Image processing method, device, equipment and medium

Abstract

The present invention relates to an image processing method, device, equipment and medium, and to the field of data processing technology. The method comprises: obtaining pixel points in a target image to obtain a target point set; selecting point set data that meets a deviation range from the target point set to obtain a data set to be sorted; determining an in-boundary data set and an out-boundary data set according to a preset boundary value and the data set to be sorted; combining the in-boundary data set and the out-boundary data set to obtain a sorting result; connecting the pixel points in the sorting result to obtain a processed image. By excluding the point set data that does not meet the deviation range, anti-noise and abnormal point processing are achieved, the robustness of the image processing algorithm in a noisy environment is improved, and the stability of the generated image is ensured.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an image processing method, device, equipment and medium. Background Art

[0002] Optical character recognition (OCR) is a technology that uses automatic data extraction to quickly convert text images into machine-readable formats. The conversion process requires sorting the identified point set. In short, it is the process of sorting a set of points in space according to certain rules or standards. This sorting technology plays a vital role in fields such as geographic information systems, image processing, machine learning, computer graphics, and virtual reality.

[0003] However, with the complexity of application scenarios and the rapid growth of data volume, traditional image processing methods have gradually revealed their limitations. These methods often face problems such as poor stability, reduced accuracy, reduced efficiency, or even inability to process large-scale data sets. Therefore, in order to meet the high requirements of modern application scenarios for image processing stability, accuracy, and efficiency, it is necessary to explore new image processing technologies. Summary of the invention

[0004] The present invention provides an image processing method to solve the problem of poor stability of sorting results.

[0005] In a first aspect, the present invention provides an image processing method, the method comprising:

[0006] Get the pixel points in the target image to obtain the target point set;

[0007] Selecting point set data that meets the deviation range from the target point set to obtain a data set to be sorted;

[0008] Determine the data set within the boundary and the data set outside the boundary according to the preset boundary value and the data set to be sorted;

[0009] Combining the data set within the boundary and the data set outside the boundary to obtain a sorting result;

[0010] The pixel points in the sorting result are connected to obtain a processed image.

[0011] In a second aspect, the present invention provides an image processing device, comprising:

[0012] A first acquisition unit is used to acquire pixel points in a target image to obtain a target point set;

[0013] A second acquisition unit is used to select point set data that meets the deviation range from the target point set to obtain a data set to be sorted;

[0014] A determination unit, configured to determine an in-boundary data set and an out-boundary data set according to a preset boundary value and the data set to be sorted;

[0015] A combining unit, used for combining the data set within the boundary and the data set outside the boundary to obtain a sorting result;

[0016] The connection unit is used to connect the pixel points in the sorting result to obtain a processed image.

[0017] In a third aspect, an electronic device is provided, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0018] Memory, used to store computer programs;

[0019] The processor is used to implement the steps of the image processing method described in any embodiment of the first aspect when executing the program stored in the memory.

[0020] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the image processing method as described in any embodiment of the first aspect are implemented.

[0021] The above technical solution provided by the embodiment of the present invention has the following advantages compared with the prior art:

[0022] By excluding pixels in the image that do not meet the deviation range, the foreign pixels in the digital image are effectively cleaned up, anti-noise and outlier processing is achieved, the robustness of the image processing algorithm in a noisy environment is improved, and the stability of the generated image is ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0025] Figure 1 A schematic diagram of a flow chart of an image processing method provided by an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of a sub-process of an image processing method provided by an embodiment of the present invention;

[0027] Figure 3 A schematic diagram of a sub-process of an image processing method provided by an embodiment of the present invention;

[0028] Figure 4 A schematic diagram of the structure of an image processing device provided by an embodiment of the present invention;

[0029] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Example 1

[0032] Figure 1 The present invention provides a flowchart of an image processing method. The present invention provides an image processing method. Specifically, see Figure 1 , the image processing method includes the following steps S101-S105.

[0033] S101, acquiring pixel points in a target image to obtain a target point set.

[0034] In this embodiment, the target image is captured by scanning, and the digital document is converted into a two-color or black-and-white version. The light and dark parts of the scanned image or bitmap are analyzed. Then, the program recognizes the dark parts as characters and lines to be recognized, and the bright areas are recognized as the background. The obtained dark parts are the target point set. In some other embodiments, the dark parts are also segmented by area, and the segmented dark parts are used as the target point set to execute subsequent steps S102-S105.

[0035] S102, selecting point set data that meets the deviation range from the target point set to obtain a data set to be sorted.

[0036] In the specific implementation, the target point set is the input two-dimensional point set. By statistically analyzing the overall distribution of the point set, the point set data is divided into those that meet the deviation range and those that do not meet the deviation range. All point set data that meet the deviation range are used as the data set to be sorted, and those that do not meet the deviation range include potential abnormal points and extreme values. By excluding the point set data that does not meet the deviation range from the target point set and obtaining the point set data that meets the deviation range, abnormal points and noise data can be effectively removed to ensure the accuracy and consistency of the data.

[0037] In one embodiment, see Figure 2 , Figure 2 A schematic diagram of a sub-flow of an image processing method provided by an embodiment of the present invention. The above step S102 includes steps S201-S202:

[0038] S201, determining whether the amount of point set data of the target point set is less than a preset amount threshold.

[0039] In one embodiment, the preset quantity threshold is 2, and it is determined whether the point set data in the target point set is less than 2. If the number of point set data of the target point set is less than 2, the point set data of the target point set is determined to be point set data that meets the deviation range. If the number of point set data of the target point set is not less than 2, it is necessary to further determine whether the point set data of the target point set meets the deviation range or does not meet the deviation range.

[0040] S202: If not, use the point set data whose target distance is less than a preset first distance threshold as the point set data that meets the deviation range, and the target distance is the distance between the average value of the target point set and the point set data.

[0041] In a specific implementation, if the number of point set data of the target point set is not less than a preset number threshold, the average value of all point set data and its corresponding position are calculated, and the distance from the position of each point set data to the position of the average value is recorded as the target distance. If the target distance is not less than the first distance threshold, it is determined that the point set data deviates too much from the average value, indicating that the point set data may be an abnormal point or noise data, and the point set data is excluded from the point set to ensure the accuracy and consistency of the data. If the target distance is less than the first distance threshold, it is determined that the deviation of the point set data from the average value is within a reasonable range, that is, the point set data is point set data that meets the deviation range. For example: the average value is calculated based on the point set of the dark part of the image, an average point is determined based on the average value, and the point set data of the target point set and the average point are calculated one by one to obtain the target distance, and the point set data whose target distance is less than the first distance threshold is regarded as point set data that meets the deviation range.

[0042] In one embodiment, the above step S201 further includes the step of: if yes, taking the target point set as the point set data that meets the deviation range.

[0043] In a specific implementation, if the amount of point set data of the target point set is less than a preset amount threshold, the point set data of the target point set is determined to be point set data that meets the deviation range.

[0044] By optimizing the complexity of the sorting algorithm, we first determine whether the number of point set data of the target point set is within the quantity threshold. If so, the point set data of the target point set is determined to be the point set data that meets the deviation range, reducing the steps of calculating the average and maximum values ​​of the target point set, improving the processing speed of large-scale point sets, realizing real-time processing, and achieving the effect of improving processing efficiency.

[0045] In one embodiment, see Figure 3 , Figure 3 A schematic diagram of a sub-flow of an image processing method provided by an embodiment of the present invention. The above step S202 includes steps S301-S303:

[0046] S301, determining an average value and a maximum value according to the target point set.

[0047] In a specific implementation, the average value includes the average value of all point set data and its corresponding position, which is obtained by calculating all point set data, and the maximum value is the point set data with the maximum target distance.

[0048] S302: Determine whether the distance between the average value and the maximum value is less than a preset second distance threshold.

[0049] In a specific implementation, it is determined whether the maximum value of the target distance in the target point set is less than a preset second distance threshold. If the maximum value of the target distance is less than the preset second distance, all point set data in the target point set meet the deviation range. If the maximum value of the target distance is not less than the preset second distance, the point set data corresponding to the maximum value does not meet the deviation range, indicating that the point may be an abnormal point or noise data.

[0050] S303: If not, the target point set after removing the maximum value is used as a new target point set, and the step of determining the average value and the maximum value according to the target point set is performed.

[0051] In a specific implementation, if the distance between the average value and the maximum value is not less than the preset second distance threshold, the point set data that does not meet the deviation range is removed from the target point set, and the removed target point set is used as the new target point set, and steps S301-S302 are repeated. For example: the point set of the dark part of the image is recorded as the character "1", and the point set of the light part of the image is recorded as the character "0". The point set data that does not meet the deviation range is removed from the target point set, that is, the character "1" whose distance from the point set data to the average value is not less than the second distance threshold is changed to the character "0", and the target point set containing the character "1" is obtained as the new target point set.

[0052] In high-precision application scenarios, by improving the accuracy of the algorithm, point set data that may be outliers or noise data can be eliminated to ensure the reliability and accuracy of the final sorting results, thereby achieving the effect of enhancing the sorting accuracy.

[0053] S103, determining the data set within the boundary and the data set outside the boundary according to the preset boundary value and the data set to be sorted.

[0054] In this embodiment, by comparing the point set data of the data set to be sorted with the boundary value, the point set data greater than the boundary value is divided into the data set outside the boundary, and the point set data less than the boundary value is divided into the data set inside the boundary. The data set inside the boundary is smaller than the data set outside the boundary. In one embodiment, the point set data of the data set inside the boundary and the point set data of the data set outside the boundary can also be sorted separately through a sorting algorithm.

[0055] In one embodiment, the above step S103 includes the steps of: taking the data set within the boundary as a new target point set, and executing the step of selecting point set data that meets the deviation range from the target point set to obtain the data set to be sorted.

[0056] In a specific implementation, by recursively executing steps S102-103, combined with dynamically changing boundary values, the first intra-boundary data set and the first extra-boundary data set obtained by the first execution, the second intra-boundary data set and the second extra-boundary data set obtained by the second execution are obtained respectively... until the entire intra-boundary data set is sorted. By recursively executing steps S102-103, point set data can be effectively sorted and the sorting algorithm can be optimized, so that when processing non-uniformly distributed point sets, the spatial locality of the data can be better maintained and the data locality can be maintained.

[0057] In one embodiment, the above step S103 further includes the step of: taking the out-of-boundary data set as a new target point set, and executing the step of selecting point set data that meets the deviation range from the target point set to obtain the data set to be sorted.

[0058] In a specific implementation, by recursively executing steps S102-103, combined with dynamically changing boundary values, the first in-boundary data set and the first out-boundary data set obtained by the first execution, the second in-boundary data set and the second out-boundary data set obtained by the second execution are obtained respectively... until the entire out-boundary data set is sorted. By recursively executing steps S102-103, point set data can be effectively sorted and the sorting algorithm can be optimized, so that when processing non-uniformly distributed point sets, the spatial locality of the data can be better maintained and the data locality can be maintained.

[0059] S104, combining the data set within the boundary and the data set outside the boundary to obtain a sorting result.

[0060] In this embodiment, by comparing the point set data of the data set to be sorted with the boundary value, the point set data greater than the boundary value is divided into the out-of-boundary data set, the point set data less than the boundary value is divided into the in-boundary data set, the in-boundary data set is smaller than the out-of-boundary data set, and the in-boundary data set and the out-of-boundary data set are combined in order to obtain the sorting result. In one embodiment, by recursively executing steps S102-S103, multiple in-boundary data sets and multiple out-of-boundary data sets are obtained, and each time the in-boundary data set and the out-of-boundary data set are divided, the outliers or noise data are excluded and the point set data are sorted at the same time. Thus, the multiple in-boundary data sets and the multiple out-of-boundary data sets finally obtained are combined to obtain the sorting result of the target point set. According to the sorting result, the improperly aligned pixels in the scanned image can be corrected, and the digital image can be cleaned up to remove foreign pixels.

[0061] S105, connecting the pixel points in the sorting result to obtain a processed image.

[0062] In specific implementation,

[0063] The embodiments of the present invention can achieve the following advantages:

[0064] By excluding pixels in the image that do not meet the deviation range, the foreign pixels in the digital image are effectively cleaned up, anti-noise and outlier processing is achieved, the robustness of the image processing algorithm in a noisy environment is improved, and the stability of the generated image is ensured.

[0065] See also Figure 4 The embodiment of the present invention further provides an image processing device 400 , which includes a first acquisition unit 401 , a second acquisition unit 402 , a determination unit 403 , a combination unit 404 , and a connection unit 405 .

[0066] The first acquisition unit 401 is used to acquire pixel points in a target image to obtain a target point set.

[0067] The second acquisition unit 402 is used to select point set data that meets the deviation range from the target point set to obtain a data set to be sorted.

[0068] In one embodiment, selecting point set data that meets the deviation range from the target point set to obtain the data set to be sorted includes:

[0069] Determining whether the number of point set data of the target point set is less than a preset number threshold;

[0070] If not, the point set data whose target distance is less than a preset first distance threshold is used as the point set data that meets the deviation range, and the target distance is the distance between the average value of the target point set and the point set data.

[0071] In one embodiment, the determining whether the amount of point set data of the target point set is less than a preset amount threshold further includes:

[0072] If so, the target point set is determined to be the point set data that meets the deviation range.

[0073] In one embodiment, the point set data whose target distance is less than a preset first distance threshold is used as the point set data that meets the deviation range, and the target distance is the distance between the average value of the target point set and the point set data, including:

[0074] Determine an average value and a maximum value according to the target point set;

[0075] Determine whether the distance between the average value and the maximum value is less than a preset second distance threshold;

[0076] If not, the target point set after removing the maximum value is used as a new target point set, and the step of determining the average value and the maximum value according to the target point set is performed.

[0077] In one embodiment, the determining whether the distance between the average value and the maximum value is less than a preset second distance threshold further includes:

[0078] If so, the point set data of the target point set is used as the point set data that meets the deviation range.

[0079] The determination unit 403 is used to determine the data set within the boundary and the data set outside the boundary according to the preset boundary value and the data set to be sorted.

[0080] In one embodiment, determining the in-boundary data set and the out-boundary data set according to the preset boundary value and the to-be-sorted data set includes:

[0081] The data set within the boundary is used as a new target point set, and the step of selecting point set data that meets the deviation range from the target point set is performed to obtain the data set to be sorted.

[0082] In one embodiment, the step of determining the in-boundary data set and the out-boundary data set according to the preset boundary value and the to-be-sorted data set further includes:

[0083] The out-of-boundary data set is used as a new target point set, and the step of selecting point set data that meets the deviation range from the target point set is performed to obtain the data set to be sorted.

[0084] The combining unit 404 is used to combine the data set within the boundary and the data set outside the boundary to obtain a sorting result.

[0085] In one embodiment, there are multiple guardianship accounts bound to the second account, and sending a video request to the guardianship accounts bound to the second account includes:

[0086] Displaying all guardianship accounts bound to the second account in the interactive interface;

[0087] receiving a first operation instruction input by a user on the interactive interface, and taking the monitoring account corresponding to the first operation instruction as a target account;

[0088] Send a video request to the target account.

[0089] In one embodiment, the displaying of all guardianship accounts bound to the second account in the interactive interface includes:

[0090] The message information is obtained from a preset storage queue, and the message information is displayed in the interactive interface.

[0091] The connecting unit 405 is used to connect the pixels in the sorting result to obtain a processed image.

[0092] like Figure 5 As shown, Figure 5 5 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a terminal or a server, wherein the terminal may be an electronic device with communication functions such as a smart phone, a tablet computer, a laptop computer, a desktop computer, a personal digital assistant, and a wearable device. The server may be an independent server or a server cluster composed of multiple servers.

[0093] The computer device 500 includes a processor 502 , a memory, and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0094] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, the processor 502 may execute an image processing method.

[0095] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0096] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute an image processing method.

[0097] The network interface 505 is used to communicate with other devices over the network. Those skilled in the art will appreciate that the above structure is only a block diagram of a portion of the structure related to the present application solution, and does not constitute a limitation on the computer device 500 to which the present application solution is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0098] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0099] It is understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0100] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program.

[0101] The storage medium is a physical, non-transient storage medium, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which can store program codes. The computer-readable storage medium can be non-volatile or volatile.

[0102] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0103] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0104] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0105] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0106] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0107] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0108] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. An image processing method, characterized in that: The method comprises: Get the pixel points in the target image to obtain the target point set; Selecting point set data that meets the deviation range from the target point set to obtain a data set to be sorted; Determine the data set within the boundary and the data set outside the boundary according to the preset boundary value and the data set to be sorted; Combining the data set within the boundary and the data set outside the boundary to obtain a sorting result; Connecting the pixels in the sorting result to obtain a processed image; The step of selecting point set data that meets the deviation range from the target point set to obtain a data set to be sorted includes: Determining whether the number of point set data of the target point set is less than a preset number threshold; If not, the point set data whose target distance is less than a preset first distance threshold is used as the point set data that meets the deviation range, and the target distance is the distance between the average value of the target point set and the point set data; The point set data whose target distance is less than a preset first distance threshold is used as the point set data that meets the deviation range, and the target distance is the distance between the average value of the target point set and the point set data, including: Determine an average value and a maximum value according to the target point set; Determine whether the distance between the average value and the maximum value is less than a preset second distance threshold; If not, the target point set after removing the maximum value is used as a new target point set, and the step of determining the average value and the maximum value according to the target point set is performed.

2. The method according to claim 1, characterized in that The step of judging whether the number of point set data of the target point set is less than a preset number threshold value further includes: If so, the target point set is determined to be the point set data that meets the deviation range.

3. The method according to claim 1, characterized in that The determining whether the distance between the average value and the maximum value is less than a preset second distance threshold further includes: If so, the point set data of the target point set is used as the point set data that meets the deviation range.

4. The method according to claim 1, characterized in that The step of determining the data set within the boundary and the data set outside the boundary according to the preset boundary value and the data set to be sorted includes: The data set within the boundary is taken as a new target point set, and the step of selecting point set data that meets the deviation range from the target point set is performed to obtain the data set to be sorted.

5. The method according to claim 1, characterized in that The step of determining the data set within the boundary and the data set outside the boundary according to the preset boundary value and the data set to be sorted further includes: The out-of-boundary data set is used as a new target point set, and the step of selecting point set data that meets the deviation range from the target point set is performed to obtain the data set to be sorted.

6. An image processing device, characterized in that: include: A first acquisition unit is used to acquire pixel points in a target image to obtain a target point set; A second acquisition unit is used to select point set data that meets the deviation range from the target point set to obtain a data set to be sorted; wherein, the step of selecting point set data that meets the deviation range from the target point set to obtain the data set to be sorted includes: judging whether the number of point set data of the target point set is less than a preset number threshold; if not, taking the point set data whose target distance is less than a preset first distance threshold as the point set data that meets the deviation range, and the target distance is the distance between the average value of the target point set and the point set data; taking the point set data whose target distance is less than the preset first distance threshold as the point set data that meets the deviation range, and the target distance is the distance between the average value of the target point set and the point set data, includes: determining an average value and a maximum value according to the target point set; judging whether the distance between the average value and the maximum value is less than a preset second distance threshold; if not, taking the target point set after removing the maximum value as a new target point set, and executing the step of determining the average value and the maximum value according to the target point set; A determination unit, configured to determine an in-boundary data set and an out-boundary data set according to a preset boundary value and the data set to be sorted; A combining unit, used for combining the data set within the boundary and the data set outside the boundary to obtain a sorting result; The connection unit is used to connect the pixel points in the sorting result to obtain a processed image.

7. A computer device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, for implementing the steps of the method described in any one of claims 1 to 5 when executing a program stored in a memory.

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

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