blind area range;

[0150] Based on the blind spot characteristics of the visual acquisition, the shooting direction of the camera is adjusted to obtain the lifting action image of the lifting device.

[0151] The beneficial effect of the above embodiment is that in order to perform automated visual recognition of the lifting action of the lifting device, a movable device such as a robot with a camera is used to perform visual acquisition of the lifting operation area, and the shooting space coordinate system of the camera inside the movable device (i.e., the second space coordinate system) is different from the first space coordinate system. In order to directly reflect the interference area distribution characteristics of the lifting device during operation to the second space coordinate system corresponding to the camera, based on the three-dimensional spatial position information of the camera inside the movable device, the second space coordinate system corresponding to the visual acquisition of the movable device is determined; the spatial transformation relationship between the second space coordinate system and the first space coordinate system is obtained, and based on the spatial transformation relationship, the interference area distribution characteristics are spatially transformed to obtain the spatial distribution characteristics of the interference area distribution characteristics within the second space coordinate system, so that the camera can be accurately controlled to shoot the lifting operation area. In addition, based on the spatial distribution characteristics, the spatial distribution characteristics of the shooting field of view obstruction formed by the lifting device to the camera during operation are determined, and combined with the shooting field of view of the camera, the visual acquisition blind spot characteristics of the camera are determined, so as to adjust the shooting direction of the camera to obtain the lifting action image of the lifting device, so that the camera can avoid the interference of the visual acquisition blind spot and improve the reliability of the lifting action image.

[0152] In another embodiment, the hazard source dynamic identification module is used to identify the lifting action image and determine the dynamic characteristics of the hazard source inside the lifting device operation area, including:

[0153] Identify the lifting action image, determine the relative position and orientation change characteristics between the lifting components and personnel within the lifting device operation area, and use this as the dynamic characteristics of the hazard source;

[0154] The lifting operation adjustment and alarm notification module is used to adjust the operation state of the lifting device and / or perform an alarm notification operation based on the dynamic characteristics of the hazard source, including:

[0155] Based on the relative position and orientation change characteristics, the time information of the collision event between the lifting component and the personnel is predicted; based on the time information and the movement speed of the lifting component, it is determined whether the lifting component can switch to a stationary state within a preset time limit; if so, the deceleration of the lifting component is adjusted and a voice alarm notification operation is performed on the personnel; if not, a voice alarm notification operation is performed on the personnel.

[0156] The beneficial effect of the above embodiment is to identify the lifting action image, determine the relative position and orientation change characteristics between the lifting components and personnel in the lifting device operation area, and accurately determine the relative position and orientation change between the lifting components and personnel during the lifting operation in the lifting device operation area. In addition, based on the relative position and orientation change characteristics, the time information of the collision event between the lifting components and personnel is predicted, and combined with the movement speed of the lifting components, it is determined whether the lifting components can switch to a static state within a preset time limit, thereby adjusting the operating state of the lifting device and / or performing an alarm notification operation, effectively braking the lifting device quickly and promptly reminding personnel to avoid risks, and effectively avoiding the occurrence of safety accidents in lifting operations.

[0157] In another embodiment, before the lifting action image is recognized, image enhancement adjustment is performed on the lifting action image, including:

[0158] Extract the gray value of each pixel contained in the lifting action image;

[0159] Obtaining the grayscale value difference between every two adjacent pixels according to the grayscale value of each pixel contained in the lifting action image;

[0160] Comparing the grayscale value difference with a preset grayscale value difference threshold;

[0161] Extracting the pixel points whose gray value difference is less than a preset gray value difference threshold as the first target pixel points;

[0162] Obtaining a brightness adjustment coefficient using the grayscale value corresponding to the first target pixel;

[0163] The brightness adjustment coefficient is obtained by the following formula:

[0164]

[0165] Wherein, Q represents the brightness adjustment coefficient; n represents the number of the first target pixel points; H mci represents the average gray value difference corresponding to the i-th first target pixel; H y Indicates the preset gray value difference threshold; H m01brepresents the standard deviation of the gray values ​​corresponding to the n first target pixels; H m01bi+1 represents the standard deviation of the gray value corresponding to the i+1th first target pixel; H m01i+1 represents the gray value corresponding to the i+1th first target pixel; H m01i Represents the gray value corresponding to the i-th first target pixel;

[0166] The brightness of the lifting action image is adjusted using the brightness adjustment coefficient, wherein the adjusted brightness is obtained by the following formula:

[0167] L=[1+exp(-Q)]·L c

[0168] Wherein, L represents the brightness value after adjustment; L c Indicates the brightness value before adjustment; Q indicates the brightness adjustment coefficient;

[0169] After brightness adjustment, the gray value difference between each two adjacent pixels of the lifting action image is extracted;

[0170] Comparing the grayscale value difference with a preset grayscale value difference threshold;

[0171] Extracting the pixel points whose gray value difference is less than a preset gray value difference threshold as the second target pixel points;

[0172] Obtaining a contrast adjustment coefficient using the grayscale value corresponding to the second target pixel;

[0173] The contrast adjustment coefficient is obtained by the following formula:

[0174]

[0175] Where R represents the contrast adjustment coefficient; m represents the number of the second target pixel points; Q represents the brightness adjustment coefficient; H xci represents the difference in grayscale value before and after brightness adjustment of the i-th second target pixel; H y01ci represents the average value of the grayscale value difference between the i-th second target pixel and its adjacent pixels before brightness adjustment; H y02ci represents the average value of the grayscale value difference between the ith second target pixel and its adjacent pixels after brightness adjustment;

[0176] The contrast adjustment coefficient is used to adjust the contrast of the lifting action image after brightness adjustment, wherein the adjusted contrast value is obtained by the following formula:

[0177] D=[1+ln(1+exp(-R))]·D c

[0178] Where D represents the adjusted contrast value; D c It indicates the contrast value before adjustment; R indicates the contrast adjustment coefficient.

[0179] The technical effect of the above technical solution is: by extracting pixels (first target pixel and second target pixel) whose gray value difference is less than a preset threshold, and adjusting the brightness and contrast accordingly, the blurred area in the image can be effectively reduced and the overall clarity can be improved. This is crucial for subsequent image recognition and analysis, because clearer images can provide more accurate information. The contrast adjustment part can more accurately adjust the contrast of the image by calculating the difference in gray values ​​before and after brightness adjustment and the average value of the gray value difference between adjacent pixels. This helps to improve the distinction between different objects in the image, making the details in the image more prominent and facilitating subsequent processing and recognition. The brightness adjustment coefficient is calculated based on information such as the gray value difference, average value and standard deviation of the first target pixel, and can make targeted adjustments to the brightness distribution in the image. This adjustment can reduce the overly bright or dark areas in the image, making the overall brightness distribution more uniform, which is conducive to improving the overall visual effect and recognition accuracy of the image. This technical solution avoids indiscriminate processing of the entire image by first extracting key pixels (first target pixels and second target pixels) and then adjusting the brightness and contrast based on the information of these pixels. This targeted processing method can reduce unnecessary calculations and improve processing efficiency. The lifting action images after the above processing have been optimized in terms of clarity, contrast and brightness distribution, which helps to improve the accuracy of subsequent image recognition. Especially under complex or harsh working conditions, this technical solution can significantly improve image quality and improve the reliability and stability of recognition.

[0180] In summary, this technical solution can significantly improve the image clarity, contrast, brightness distribution uniformity and processing efficiency in terms of the performance indicators of lifting action image processing, thereby enhancing the accuracy of recognition. This is of great significance for the safety monitoring of lifting operations, accident prevention and subsequent data analysis.

[0181] In general, the method and system for identifying hazard sources in lifting operations based on movable devices determine the action state of the lifting device in the first spatial coordinate system based on the lifting action characteristic data of the lifting device, thereby determining the distribution characteristics of the interference area of ​​the lifting device during the working process, and providing a basis for avoiding the obstruction of the lifting device in the subsequent visual acquisition process; based on the relationship between the second spatial coordinate system and the first spatial coordinate system corresponding to the visual acquisition of the movable device, determine the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system, thereby determining the visual acquisition blind area characteristics of the movable device, adjusting the visual acquisition parameters, obtaining the lifting action image, and comprehensively photographing the working process of the lifting device, effectively reducing blind area interference; also identifying the lifting action image, obtaining the dynamic characteristics of the hazard source within the operating area of ​​the lifting device, thereby adjusting the operating state of the lifting device and / or issuing an alarm notification, reducing the dependence of the lifting operation on manual safety supervision, realizing automatic real-time identification of hazard sources, and effectively avoiding the occurrence of safety accidents in lifting operations.

[0182] The above is only a specific implementation of the present invention, and any other improvements made based on the concept of the present invention are considered to be within the protection scope of the present invention.

Claims

1. A method for identifying hazard sources in lifting operations based on a movable device, characterized in that: include: Acquiring lifting action characteristic data of the lifting device, analyzing the lifting action characteristic data, and determining the action state of the lifting device in the first space coordinate system; Based on the action state, determining the interference area distribution characteristics of the lifting device during operation; Determine the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system based on the relationship between the second spatial coordinate system and the first spatial coordinate system corresponding to the visual acquisition performed by the mobile device; Based on the spatial distribution characteristics, determining the visual acquisition blind area characteristics of the movable device, thereby adjusting the visual acquisition parameters of the movable device, thereby obtaining the lifting action image of the lifting device; Recognize the lifting action image to determine the dynamic characteristics of the danger source inside the lifting device operation area; Based on the dynamic characteristics of the hazard source, the operating state of the lifting device is adjusted and / or an alarm notification operation is performed.

2. The method for identifying hazard sources of lifting operations based on a mobile device according to claim 1, characterized in that: Acquiring lifting action characteristic data of the lifting device, analyzing the lifting action characteristic data, and determining the action state of the lifting device in the first space coordinate system; Based on the action state, determining the interference area distribution characteristics of the lifting device during the working process includes: Performing motion detection on the lifting components of the lifting device to obtain six-degree-of-freedom characteristic data and motion speed characteristic data of the lifting action of the lifting component; performing modeling analysis on the six-degree-of-freedom characteristic data and motion speed characteristic data of the lifting action to determine the motion path change trend of the lifting component in the world space coordinate system; Based on the changing trend of the motion path and the three-dimensional shape characteristics of the lifting component, the three-dimensional space occupied by the lifting component in the world space coordinate system during its operation is identified to obtain the interference area distribution characteristics corresponding to the lifting component; wherein the interference area distribution characteristics include the continuous area distribution position information formed by the three-dimensional space occupied by the lifting component in the world space coordinate system during its operation.

3. The method for identifying hazard sources of lifting operations based on a mobile device according to claim 1, characterized in that: Determine the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system based on the relationship between the second spatial coordinate system and the first spatial coordinate system corresponding to the visual acquisition performed by the mobile device; Based on the spatial distribution characteristics, the visual acquisition blind area characteristics of the movable device are determined, and the visual acquisition parameters of the movable device are adjusted to obtain the lifting action image of the lifting device, including: Based on the three-dimensional spatial position information of the camera inside the mobile device, determine the second spatial coordinate system corresponding to the visual acquisition of the mobile device; obtain the spatial transformation relationship between the second spatial coordinate system and the first spatial coordinate system, and based on the spatial transformation relationship, perform spatial transformation processing on the interference area distribution characteristics to obtain the spatial distribution characteristics of the interference area distribution characteristics inside the second spatial coordinate system; Based on the spatial distribution characteristics, determine the spatial distribution characteristics of the shooting field of view occlusion formed by the lifting device on the camera during operation; based on the spatial distribution characteristics of the shooting field of view occlusion and the shooting field of view range of the camera, determine the visual acquisition blind spot characteristics of the camera; Wherein, the visual acquisition blind area feature includes the size of the visual acquisition blind area; Based on the visual acquisition blind spot characteristics, the shooting direction of the camera is adjusted to obtain the lifting action image of the lifting device.

4. The method for identifying hazard sources of lifting operations based on a mobile device according to claim 1, characterized in that: Recognize the lifting action image to determine the dynamic characteristics of the danger source inside the lifting device operation area; Based on the dynamic characteristics of the hazard source, adjusting the operating state of the lifting device and / or performing an alarm notification operation includes: Recognize the lifting action image to determine the relative position and orientation change characteristics between the lifting components and personnel within the lifting device operation area, and use this as the dynamic characteristics of the hazard source; Based on the relative position and orientation change characteristics, predict the time information of the collision event between the lifting component and the personnel; based on the time information and the movement speed of the lifting component, judge whether the lifting component can switch to a static state within a preset time limit; if so, adjust the deceleration of the lifting component and perform a voice alarm notification operation on the personnel; if not, perform a voice alarm notification operation on the personnel.

5. The method for identifying hazard sources of lifting operations based on a mobile device according to claim 4, characterized in that: Before recognizing the lifting action image, image enhancement adjustment is performed on the lifting action image, including: Extract the gray value of each pixel contained in the lifting action image; Obtaining the grayscale value difference between every two adjacent pixels according to the grayscale value of each pixel contained in the lifting action image; Comparing the grayscale value difference with a preset grayscale value difference threshold; Extracting the pixel points whose gray value difference is less than a preset gray value difference threshold as the first target pixel points; Obtaining a brightness adjustment coefficient using the grayscale value corresponding to the first target pixel; The brightness adjustment coefficient is obtained by the following formula: Wherein, Q represents the brightness adjustment coefficient; n represents the number of the first target pixel points; H mci represents the average gray value difference corresponding to the i-th first target pixel; H y Indicates the preset gray value difference threshold; H m01b represents the standard deviation of the gray values ​​corresponding to the n first target pixels; H m01bi+1 represents the standard deviation of the gray value corresponding to the i+1th first target pixel; H m01i+1 represents the gray value corresponding to the i+1th first target pixel; H m01i Represents the gray value corresponding to the i-th first target pixel; The brightness of the lifting action image is adjusted using the brightness adjustment coefficient, wherein the adjusted brightness is obtained by the following formula: L=[1+exp(-Q)]·L c Wherein, L represents the brightness value after adjustment; L c Indicates the brightness value before adjustment; Q indicates the brightness adjustment coefficient; After brightness adjustment, the gray value difference between each two adjacent pixels of the lifting action image is extracted; Comparing the grayscale value difference with a preset grayscale value difference threshold; Extracting the pixel points whose gray value difference is less than a preset gray value difference threshold as the second target pixel points; Obtaining a contrast adjustment coefficient using the grayscale value corresponding to the second target pixel; The contrast adjustment coefficient is obtained by the following formula: Wherein, R represents the contrast adjustment coefficient; m represents the number of the second target pixel points; Q represents the brightness adjustment coefficient; H xci represents the difference in grayscale value before and after brightness adjustment of the i-th second target pixel; H y01ci represents the average value of the grayscale value difference between the i-th second target pixel and its adjacent pixels before brightness adjustment; H y02ci represents the average value of the grayscale value difference between the ith second target pixel and its adjacent pixels after brightness adjustment; The contrast adjustment coefficient is used to adjust the contrast of the lifting action image after brightness adjustment, wherein the adjusted contrast value is obtained by the following formula: D=[1+ln(1+exp(-R))]·D c Where D represents the adjusted contrast value; D c It indicates the contrast value before adjustment; R indicates the contrast adjustment coefficient.

6. A system for identifying hazard sources of lifting operations based on a mobile device, characterized in that: include: An action state recognition module is used to obtain lifting action characteristic data of the lifting device, analyze the lifting action characteristic data, and determine the action state of the lifting device in the first space coordinate system; An interference region distribution determination module, used to determine the interference region distribution characteristics of the lifting device during operation based on the action state; A visual acquisition preparation module, configured to determine the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system based on the relationship between the second spatial coordinate system corresponding to the visual acquisition performed by the mobile device and the first spatial coordinate system; A visual acquisition adjustment module, used to determine the visual acquisition blind area characteristics of the movable device based on the spatial distribution characteristics, so as to adjust the visual acquisition parameters of the movable device, thereby obtaining the lifting action image of the lifting device; A hazard source dynamic identification module, used to identify the lifting action image and determine the dynamic characteristics of the hazard source inside the lifting device operation area; The lifting operation adjustment and alarm notification module is used to adjust the operating state of the lifting device and / or perform alarm notification operations based on the dynamic characteristics of the hazard source.

7. The mobile device-based lifting operation hazard identification system according to claim 6, characterized in that: The action state recognition module is used to obtain lifting action characteristic data of the lifting device, analyze the lifting action characteristic data, and determine the action state of the lifting device in the first space coordinate system, including: Performing motion detection on the lifting components of the lifting device to obtain six-degree-of-freedom characteristic data and motion speed characteristic data of the lifting action of the lifting component; performing modeling analysis on the six-degree-of-freedom characteristic data and motion speed characteristic data of the lifting action to determine the motion path change trend of the lifting component in the world space coordinate system; The interference area distribution determination module is used to determine the interference area distribution characteristics of the lifting device during the working process based on the action state, including: Based on the changing trend of the motion path and the three-dimensional shape characteristics of the lifting component, the three-dimensional space occupied by the lifting component in the world space coordinate system during its operation is identified to obtain the interference area distribution characteristics corresponding to the lifting component; wherein the interference area distribution characteristics include the continuous area distribution position information formed by the three-dimensional space occupied by the lifting component in the world space coordinate system during its operation.

8. The mobile device-based lifting operation hazard source identification system according to claim 6 is characterized in that: The visual acquisition preparation module is used to determine the spatial distribution characteristics of the interference area distribution characteristics within the second spatial coordinate system based on the relationship between the second spatial coordinate system corresponding to the visual acquisition of the mobile device and the first spatial coordinate system, including: Based on the three-dimensional spatial position information of the camera inside the mobile device, determine the second spatial coordinate system corresponding to the visual acquisition of the mobile device; obtain the spatial transformation relationship between the second spatial coordinate system and the first spatial coordinate system, and based on the spatial transformation relationship, perform spatial transformation processing on the interference area distribution characteristics to obtain the spatial distribution characteristics of the interference area distribution characteristics inside the second spatial coordinate system; The visual acquisition adjustment module is used to determine the visual acquisition blind area characteristics of the movable device based on the spatial distribution characteristics, so as to adjust the visual acquisition parameters of the movable device to obtain the lifting action image of the lifting device, including: Based on the spatial distribution characteristics, determine the spatial distribution characteristics of the shooting field of view occlusion formed by the lifting device on the camera during operation; based on the spatial distribution characteristics of the shooting field of view occlusion and the shooting field of view range of the camera, determine the visual acquisition blind spot characteristics of the camera; Wherein, the visual acquisition blind area feature includes the size of the visual acquisition blind area; Based on the visual acquisition blind spot characteristics, the shooting direction of the camera is adjusted to obtain the lifting action image of the lifting device.

9. The system for identifying hazard sources of lifting operations based on a mobile device according to claim 6, characterized in that: The hazard source dynamic identification module is used to identify the lifting action image and determine the dynamic characteristics of the hazard source inside the lifting device operation area, including: Recognize the lifting action image to determine the relative position and orientation change characteristics between the lifting components and personnel within the lifting device operation area, and use this as the dynamic characteristics of the hazard source; The lifting operation adjustment and alarm notification module is used to adjust the operation state of the lifting device and / or perform an alarm notification operation based on the dynamic characteristics of the hazard source, including: Based on the relative position and orientation change characteristics, predict the time information of the collision event between the lifting component and the personnel; based on the time information and the movement speed of the lifting component, judge whether the lifting component can switch to a static state within a preset time limit; if so, adjust the deceleration of the lifting component and perform a voice alarm notification operation on the personnel; if not, perform a voice alarm notification operation on the personnel.

10. The mobile device-based lifting operation hazard source identification system according to claim 9, characterized in that: Before recognizing the lifting action image, image enhancement adjustment is performed on the lifting action image, including: Extracting the grayscale value of each pixel contained in the lifting action image; obtaining the grayscale value difference between every two adjacent pixels according to the grayscale value of each pixel contained in the lifting action image; Comparing the grayscale value difference with a preset grayscale value difference threshold; Extracting the pixel points whose gray value difference is less than a preset gray value difference threshold as the first target pixel points; Obtaining a brightness adjustment coefficient using the grayscale value corresponding to the first target pixel; The brightness adjustment coefficient is obtained by the following formula: Wherein, Q represents the brightness adjustment coefficient; n represents the number of the first target pixel points; H mci represents the average gray value difference corresponding to the i-th first target pixel; H y Indicates the preset gray value difference threshold; H m01b represents the standard deviation of the gray values ​​corresponding to the n first target pixels; H m01bi+1 represents the standard deviation of the gray value corresponding to the i+1th first target pixel; H m01i+1 represents the gray value corresponding to the i+1th first target pixel; H m01i Represents the gray value corresponding to the i-th first target pixel; The brightness of the lifting action image is adjusted using the brightness adjustment coefficient, wherein the adjusted brightness is obtained by the following formula: L=[1+exp(-Q)]·L c Wherein, L represents the brightness value after adjustment; L c Indicates the brightness value before adjustment; Q indicates the brightness adjustment coefficient; After brightness adjustment, the gray value difference between each two adjacent pixels of the lifting action image is extracted; Comparing the grayscale value difference with a preset grayscale value difference threshold; Extracting the pixel points whose gray value difference is less than a preset gray value difference threshold as the second target pixel points; Obtaining a contrast adjustment coefficient using the grayscale value corresponding to the second target pixel; The contrast adjustment coefficient is obtained by the following formula: Wherein, R represents the contrast adjustment coefficient; m represents the number of the second target pixel points; Q represents the brightness adjustment coefficient; H xci represents the difference in grayscale value before and after brightness adjustment of the i-th second target pixel; H y01ci represents the average value of the grayscale value difference between the i-th second target pixel and its adjacent pixels before brightness adjustment; H y02ci represents the average value of the grayscale value difference between the ith second target pixel and its adjacent pixels after brightness adjustment; The contrast adjustment coefficient is used to adjust the contrast of the lifting action image after brightness adjustment, wherein the adjusted contrast value is obtained by the following formula: D=[1+ln(1+exp(-R))]·D c Where D represents the adjusted contrast value; D c It indicates the contrast value before adjustment; R indicates the contrast adjustment coefficient.

Images