Vision-based coal wharf operation point tracking method and system

Abstract

The invention provides a coal wharf operation point tracking method and system based on vision. The method comprises the following steps: S1, shooting a first monitoring image of a wharf through a distributed camera, automatically identifying a wharf operation point according to the monitoring image, and determining a first image coordinate of the wharf operation point; s2, aligning the first image coordinate of the wharf operation point with a map coordinate of a pre-constructed map to obtain a corresponding world coordinate, and completing positioning of the wharf operation point; s3, discrete point difference calculation is carried out through the pre-constructed map, density calculation of wharf operation points is achieved, and global operation point tracking is formed. According to the invention, automatic identification and global tracking of the wharf operation point are realized, and compared with a field personnel talkback broadcasting and manual scheduling monitoring mode, the efficiency is higher.

Description

Technical Field

[0001] The present invention relates to the field of image recognition technology, and in particular to a vision-based coal terminal operation point tracking method and system. Background Art

[0002] The management of work sites is a crucial component of terminal management. Understanding the status of on-site work sites is a critical aspect of production operations and requires real-time monitoring. Common terminal management models rely on on-site managers actively reporting via walkie-talkies and back-end dispatchers monitoring the site through monitoring equipment.

[0003] In actual management, on-site operations are widely distributed, making it difficult to pinpoint work times and locations. On-site managers lack intuitive reporting capabilities through intercoms, and monitoring calls often lack a comprehensive overview due to delayed responses, which can easily lead to blind spots in monitoring.

[0004] Therefore, how to automatically achieve global tracking of coal terminal operation points is a problem that needs to be solved. Summary of the Invention

[0005] In order to at least solve the technical problems existing in the above-mentioned background technology, the present invention provides a vision-based coal terminal operation point tracking method, system, electronic device, computer storage medium and computer program product.

[0006] The present invention provides a vision-based coal terminal operation point tracking method, comprising the following steps: S1. Capturing a first monitoring image of the terminal using a distributed camera, automatically identifying a terminal operation point based on the monitoring image, and determining its first image coordinates; S2. Aligning the first image coordinates of the terminal operation point with the map coordinates of the pre-built map to obtain corresponding world coordinates, thereby completing the positioning of the terminal operation point; S3. Calculate the difference between discrete points using the pre-built map to calculate the density of the terminal operation points and form global operation point tracking.

[0007] Furthermore, step S1 specifically includes: Acquire video data through code streams of distributed surveillance cameras, calculate a frame extraction interval and a number of frames according to terminal operation conditions, perform frame extraction analysis on the video data based on the frame extraction interval and the number of frames, and obtain the surveillance image; Feature points are extracted from the monitoring image using a visual analysis algorithm, the terminal operation point is detected based on the feature points, and its first image coordinates are determined.

[0008] Furthermore, the characteristic points include at least one of mobile forklift operation characteristics, personnel gathering characteristics, port machinery equipment operation characteristics, and berth ship docking characteristics.

[0009] Furthermore, step S2 specifically includes: Get the map coordinate system of the pre-built map, assuming it is: M=(x, y), and convert it into vector form: ; Initially mark the second image coordinates in the camera and the map coordinates to obtain a preliminary conversion relationship , where H is the preliminary transformation matrix; Through map measurement, the initial transformation relationship is continuously corrected to obtain the target transformation matrix , to achieve alignment of the second image coordinates with the map coordinates; According to the target transformation matrix The first image coordinates of the dock operation point are converted into world coordinates to complete the positioning of the dock operation point.

[0010] Furthermore, step S3 specifically includes: Extracting a second monitoring image of the dock operation point from the first monitoring image, extracting regional surface information from the second monitoring image, and converting the regional surface information to a corresponding position in the pre-built map; The continuous surface curves and mathematical models in the converted pre-built map are spatially segmented, including TIN to raster, raster to TIN, or vector polygon to raster, to obtain raster basic data and vector basic data; According to the terminal terrain, corresponding sampling methods are used to obtain sampling data of different terrain plane points, that is, unevenly distributed spatial surface data under different scenarios; wherein, the sampling methods include regular sampling, random sampling, cross-section sampling, and clustered sampling, corresponding to the ground, yard, berth, and work area respectively; Considering the error term, the spatial surface data of the dock operation point is converted to Perform surface conversion to obtain surface conversion results; for random samples of operating points, , add the error term , ,in, is the coefficient, i=1,2,3…n; Combining the spatial surface data with the result of the curved surface conversion, using difference calculation to interpolate the discrete working point data into a continuous data surface; Combined with map construction, operation point tracking is formed within the entire terminal area.

[0011] Furthermore, the difference calculation adopts linear interpolation or Kriging interpolation algorithm.

[0012] The present invention also provides a vision-based coal terminal operation point tracking system, comprising a distributed camera, a processing module, and a storage module; the processing module is connected to the distributed camera and the storage module; The storage module is used to store executable computer program code; The distributed camera is used to capture surveillance images of the terminal and transmit them to the processing module; The processing module is configured to execute the method as described in any of the preceding items by calling the executable computer program code in the storage module.

[0013] The present invention also provides an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calling the executable program code stored in the memory to execute any of the methods described above.

[0014] The present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, any of the above methods is executed.

[0015] The present invention further provides a computer program product, comprising a computer program stored on a non-transitory computer-readable medium, wherein the computer program is executed by a processor to execute any of the above methods.

[0016] The beneficial effects of the present invention are at least: The present invention realizes automatic identification and global tracking of dock operation points, which is more efficient than on-site personnel intercom broadcasting and manual scheduling and monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 The present invention is a flowchart of a method for tracking coal terminal operation points based on vision disclosed in an embodiment of the present invention.

[0019] Figure 2 It is a structural schematic diagram of a vision-based coal terminal operation point tracking system disclosed in an embodiment of the present invention.

[0020] Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions, and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings. It is apparent that the embodiments described are only some, not all, of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0022] The terms used in the examples of this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms "a," "the," and "the" used in the examples of this application and the appended claims are also intended to include plural forms, and unless the context clearly indicates otherwise, "a plurality" generally includes at least two.

[0023] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0024] It should be understood that although the terms "first," "second," "third," etc. may be used in the embodiments of this application to describe "...," these "..." should not be limited to these terms. These terms are merely used to distinguish "...." For example, "first..." could also be referred to as "second...", and similarly, "second..." could also be referred to as "first..." without departing from the scope of the embodiments of this application.

[0025] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0026] It should also be noted that the terms "include," "comprises," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a product or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such product or system. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the product or system comprising the element.

[0027] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0028] like Figure 1 As shown, the embodiment of the present invention discloses a vision-based coal terminal operation point tracking method, comprising the following steps: S1. Capturing a first monitoring image of the terminal using a distributed camera, automatically identifying a terminal operation point based on the monitoring image, and determining its first image coordinates; In this step, multiple cameras are installed at different locations inside the coal terminal, and the terminal operation points are automatically identified through the monitoring images captured by the cameras.

[0029] S2. Aligning the first image coordinates of the terminal operation point with the map coordinates of the pre-built map to obtain corresponding world coordinates, thereby completing the positioning of the terminal operation point; In this step, by pre-building the map and initial labeling, we establish a transformation relationship between image coordinates and map coordinates. By continuously revising the labeling relationship, we can obtain a more accurate transformation matrix. Ultimately, using the image coordinates of the dock work point and the aforementioned transformation matrix, we can calculate the world coordinates of the work point, achieving precise positioning of the dock work point.

[0030] S3. Calculate the difference between discrete points using the pre-built map to calculate the density of the terminal operation points and form global operation point tracking.

[0031] In this step, the density calculation of the operation points is one of the important links in tracking the operation points. The present invention uses the discrete point difference calculation method to realize the density calculation of the terminal operation points, thereby realizing the global tracking of the terminal operation points.

[0032] Furthermore, step S1 specifically includes: Acquire video data through code streams of distributed surveillance cameras, calculate a frame extraction interval and a number of frames according to terminal operation conditions, perform frame extraction analysis on the video data based on the frame extraction interval and the number of frames, and obtain the surveillance image; Feature points are extracted from the monitoring image using a visual analysis algorithm, the terminal operation point is detected based on the feature points, and its first image coordinates are determined.

[0033] In this embodiment, the relative position of the dock operation point is obtained by acquiring the code stream of the monitoring camera, performing frame analysis, target detection, and pixel point extraction. The main steps are: 1) Obtain camera parameters, increase shutter speed, reduce P-frame interval, and adjust tone mapping; 2) Use ONVIF protocol to obtain H264 and H265 encapsulated RTSP streams in real time and pre-cache the streams; 3) Calculate the frame extraction interval and number of frames in real time based on terminal operations. Set the current frame extraction time (MT) based on field measurements. The maximum number of frames extracted within 1 second is n = 1 / MT. When n > 10, set the capture interval (i.e., frame extraction interval) to 300ms. The corresponding actual number of captures (i.e., number of frames extracted) is: n1 = (T / (MT + 300)). When n < 10, set the capture interval (i.e., frame extraction interval) to 200ms. The corresponding actual number of captures (i.e., number of frames extracted) is: n1 = (T / (MT + 200)).

[0034] 4) Target extraction: Through the visual analysis algorithm, the dock operation point is framed and the pixel coordinates of the dock operation point based on the camera are obtained. Assuming that the distance from the optical center to the principal point c on the image plane is equal to the focal length f (dynamic acquisition), the target from the operation point , is mapped to the plane point , ignoring the depth, the image coordinates of the dock operation point are obtained.

[0035] Furthermore, the characteristic points include at least one of mobile forklift operation characteristics, personnel gathering characteristics, port machinery equipment operation characteristics, and berth ship docking characteristics.

[0036] In this embodiment, when an area on the dock becomes a dock operation point, the area generally has features such as mobile forklift operations, personnel gathering, port machinery equipment operations, and berthed ships. By analyzing whether at least one of the above features exists in the monitoring image, it can be determined whether the area is a dock operation point.

[0037] Furthermore, step S2 specifically includes: Get the map coordinate system of the pre-built map, assuming it is: M=(x, y), and convert it into vector form: ; Initially mark the second image coordinates in the camera and the map coordinates to obtain a preliminary conversion relationship , where H is the preliminary transformation matrix; Through map measurement, the initial transformation relationship is continuously corrected to obtain the target transformation matrix , to achieve alignment of the second image coordinates with the map coordinates; According to the target transformation matrix The first image coordinates of the dock operation point are converted into world coordinates to complete the positioning of the dock operation point.

[0038] In this embodiment, the surveillance image captured by the camera has multiple marker points deployed. The world coordinates of these marker points are known. By matching the image coordinates in the surveillance image with the world coordinates of the marker points, the above-mentioned preliminary transformation matrix H can be obtained. At the same time, the above-mentioned preliminary transformation relationship can be continuously corrected through map measurement. , n is the number of corrections, Finally, the target transformation matrix can be used to transform the first image coordinates of the dock operation point into world coordinates to complete the positioning of the dock operation point.

[0039] Furthermore, step S3 specifically includes: A second monitoring image of the dock operation point is extracted from the first monitoring image, regional surface information is extracted from the second monitoring image, and the regional surface information is converted to a corresponding position in the pre-constructed map.

[0040] The continuous surface curves and mathematical models in the converted pre-built map are spatially segmented, including TIN to raster, raster to TIN or vector polygon to raster, to obtain raster basic data and vector basic data.

[0041] 3D oblique photography is used to construct maps, requiring the processing of continuous surface curves and data models from pre-constructed maps. This is accomplished through specific spatial segmentation methods, such as converting triangulated irregular networks (TINs) to raster data, converting raster back to TIN, or converting vector polygons to raster. This is done to calculate accurate 3D surfaces, thereby generating the raster and vector data required for subsequent GIS (Geographic Information System) applications. Raster data is suitable for spatial analysis, while vector data facilitates precise mapping and topological analysis.

[0042] According to the terminal terrain, corresponding sampling methods are used to obtain sampling data of different terrain plane points, that is, unevenly distributed spatial surface data under different scenarios; wherein, the sampling methods include regular sampling, random sampling, cross-section sampling, and clustered sampling, corresponding to the ground, yard, berth, and work area respectively; The terminal is composed of diverse terrain regions, and different sampling methods are employed for the ground, storage yards, berths, and work areas. Regular sampling, applied to the ground, ensures uniform data collection; random sampling, used in the storage yard, accommodates the relatively irregular material stacking conditions; cross-sectional sampling, targeting berths, reflects their linear spatial characteristics; and clustered sampling, serving work areas, where equipment and facilities often cluster. Due to the varying characteristics of spatial surface data in each region, appropriate sampling methods are required to minimize interference with the accuracy of subsequent spatial interpolation and obtain initial results that align with the actual spatial locations of each region—that is, spatial surface data with uneven density under different scenarios.

[0043] Considering the error term, the spatial surface data of the dock operation point is converted to Perform surface conversion to obtain surface conversion results; for random samples of operating points, , add the error term , ,in, is the coefficient, i=1,2,3…n; Taking into account the errors in actual operation, the operation points Perform surface conversion to obtain the surface conversion result. This is to make the work point data more consistent with the actual terrain and work conditions, and compensate for deviations introduced by measurement, environmental factors, and other factors.

[0044] The spatial surface data and the result of the curved surface conversion are combined and difference calculation is used to interpolate the discrete working point data into a continuous data surface.

[0045] Combining the initial spatial position results obtained previously with the surface conversion results, we use interpolation to transform the scattered work point data into a continuous data surface. Interpolation can fill in data gaps and infer missing data based on existing work points, allowing the data to transition from a discrete state to a continuous state, which is more conducive to subsequent analysis.

[0046] Combined with map construction, operation point tracking is formed within the entire terminal area.

[0047] Finally, by integrating the entire map-building process, a system for tracking work points across the entire terminal area was established. With continuous work point data and a complete map, the dynamics of each terminal work point can be monitored in real time, enabling efficient management. For example, tracking cargo handling routes and equipment operating locations ensures smooth terminal operations.

[0048] In this embodiment, operating point density calculation based on the interpolation of discrete points is a density calculation method used for operating point tracking in this invention, used to display high-frequency operating points. This method uses interpolation of discrete points to create a continuous density surface, enabling a better understanding of the spatial distribution trends of operating point data, thereby enabling tracking and overview of coal terminal operating points.

[0049] Furthermore, the difference calculation adopts linear interpolation or Kriging interpolation algorithm.

[0050] like Figure 2 As shown, the embodiment of the present invention discloses a vision-based coal terminal operation point tracking system, including a distributed camera, a processing module, and a storage module; the processing module is connected to the distributed camera and the storage module; The storage module is used to store executable computer program code; The distributed camera is used to capture surveillance images of the terminal and transmit them to the processing module; The processing module is configured to execute the method as described in any of the preceding items by calling the executable computer program code in the storage module.

[0051] The specific functions of a vision-based coal terminal operation point tracking system in this embodiment refer to the above embodiments. Since the system of this embodiment adopts all the technical solutions of the above embodiments, it at least has all the beneficial effects brought by the technical solutions of the above embodiments, which will not be described one by one here.

[0052] like Figure 3 As shown, an embodiment of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the method as described in the above embodiment.

[0053] An embodiment of the present invention further discloses a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in the above embodiment is executed.

[0054] An embodiment of the present invention further discloses a computer program product, including a computer program stored on a non-transitory computer-readable medium, wherein the computer program is executed by a processor to perform the method described in the above embodiment.

[0055] The device / system according to an embodiment of the present disclosure may include a processor, a memory for storing program data and executing the program data, a permanent memory such as a disk drive, a communication port for processing communication with an external device, and a user interface device, etc. The method is implemented as a software module or can be stored on a computer-readable recording medium as a computer-readable code or program command that can be executed by a processor. The example of a computer-readable recording medium may include a magnetic storage medium (e.g., a read-only memory (ROM), a random access memory (RAM), a floppy disk, a hard disk, etc.), an optical reading medium (e.g., a CD-ROM, a digital versatile disk (DVD), etc.), etc. The computer-readable recording medium can be distributed in a computer system connected in a network, and the computer-readable code can be stored and executed in a distributed manner. The medium can be computer-readable, stored in a memory and executed by a processor.

[0056] The embodiments of the present disclosure may be indicated as functional block components and various processing operations. Functional blocks may be implemented as various numbers of hardware and / or software components that perform specific functions. For example, the embodiments of the present disclosure may implement direct circuit components that can perform various functions under the control of one or more microprocessors or other control devices, such as memory, processing circuits, logic circuits, lookup tables, etc. The components of the present disclosure may be implemented through software programming or software components. Similarly, the embodiments of the present disclosure may include various algorithms implemented by a combination of data structures, processes, routines, or other programming components, and may be implemented by programming or scripting languages (such as C, C++, Java, assembler, etc.). Functional aspects may be implemented by algorithms executed by one or more processors. In addition, the embodiments of the present disclosure may implement related technologies for electronic environment settings, signal processing, and / or data processing. Terms such as "mechanism," "element," "unit," etc. may be used broadly and are not limited to mechanical and physical components. These terms may represent a series of software routines associated with a processor, etc.

[0057] Specific embodiments are described in this disclosure as examples, and the scope of the embodiments is not limited thereto.

[0058] Although the embodiments of the present disclosure have been described, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the present disclosure as defined by the appended claims. Therefore, the above-described embodiments of the present disclosure should be interpreted as examples and do not limit the embodiments in all respects. For example, each component described as a single unit may be executed in a distributed manner, and similarly, components described as distributed may be executed in a combined manner.

[0059] All examples or exemplary terms (for example, etc.) used in the embodiments of the present disclosure are for the purpose of describing the embodiments of the present disclosure and are not intended to limit the scope of the embodiments of the present disclosure.

[0060] Furthermore, unless explicitly stated otherwise, expressions such as “essential,” “important,” etc., associated with certain components may not indicate that the components are absolutely required.

[0061] It will be understood by those skilled in the art that the embodiments of the present disclosure may be implemented in modified forms without departing from the spirit and scope of the present disclosure.

[0062] Since the present disclosure allows various changes to the embodiments of the present disclosure, the present disclosure is not limited to specific embodiments, and it will be understood that all changes, equivalents and substitutes that do not depart from the spirit and technical scope of the present disclosure are included in the present disclosure. Therefore, the embodiments of the present disclosure described herein should be understood in all aspects as examples and should not be interpreted as limitations.

[0063] In addition, terms such as "unit", "module", etc. refer to a unit that can be implemented as hardware or software or a combination of hardware and software to process at least one function or operation. "Unit" and "module" can be stored in a storage medium to be addressed and can be implemented as a program that can be executed by a processor. For example, "unit" and "module" can refer to components such as software components, object-oriented software components, class components, and task components, and can include processes, functions, properties, procedures, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables.

[0064] In the present disclosure, the expression "A may include one of a1, a2, and a3" may broadly indicate that examples that may be included in element A include a1, a2, or a3. This expression should not be interpreted as being limited to the meaning that examples included in element A must be limited to a1, a2, and a3. Therefore, as examples included in element A, it should not be interpreted as excluding elements other than a1, a2, and a3. In addition, this expression indicates that element A may include a1, a2, or a3. This expression does not mean that the elements included in element A must be selected from a specific set of elements. That is, this expression should not be restrictively understood as indicating that a1, a2, or a3 that must be selected from the set including a1, a2, and a3 is included in element A.

[0065] In addition, in the present disclosure, the expression “at least one of a1, a2, and / or a3” means one of “a1,” “a2,” “a3,” “a1 and a2,” “a1 and a3,” “a2 and a3,” and “a1, a2, and a3.” Therefore, it should be noted that unless explicitly described as “at least one of a1, at least one of a2, and at least one of a3,” the expression “at least one of a1, a2, and / or a3” should not be interpreted as “at least one of a1,” “at least one of a2,” and “at least one of a3.”

Claims

1. A vision-based coal terminal operation point tracking method, characterized in that: The steps include: S1. Capturing a first monitoring image of the terminal using a distributed camera, automatically identifying a terminal operation point based on the monitoring image, and determining its first image coordinates; S2. Aligning the first image coordinates of the terminal operation point with the map coordinates of the pre-built map to obtain corresponding world coordinates, thereby completing the positioning of the terminal operation point; S3. Calculate the difference between discrete points using the pre-built map to calculate the density of the terminal operation points and form global operation point tracking.

2. The vision-based coal terminal operation point tracking method according to claim 1, characterized in that: Step S1 specifically includes: Acquire video data through code streams of distributed surveillance cameras, calculate a frame extraction interval and a number of frames according to terminal operation conditions, perform frame extraction analysis on the video data based on the frame extraction interval and the number of frames, and obtain the surveillance image; Feature points are extracted from the monitoring image using a visual analysis algorithm, the terminal operation point is detected based on the feature points, and its first image coordinates are determined.

3. The vision-based coal terminal operation point tracking method according to claim 2, characterized in that: The characteristic points include at least one of a mobile forklift operation characteristic, a personnel gathering characteristic, a port machinery equipment operation characteristic, and a berth vessel docking characteristic.

4. The vision-based coal terminal operation point tracking method according to claim 1, characterized in that: Step S2 specifically includes: Get the map coordinate system of the pre-built map, assuming it is: M=(x, y), and convert it into vector form: ; Initially mark the second image coordinates in the camera and the map coordinates to obtain a preliminary conversion relationship , where H is the preliminary transformation matrix; Through map measurement, the initial transformation relationship is continuously corrected to obtain the target transformation matrix , to achieve alignment of the second image coordinates with the map coordinates; According to the target transformation matrix The first image coordinates of the dock operation point are converted into world coordinates to complete the positioning of the dock operation point.

5. The vision-based coal terminal operation point tracking method according to claim 1, characterized in that: Step S3 specifically includes: Extracting a second monitoring image of the dock operation point from the first monitoring image, extracting regional surface information from the second monitoring image, and converting the regional surface information to a corresponding position in the pre-built map; The continuous surface curves and mathematical models in the converted pre-built map are spatially segmented, including TIN to raster, raster to TIN, or vector polygon to raster, to obtain raster basic data and vector basic data; According to the terminal terrain, corresponding sampling methods are used to obtain sampling data of different terrain plane points, that is, unevenly distributed spatial surface data under different scenarios; wherein, the sampling methods include regular sampling, random sampling, cross-section sampling, and clustered sampling, corresponding to the ground, yard, berth, and work area respectively; Considering the error term, the spatial surface data of the dock operation point is converted to Perform surface conversion to obtain surface conversion results; for random samples of operating points, , add the error term , ,in, is the coefficient, i=1,2,3…n; Combining the spatial surface data with the result of the curved surface conversion, using difference calculation to interpolate the discrete working point data into a continuous data surface; Combined with map construction, operation point tracking is formed within the entire terminal area.

6. The vision-based coal terminal operation point tracking method according to claim 5, characterized in that: The difference calculation adopts linear interpolation or Kriging interpolation algorithm.

7. A vision-based coal terminal operation point tracking system, comprising a distributed camera, a processing module, and a storage module; the processing module is connected to the distributed camera and the storage module; The storage module is used to store executable computer program code; The distributed camera is used to capture surveillance images of the terminal and transmit them to the processing module; Its characteristics are: The processing module is configured to execute the method according to any one of claims 1 to 6 by calling the executable computer program code in the storage module.

8. An electronic device comprising: a memory storing executable program code; A processor coupled to the memory; characterized in that: the processor calls the executable program code stored in the memory to execute the method according to any one of claims 1-6.

9. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is executed.

10. A computer program product comprising a computer program stored on a non-transitory computer-readable medium, characterized in that: The computer program is used by a processor to execute the method according to any one of claims 1 to 6.

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