Automatic obstacle avoidance method and system based on Internet of Things

Abstract

The present application relates to the field of data processing technology, and in particular to an automatic obstacle avoidance method and system based on the Internet of Things. The method comprises the following steps: dividing a device into regions to form multiple component regions; for each component region, determining obstacles that have an impact on the component region as impacting objects, and calculating the threat level of the impacting objects to the component region; for each component region, obtaining a repulsion adjustment weight based on the threat level of each impacting object to the component region; obtaining the initial repulsion of the impacting object, taking the product of the repulsion adjustment weight and the initial repulsion of the impacting object as the local optimal repulsion, and obtaining the comprehensive repulsion of each component region based on multiple local optimal repulsions; and automatically avoiding obstacles using an artificial potential energy method based on the comprehensive repulsion and gravitational field experienced by each component region. The present application has the effect of improving obstacle avoidance accuracy and reducing collision risk.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an automatic obstacle avoidance method and system based on the Internet of Things. Background Art

[0002] With the development of industrial machinery, integration has become a common equipment requirement in the chemical and fine chemical industries. Skid-mounted equipment is one such integration method. Skid-mounted equipment primarily involves a group of equipment mounted on a chassis that can be moved using a crowbar, facilitating the relocation of skid-mounted equipment. Traditionally, the relocation of skid-mounted equipment relied on manual labor. However, with the advancement of industrial machinery, automated transport methods such as cranes or other transport methods (such as movable chassis) have gradually replaced manual handling. While cranes or mobile chassis can replace manual handling of equipment, manual operation is still required to avoid collisions with other equipment or objects during movement. This is particularly true for chemical equipment manufacturers, who require special care during both loading and unloading of equipment to prevent damage. With the advancement of the Internet of Things (IoT) and electronic information technology, automated obstacle avoidance methods are increasingly being applied to the movement of skid-mounted equipment to address complex and changing environments while further reducing the workload of personnel. The artificial potential field method is a common obstacle avoidance method in the field. Its core idea is to construct a repulsive potential field around the obstacle and a gravitational potential field at the target point. Based on the gravitational and repulsive forces acting on the controlled object (i.e., the moving object, in this case, the skid-mounted equipment) in the composite field, it searches for a collision-free path, guides the movement of the controlled object, and realizes automatic obstacle avoidance for the controlled object.

[0003] In real-world production environments, skid-mounted equipment comes in a wide variety of shapes and sizes. Furthermore, during the movement of skid-mounted equipment, obstacles in its surroundings are not simply static, but often a mixture of dynamic and static obstacles. Traditional artificial potential field methods treat equipment and obstacles as point masses. During obstacle avoidance, they only consider the distance between the entire equipment and obstacles, failing to perceive the position of specific equipment components. This results in an inability to identify collision risks with specific parts of the equipment, leading to ineffective or delayed obstacle avoidance. Summary of the Invention

[0004] In order to solve the problem that traditional obstacle avoidance methods cannot effectively avoid obstacles or avoid obstacles in a timely manner, the present application provides an automatic obstacle avoidance method and system based on the Internet of Things.

[0005] In the first aspect, the present application provides an automatic obstacle avoidance method based on the Internet of Things, which adopts the following technical solutions:

[0006] An automatic obstacle avoidance method based on the Internet of Things comprises the following steps: dividing a device into regions to form multiple component regions; for each component region, determining obstacles that have an impact on the component region as impacting objects, and calculating the threat level of the impacting objects to the component region; for each component region, obtaining a repulsion adjustment weight based on the threat level of each impacting object to the component region; obtaining an initial repulsion of the impacting object, multiplying the repulsion adjustment weight by the initial repulsion of the impacting object as a local optimal repulsion, and obtaining a comprehensive repulsion of each component region based on the multiple local optimal repulsions; and automatically avoiding obstacles using an artificial potential energy method based on the comprehensive repulsion and gravitational field experienced by each component region.

[0007] The step of calculating the threat level of the influencing object to the component area includes: obtaining the local density of the influencing object based on the distribution of the influencing object; obtaining the motion consistency between the influencing object and the component area based on the motion state of the influencing object; and using the ratio of the local density of the influencing object to the motion consistency as the threat level of the influencing object to the component area.

[0008] On the one hand, in this application, the skid-mounted equipment is divided to form multiple component areas, the comprehensive repulsive force on each component area is calculated, and obstacle avoidance is performed for each component area based on the comprehensive repulsive force corresponding to each component area and the attraction in the artificial potential field method. Compared with the traditional artificial potential field method for obstacle avoidance, in this application, each component area can be adjusted individually during the movement of the skid-mounted equipment, thereby making the obstacle avoidance more accurate, and improving the timeliness of obstacle avoidance and reducing the risk of collision.

[0009] Furthermore, this method calculates the degree of influence of different influencing objects on the component region. Influencing objects with poor motion consistency and high local density are determined to pose a greater threat to the component region. The threat level represents the risk of collision between the influencing object and the component region. Based on the threat level, the initial repulsive force is adjusted, thereby adjusting the comprehensive repulsive force. By combining the comprehensive repulsive force with the gravitational field in the artificial potential field method, the direction of the component region's motion can be changed, achieving precise obstacle avoidance in the component region.

[0010] Optionally, the step of obtaining the local density of the influencing object includes: determining a reference area of ​​the influencing object, obtaining the average of the Euclidean distances between the influencing object and each obstacle in the corresponding reference area as the distribution distance, and determining the local density of the influencing object based on the distribution distance, wherein the local density of the influencing object is inversely proportional to the distribution distance.

[0011] Determine the reference area of ​​the influencing object and calculate the local density of the influencing object area based on the distance between the obstacle and the influencing object in the reference area. If the distance between the obstacle and the influencing object in the reference area is small, it means that the local density corresponding to the influencing object is large, which means that the component area has a higher difficulty in the obstacle avoidance process and thus has a higher collision risk.

[0012] Optionally, when the influencing object and obstacles in the influencing object area are both stationary: the motion consistency between the influencing object and the component area is 1;

[0013] When an obstacle exists in the influencing object or the reference area corresponding to the influencing object and is in motion, the horizontal velocity difference, vertical velocity difference, and velocity direction difference between the influencing object and the component area are obtained, and the product of the horizontal velocity difference, vertical velocity difference, and velocity direction difference is used as the total difference to calculate the motion consistency.

[0014] During obstacle avoidance, if the influencing object and the obstacles in the corresponding reference area are both stationary, obstacle avoidance is relatively easy, and therefore poses a relatively low threat to the component area. Therefore, the motion consistency of the influencing object in this state is directly defined as 1. However, if the influencing object or the obstacles in the reference area corresponding to the influencing object are moving, the motion consistency of the influencing object and the component area is obtained. If the motion consistency is poor, it means that the influencing object and the obstacles in the corresponding reference area are moving in a chaotic manner, making obstacle avoidance more difficult and posing a greater threat to the component area.

[0015] Optionally, the step of determining the reference area of ​​the influencing object includes: obtaining the centroid of the obstacle, constructing a circle with a radius of The circular area is used as the reference area of ​​the influencing object.

[0016] Optionally, the horizontal component velocities of the component area, the influencing object, and the obstacles in the reference area corresponding to the influencing object constitute a horizontal velocity sequence; and the standard deviation of the horizontal velocity sequence is used as the horizontal velocity difference;

[0017] The vertical component velocities of the component area, the influencing object, and the obstacles in the reference area corresponding to the influencing object constitute a vertical velocity sequence; the standard deviation of the vertical velocity sequence is taken as the vertical velocity difference;

[0018] The motion directions of the component area, the influencing object, and the obstacles in the influencing object reference area are obtained. The angles between the velocity direction corresponding to the component area and the influencing object and the obstacles are obtained to form an angle sequence. The standard deviation of the angle sequence is used as the velocity direction difference.

[0019] To facilitate comparison of the speed differences between the component area, the impact object, and the obstacle in the reference area, the speed was decomposed and compared based on the differences in the horizontal and vertical speed components. The standard deviation indicates the fluctuation of the series, thus reflecting the differences in the horizontal and vertical speed components and the speed direction.

[0020] Optionally, a range dynamic adjustment coefficient is obtained; the influence range of the influencing object is adjusted based on the range dynamic adjustment coefficient, and the repulsion function in the artificial potential field method is optimized; and the initial repulsion of each obstacle is obtained based on the repulsion function.

[0021] In the artificial potential field method, obstacles have a predetermined range of influence, which is preset by the operator. However, different obstacles have different motion patterns. To reduce the risk of collision between faster-moving obstacles and the component area, the preset range of influence should be adjusted to improve obstacle avoidance accuracy.

[0022] Optionally, the minimum distance from the component area to the motion path of the influencing object and the minimum time to the motion path of the influencing object are obtained; the product of the minimum distance and the minimum time is used as the first adjustment value; the relative speed of the component area and the influencing object is used as the second adjustment value, and the normalized result of the ratio of the second adjustment value to the first adjustment value is used as the range dynamic adjustment coefficient.

[0023] The dynamic adjustment coefficient changes with the relative speed between the influencing object and the component area. For influencing objects with a high relative speed, the influence range should be expanded so that the influencing object can act on the component area in advance, allowing the component area to react in advance and further reducing the risk of collision.

[0024] Optionally, the step of adjusting the influence range of the influencer based on the range dynamic adjustment coefficient includes: for each influencer, taking the product of the preset initial range of the influencer and the dynamic influence coefficient as the adjustment range, and taking the sum of the initial range and the adjustment range as the influence range of the influencer.

[0025] Optionally, the step of obtaining the comprehensive repulsive force of each component area based on multiple local optimal repulsive forces includes: for each component area, obtaining the resultant force direction of the initial repulsive forces of all influencing objects corresponding to the component area; for each influencing object, taking the angle between the initial repulsive force direction of the influencing object and the resultant force direction as the direction angle, taking the product of the optimal repulsive force and the cosine value of the direction angle as the local repulsive force, and taking the sum of multiple local repulsive forces as the comprehensive repulsive force.

[0026] In a second aspect, the present application provides an automatic obstacle avoidance system based on the Internet of Things, which adopts the following technical solutions:

[0027] An automatic obstacle avoidance system based on the Internet of Things includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the automatic obstacle avoidance method based on the Internet of Things is implemented.

[0028] The above-mentioned automatic obstacle avoidance method based on the Internet of Things is generated into a computer program and stored in a memory so as to be loaded and executed by a processor. Thus, a system is made based on the memory and the processor for easy use.

[0029] This application has the following technical effects:

[0030] Divide the skid-mounted equipment into multiple component areas, obtain comprehensive repulsion based on the multiple component areas, and perform obstacle avoidance based on the comprehensive repulsion of the multiple component areas, thereby improving the accuracy and timeliness of obstacle avoidance and reducing the risk of collision of the skid-mounted equipment during movement. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a method flow chart of an automatic obstacle avoidance method based on the Internet of Things in an embodiment of the present application.

[0032] Figure 2 This is a method flow chart of step S2 in an automatic obstacle avoidance method based on the Internet of Things in an embodiment of the present application. DETAILED DESCRIPTION

[0033] The embodiment of the present application discloses an automatic obstacle avoidance method based on the Internet of Things, which divides irregular skid-mounted equipment into multiple component areas; based on the distribution of influencing objects corresponding to the component area and the motion state of the influencing objects, the threat level of each influencing object to the component area is determined; based on the threat level of each influencing object to the component area, the repulsion adjustment weight is obtained and the initial repulsion is adjusted to obtain the local optimal repulsion. The repulsions of multiple influencing objects on the component area are combined to obtain the total repulsion received by the component area. In this method, for influencing objects with a greater threat level, the optimal local repulsion ultimately obtained is greater, so that in the process of obstacle avoidance, obstacles can be avoided in advance for such influencing objects, and the influence range of such influencing objects is larger, that is, the safe distance between the component area and the influencing objects is maintained greater during the obstacle avoidance process, further reducing the risk of ineffective obstacle avoidance or untimely obstacle avoidance.

[0034] Reference Figure 1 , an automatic obstacle avoidance method based on the Internet of Things includes steps S1 to S5.

[0035] S1: Divide the equipment into areas to form multiple component areas.

[0036] Because skid-mounted equipment typically moves horizontally, the division is performed here based on the projection of the skid-mounted equipment on a horizontal plane. During the division process, each component of the skid-mounted equipment is considered a component region. Of course, in other embodiments, even division can be performed based on the projection of the skid-mounted equipment on a horizontal plane to form multiple component regions.

[0037] S2: For each component area, obstacles that have an impact on the component area are determined as influencing objects, and the threat level of the influencing objects to the component area is calculated.

[0038] The step of calculating the threat level of the influencing object to the component area includes: obtaining the local density of the influencing object based on the distribution of the influencing object; obtaining the motion consistency between the influencing object and the component area based on the motion state of the influencing object; and using the ratio of the local density of the influencing object to the motion consistency as the threat level of the influencing object to the component area.

[0039] Each component area on the skid-mounted equipment is equipped with UWB (Ultra Wide Band) and IMU (Inertial Measurement Unit). The UWB determines the centroid of each component area, while the IMU measures its linear velocity. Radar is also installed in each component area to detect the position and velocity of obstacles within its range of motion.

[0040] For the more complex movement scenarios of skid-mounted equipment, the movement scenarios usually include dynamic and static obstacles. In the artificial potential field method, there is a preset influence range for obstacles in the movement of skid-mounted equipment. The influence range is a preset initial range, which means that when the component area falls within the initial range, the obstacle has a repulsive effect on the component area. In one area, the component area may fall within the initial range of multiple obstacles. For the sake of ease of description, this type of obstacle is defined as an influence object in this application. When a component area is in a certain position, it may fall within the initial range of multiple influence objects at the same time, so one component area corresponds to multiple influence objects. For influence objects, if the area where an influence object is located has a denser distribution of obstacles, then the risk of collision in that area will be greater. Similarly, if the movement of an obstacle is more chaotic, the risk of collision with the skid-mounted equipment will also be greater.

[0041] Reference Figure 2 , step S2 includes steps S21 to S23.

[0042] S21: Obtain the local density of the influencing object according to the distribution of the influencing object.

[0043] Determine the reference area of ​​the influencing object, obtain the average of the Euclidean distances between the influencing object and each obstacle in the corresponding reference area as the distribution distance, and determine the local density of the influencing object based on the distribution distance. The local density of the influencing object is inversely proportional to the distribution distance.

[0044] With the centroid of the influencing object as the center of the circle, the radius is The circular area is defined as the reference area. If the distance between the influencing object and other obstacles in the reference area is smaller, it means that the density of obstacles in the reference area is greater, which in turn means that the local density of the area where the influencing object is located is greater.

[0045] Specifically, the calculation formula of local density can be expressed as: Where, Influence The local density of Indicates the The influencing factor and the reference area The Euclidean distance between obstacles (obstacles include influencing objects. When analyzing the local density of an influencing object, there may be other influencing objects in its reference area. Here, other influencing objects are still considered obstacles); Indicates the number of obstacles in the reference area; Expressed as a natural constant An exponential function with base .

[0046] The formula calculates the Euclidean distance between each obstacle and the influencing object in the reference area and sums the Euclidean distances. A smaller sum of the Euclidean distances indicates a smaller separation between the obstacle and the influencing object, which in turn indicates a higher local density of influencing objects.

[0047] S22: Obtaining the motion consistency between the influencing object and the component region according to the motion state of the influencing object.

[0048] Obstacles include both moving and stationary obstacles. For stationary obstacles, their state is relatively simple, and the device can avoid them easily during movement. However, the difficulty of avoiding moving obstacles is affected by their own speed and direction of movement, so these obstacles should be differentiated.

[0049] When the influencing object and the obstacles in the influencing object area are both in a stationary state: the motion consistency between the influencing object and the component area is 1. Here, the stationary state refers to the state where the motion speed of the influencing object is 0.

[0050] When an obstacle exists in the influencing object or the reference area corresponding to the influencing object and is in motion: the horizontal velocity difference, vertical velocity difference, and velocity direction difference between the influencing object and the component area are obtained, and the product of the horizontal velocity difference, vertical velocity difference, and velocity direction difference is used as the total difference to calculate the motion consistency.

[0051] The steps to obtain the horizontal velocity difference between the influencing object and the component area include:

[0052] Construct a horizontal velocity sequence: Build a plane coordinate system and decompose the motion speed of the influencing object, the component area, and the obstacles in the reference area corresponding to the influencing object (in this embodiment, the motion speed refers to the motion speed of the centroid of the obstacle or component area). The horizontal velocities corresponding to the influencing object, the component area, and the obstacles in the reference area corresponding to the influencing object constitute a horizontal velocity sequence, and the standard deviation of the horizontal velocity is used as the horizontal velocity difference.

[0053] Similar to the acquisition of horizontal velocity difference, the vertical velocity difference between the influencing object and the component area is obtained.

[0054] The motion directions of the component area, the influencing object, and the obstacles in the influencing object reference area are obtained. The angles between the velocity direction corresponding to the component area and the influencing object and the obstacles are obtained to form an angle sequence. The standard deviation of the angle sequence is used as the velocity direction difference.

[0055] Then the motion consistency is obtained based on the horizontal velocity difference, vertical velocity difference and velocity direction difference.

[0056] Specifically, the calculation formula of motion consistency can be expressed as:

[0057] Where, Indicates the The motion consistency between the influence object and the corresponding component area; represents the horizontal velocity difference; represents the vertical velocity difference; Expressed as a natural constant An exponential function with base ; Indicates the difference in velocity direction.

[0058] By decomposing the velocities of the influencing object and the component region, we can compare the speed differences between the influencing object and the component region. The speed and angle differences between the influencing object and the component region reflect the motion consistency of the component region and the influencing object. The greater the speed and angle differences between the component region and the influencing object, the greater the difference in their operating states, indicating poorer motion consistency.

[0059] S23: The ratio of the local density of the influencing object to the motion consistency is used as the threat level of the influencing object to the component area.

[0060] The threat level of an influencing object to a component area is calculated based on the local density of the influencing object and its consistency with the movement of the component area. If the local density of an influencing object is greater, it means that there are more obstacles in the vicinity of the influencing object. In this case, the risk of collision between the influencing object and the component area during the movement of the component area is higher, which means that the threat level of the influencing object to the component area is greater. The motion consistency of the influencing object indicates the difference between the movement of obstacles in the reference area corresponding to the influencing object and the component area. If the motion consistency of the influencing object is poor, it means that the movement of obstacles in the reference area corresponding to the influencing object is chaotic. In this case, the risk of collision between the component area and the influencing object and the obstacles in the reference area corresponding to the influencing object during the movement is higher, which means that the threat level of the influencing object to the component area is greater. The two work together to improve the accuracy and robustness of the threat level calculation of the influencing object to the component area.

[0061] S3: For each component area, obtain a repulsion adjustment weight based on the threat level of each influencing object to the component area.

[0062] When skid-mounted equipment moves to a certain area, each component area is affected by multiple influencing factors. Different influencing factors pose different threat levels to the component area. For the adjustment weight corresponding to each influencing factor, the sum of the threat levels of all influencing factors to the component area is obtained. This sum of the threat levels is used as the total threat level. The ratio of the threat level corresponding to each influencing factor to the total threat level is used as the adjustment level. The sum of the adjustment level and a preset base value is used as the adjustment weight for the influencing factor. In this embodiment, the base value is 1.

[0063] S4: Obtain the initial repulsion of the influencing object, take the product of the repulsion adjustment weight and the initial repulsion of the influencing object as the local optimal repulsion, and obtain the comprehensive repulsion of each component area based on multiple local optimal repulsions.

[0064] Get the range dynamic adjustment coefficient:

[0065] The minimum distance between the component area and the influencing object's motion path and the minimum time to reach the influencing object's motion path are obtained; the product of the minimum distance and the minimum time is used as the first adjustment value; the relative speed between the component area and the influencing object's motion is used as the second adjustment value, and the normalized result of the ratio of the second adjustment value to the first adjustment value is used as the range dynamic adjustment coefficient.

[0066] Specifically, the calculation formula of the range dynamic adjustment coefficient can be expressed as: Where, Influence Range dynamic adjustment coefficient; Influence Relative velocity to the component area; Parts area reaches the impact Minimum time of motion path; Parts area reaches the impact The minimum distance of the motion path, where the minimum distance can be understood as the distance from the component area to the perpendicular line of the motion trajectory of the influencing object; Represents the time of a preset distance, mainly used to eliminate the dimension of the final range dynamic adjustment coefficient and normalize the final calculation result.

[0067] The influence range of the influencing object is adjusted based on the range dynamic adjustment coefficient, and the repulsion function in the artificial potential field method is optimized; the initial repulsion of each obstacle is obtained based on the repulsion function.

[0068] For each influencing factor, the product of the preset initial range of the influencing factor and the dynamic influence coefficient is used as the adjustment range, and the sum of the initial range and the adjustment range is used as the influence range of the influencing factor.

[0069] represents the first adjustment value, Represents the second adjustment value. The two adjustment values ​​adjust the initial range of the influence of the preset influencing object on the component area. By adjusting the preset initial range through the first adjustment value and the second adjustment value, the effect of adjusting the repulsive force of the influencing object on the component area can be achieved. When the first adjustment value is small, it means that the minimum distance and the minimum time are small, which means that the risk of collision between the corresponding influencing object and the component area is greater, thereby expanding the initial range, so that the influencing object has an impact on the component area in advance, thereby improving the timeliness of obstacle avoidance. Similarly, if the relative speed between the influencing object and the component area is greater, it means that the two are approaching at a faster speed, the risk of collision is higher, and the impact expands the initial range.

[0070] The repulsion function in the artificial potential field method is optimized, and the influence range of the optimized influencing object is substituted into the repulsion function in the artificial potential field method.

[0071] The formula of the optimized repulsion function can be expressed as: Where, Influence repulsive forces on the component area; is a positive proportional coefficient; Influence Euclidean distance from the centroid of the component area; Indicates the initial range; Influence Dynamic adjustment coefficient of the range.

[0072] The influence range after the initial range is optimized is applied to the repulsion function to optimize the repulsion function. The repulsion function is a conventional technical means in this field and will not be described in detail here.

[0073] For each influencing object in the component area, the Euclidean distance between the influencing object and the component area and the corresponding influence range are substituted into the repulsion function, thereby obtaining the initial repulsion.

[0074] The product of the repulsion adjustment weight and the initial repulsion of the influencing object is used as the local optimal repulsion, and the comprehensive repulsion of each component area is obtained based on multiple local optimal repulsions;

[0075] Where, It represents the comprehensive repulsive force on the component area; Influence The degree of threat to the component area; Indicates the number of influencers corresponding to the component area; Influence Initial repulsive force on the component area; Influence The angle between the direction of the initial repulsive force and the direction of the resultant of the initial repulsive forces of multiple influencing objects; Indicates the base value, which is 1 in this embodiment.

[0076] S5: Automatic obstacle avoidance is performed using the artificial potential energy method based on the combined repulsive force and gravitational field acting on each component area.

[0077] Obstacle avoidance is achieved for each component area using an artificial potential energy method based on the combined repulsive and attractive forces acting on each area. This method, a conventional technique in the field and not detailed here, constructs a motion path for each component area based on the attractive and repulsive forces acting on the component area. By controlling the translation and torsion of the skid-mounted equipment, the position of multiple component areas can be adjusted independently, improving the timeliness of obstacle avoidance and reducing the risk of collision.

[0078] An embodiment of the present application also discloses an automatic obstacle avoidance system based on the Internet of Things, including a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, an automatic obstacle avoidance method based on the Internet of Things according to the present application is implemented.

[0079] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0080] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the results, shapes, and principles of the present application should be included in the scope of protection of the present application.

Claims

1. An automatic obstacle avoidance method based on the Internet of Things, characterized in that: The method comprises the following steps: dividing the equipment into regions to form a plurality of component regions; for each component region, determining an obstacle that has an impact on the component region as an influencing object, and calculating the threat degree of the influencing object to the component region; For each component area, a repulsion adjustment weight is obtained based on the threat level of each influencing object to the component area; the initial repulsion of the influencing object is obtained, and the product of the repulsion adjustment weight and the initial repulsion of the influencing object is used as the local optimal repulsion. Based on multiple local optimal repulsions, the comprehensive repulsion of each component area is obtained; based on the comprehensive repulsion and gravitational field of each component area, the artificial potential energy method is used to automatically avoid obstacles; The step of calculating the threat level of the influencing object to the component area includes: obtaining the local density of the influencing object based on the distribution of the influencing object; obtaining the motion consistency between the influencing object and the component area based on the motion state of the influencing object; and using the ratio of the local density of the influencing object to the motion consistency as the threat level of the influencing object to the component area; The motion consistency of the influencing object indicates the difference in motion between the obstacles in the reference area corresponding to the influencing object and the component area. If the motion consistency of the influencing object is poor, it means that the obstacles in the reference area corresponding to the influencing object move chaotically. Then, the component area has a higher risk of colliding with the influencing object and the obstacles in the reference area corresponding to the influencing object during movement, and thus the threat degree of the influencing object to the component area is greater.

2. The automatic obstacle avoidance method based on the Internet of Things according to claim 1, characterized in that: The step of obtaining the local density of the influencing object includes: determining a reference area of ​​the influencing object, obtaining the average of the Euclidean distances between the influencing object and each obstacle in the corresponding reference area as the distribution distance, and determining the local density of the influencing object based on the distribution distance, wherein the local density of the influencing object is inversely proportional to the distribution distance.

3. The automatic obstacle avoidance method based on the Internet of Things according to claim 1, characterized in that: The steps to obtain the kinematic consistency of the influencing object and the component region include: When the influencing object and the obstacles in the influencing object area are both stationary: the motion consistency between the influencing object and the component area is 1; When an obstacle exists in the influencing object or the reference area corresponding to the influencing object and is in motion, the horizontal velocity difference, vertical velocity difference, and velocity direction difference between the influencing object and the component area are obtained, and the product of the horizontal velocity difference, vertical velocity difference, and velocity direction difference is used as the total difference to calculate the motion consistency.

4. The automatic obstacle avoidance method based on the Internet of Things according to claim 2, characterized in that: The steps of determining the reference area of ​​the influencing object include: obtaining the centroid of the obstacle, constructing a circle with a radius of The circular area is used as the reference area of ​​the influencing object.

5. The automatic obstacle avoidance method based on the Internet of Things according to claim 3, characterized in that: The horizontal component velocities of the component area, the influencing object, and the obstacles in the reference area corresponding to the influencing object constitute a horizontal velocity sequence; the standard deviation of the horizontal velocity sequence is taken as the horizontal velocity difference; The vertical component velocities of the component area, the influencing object, and the obstacles in the reference area corresponding to the influencing object constitute a vertical velocity sequence; the standard deviation of the vertical velocity sequence is taken as the vertical velocity difference; The motion directions of the component area, the influencing object, and the obstacles in the influencing object reference area are obtained. The angles between the velocity direction corresponding to the component area and the influencing object and the obstacles are obtained to form an angle sequence. The standard deviation of the angle sequence is used as the velocity direction difference.

6. The automatic obstacle avoidance method based on the Internet of Things according to claim 1, characterized in that: The steps of obtaining the initial repulsion of the influencing object include: obtaining a range dynamic adjustment coefficient; adjusting the influence range of the influencing object based on the range dynamic adjustment coefficient, optimizing the repulsion function in the artificial potential field method; and obtaining the initial repulsion of each obstacle based on the repulsion function.

7. The automatic obstacle avoidance method based on the Internet of Things according to claim 6, characterized in that: The steps of obtaining the range dynamic adjustment coefficient include: obtaining the minimum distance from the component area to the motion path of the influencing object and the minimum time to reach the motion path of the influencing object; taking the product of the minimum distance and the minimum time as the first adjustment value; taking the relative speed of the component area and the influencing object as the second adjustment value, and taking the normalized result of the ratio of the second adjustment value to the first adjustment value as the range dynamic adjustment coefficient.

8. The automatic obstacle avoidance method based on the Internet of Things according to claim 7, characterized in that: The step of adjusting the influence range of the influencer based on the range dynamic adjustment coefficient includes: for each influencer, taking the product of the preset initial range of the influencer and the dynamic influence coefficient as the adjustment range, and taking the sum of the initial range and the adjustment range as the influence range of the influencer.

9. The automatic obstacle avoidance method based on the Internet of Things according to claim 8, characterized in that: The steps of obtaining the comprehensive repulsive force of each component area based on multiple local optimal repulsive forces include: for each component area, obtaining the resultant force direction of the initial repulsive forces of all influencing objects corresponding to the component area; for each influencing object, taking the angle between the initial repulsive force direction of the influencing object and the resultant force direction as the direction angle, taking the product of the optimal repulsive force and the cosine value of the direction angle as the local repulsive force, and taking the sum of multiple local repulsive forces as the comprehensive repulsive force.

10. An automatic obstacle avoidance system based on the Internet of Things, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an automatic obstacle avoidance method based on the Internet of Things according to any one of claims 1 to 9 is implemented.

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