Scanning strategy dynamic adjustment method and system based on multi-time oral cavity scanning optimization

Abstract

The invention relates to the technical field of oral scanning imaging, in particular to a scanning strategy dynamic adjustment method and system based on multi-time oral scanning optimization, and the method comprises the steps: obtaining the three-dimensional point cloud data of the oral cavity of a patient through high-precision oral scanning equipment, and carrying out the statistics of the number of times of current scanning; further calculating the error between the current scanned point cloud and the global point cloud by calculating the Euclidean distance between the point pairs; calculating the difference degree between the original point cloud data obtained by the current scanning and the repaired point cloud data; constructing an objective function to minimize scanning times and errors; and monitoring the scanning quality and times in real time, and adjusting the scanning strategy according to the feedback of the target function. The scanning strategy is dynamically adjusted by monitoring the target function value in real time, it can be ensured that the high-precision oral cavity three-dimensional data model is obtained within the shortest time, computing resource waste is reduced, and efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oral scanning imaging, and in particular to a scanning strategy dynamic adjustment method and system based on multiple oral scanning optimizations. Background Art

[0002] Due to the small space in the oral cavity, the complex environment, and the jitter of the handheld device, when the doctor scans the patient's mouth, he needs to repeatedly operate the scanner on the teeth in the same area to obtain complete tooth point cloud data.

[0003] In existing technologies, there is usually no efficient mechanism to update the global point cloud data and re-evaluate the errors in real time, which means that in subsequent scanning processes, the errors and stitching accuracy may be underestimated or not corrected in time.

[0004] In the prior art, the scanning process usually relies on manual experience to decide whether to continue scanning or make adjustments, and lacks a data-driven decision support mechanism, which results in a lack of flexibility and adaptability in the scanning process.

[0005] The existing technology has defects in scanning times, error accumulation, repair effect evaluation, real-time feedback mechanism, etc. In particular, the shortcomings in dynamic adjustment and real-time feedback make it impossible to achieve the best balance between accuracy and efficiency in the scanning process. Summary of the invention

[0006] The present invention aims to solve the problems in the prior art that scanning accuracy and scanning efficiency cannot be effectively balanced, and there is a lack of real-time feedback and adjustment mechanisms. The present invention proposes a scanning strategy dynamic adjustment method and system based on multiple oral scan optimizations. By dynamically adjusting the scanning strategy through real-time monitoring of the objective function value, it can ensure that a high-precision oral three-dimensional data model is obtained in the shortest time, reduce the waste of computing resources and improve efficiency.

[0007] To achieve the above purpose, the technical solution adopted is:

[0008] The present invention provides a scanning strategy dynamic adjustment method based on multiple oral scan optimizations, comprising:

[0009] Use high-precision oral scanning equipment to obtain the three-dimensional point cloud data of the patient's oral cavity and count the number of scans for the current time;

[0010] By calculating the Euclidean distance between point pairs, the error between the point cloud of the current scan and the global point cloud is further calculated;

[0011] Calculate the difference between the original point cloud obtained from the current scan and the repaired point cloud data;

[0012] Construct an objective function to minimize the number of scans and the error;

[0013] Monitor the scanning quality and number in real time, and adjust the scanning strategy according to the feedback of the objective function.

[0014] According to the scanning strategy dynamic adjustment method based on multiple oral scan optimization of the present invention, further, calculating the error between the point cloud of the current scan and the global point cloud includes:

[0015] The point clouds obtained from all scans are registered as the global point cloud;

[0016] Use feature matching method to preliminarily align the point cloud of the current scan with the global point cloud;

[0017] The ICP algorithm is used to accurately align the point cloud of the current scan with the global point cloud;

[0018] After the registration is completed, the distance difference between the point cloud of the current scan and the global point cloud is calculated, and the distance difference is used as the error between the point cloud of the current scan and the global point cloud.

[0019] According to the scanning strategy dynamic adjustment method based on multiple oral scan optimization of the present invention, further, calculating the distance difference between the point cloud of the current scan and the global point cloud, and taking the distance difference as the error between the point cloud of the current scan and the global point cloud includes:

[0020] First, the point cloud of the current scan is recorded as P current , the global point cloud is recorded as P global , for each point P in the current scan i ∈P current , find the point P closest to it in the global point cloud j ∈P global , calculate the Euclidean distance between point pairs;

[0021] Then the mean square error of the Euclidean distance between the point cloud scanned currently and all point pairs in the global point cloud is calculated, and the mean square error value is used as the error E between the point cloud collected currently and the global point cloud.

[0022] According to the scanning strategy dynamic adjustment method based on multiple oral scan optimization of the present invention, further, calculating the difference between the original point cloud obtained by the current scan and the repaired point cloud data includes:

[0023] First, the point cloud data of the current scan is read, and after denoising and smoothing the data, the nearest neighbor interpolation method is used to fill in the missing area to complete the repair; then the error E1 between the currently acquired point cloud and the global point cloud after the repair is calculated, and finally the difference degree S = |E1-E| is calculated, where E is the error between the currently acquired point cloud and the global point cloud before repair.

[0024] According to the scanning strategy dynamic adjustment method based on multiple oral scan optimization of the present invention, further, according to the calculated current scanning number N, the error E of the current scanning point cloud, and the difference degree S of the current scanning point cloud, the objective function expression is constructed as follows:

[0025] F=W1N+W2E-W3S+W4R(N,E)

[0026] Among them, W1, W2, W3, and W4 are weight coefficients, and R(N, E) is the relationship function between E and N.

[0027] According to the scanning strategy dynamic adjustment method based on multiple oral scan optimization of the present invention, further, the expression of the relationship function R(N, E) is as follows:

[0028]

[0029] in, It means that the scanning efficiency will decrease with the increase of the number of scans. This item encourages reducing redundant scans. E means that the scanning accuracy will decrease with the increase of errors. This item encourages increasing the number of scans or performing data repair when the error is large. W5 and W6 are weight coefficients used to balance the relationship between the number of scans and the error.

[0030] According to the scanning strategy dynamic adjustment method based on multiple oral scan optimization of the present invention, further, real-time monitoring of scanning quality and number of times, and adjusting the scanning strategy according to feedback of the objective function include:

[0031] During the oral scanning process, the objective function will be calculated every time a scan is performed; if the calculated current scanning objective function is greater than the objective function threshold, the scanning strategy needs to be adjusted to optimize the scanning path or angle; if the calculated current scanning objective function is less than the objective function threshold, the requirements for scanning efficiency and accuracy have been met;

[0032] After each scan, the global point cloud data needs to be updated, and the error E and the degree of repair difference S between the currently acquired point cloud and the global point cloud need to be re-evaluated.

[0033] Furthermore, the present invention also provides a scanning strategy dynamic adjustment system based on multiple oral scan optimization, including a scanning times statistics module, an error calculation module, a restoration difference calculation module, an objective function construction module and a strategy adjustment module, wherein:

[0034] The scanning times statistics module is used to obtain the three-dimensional point cloud data of the patient's oral cavity using a high-precision oral scanning device and count the current scanning times;

[0035] The error calculation module is used to further calculate the error between the point cloud of the current scan and the global point cloud by calculating the Euclidean distance between the point pairs;

[0036] A repair difference calculation module is used to calculate the difference between the original point cloud obtained by the current scan and the repaired point cloud data;

[0037] An objective function building module is used to build an objective function to minimize the number of scans and the error;

[0038] The strategy adjustment module is used to monitor the scanning quality and number in real time and adjust the scanning strategy according to the feedback of the objective function.

[0039] The beneficial effects achieved by adopting the above technical solution are:

[0040] The present invention integrates the number of scans, error, degree of difference and the relationship function between the number of scans and the error into a comprehensive evaluation index by constructing a dynamic objective function, and dynamically adjusts the scanning strategy according to the real-time monitoring objective function value, optimizes the scanning path or angle, and can maintain a small error with the minimum number of scans, and optimize the repair operation to achieve a balance between efficiency and accuracy. After each scan, the system needs to determine whether the scanning task has achieved the required accuracy to avoid wasting time and computing resources due to excessive scanning. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings of the embodiments of the present invention, wherein the drawings are only used to illustrate some embodiments of the present invention, but not to limit all embodiments of the present invention thereto.

[0042] Figure 1 It is a flow chart of a method for dynamically adjusting scanning strategies based on multiple oral scan optimizations according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings of specific embodiments of the present invention to clearly and completely describe the exemplary scheme of the embodiment of the present invention. Unless otherwise defined, the technical terms or scientific terms used in the present invention should be the common meanings understood by people with ordinary skills in the field.

[0044] This embodiment discloses a scanning strategy dynamic adjustment method based on multiple oral scan optimizations, such as Figure 1 As shown, the following steps are included:

[0045] Step S101: Use a high-precision oral scanning device to obtain three-dimensional point cloud data of the patient's oral cavity and count the number of current scans.

[0046] During the scanning process, the oral scanning device obtains point cloud data of a small range and small scene in a single scan, so multiple scans are required to align and splice the scan results of different times in sequence to obtain a complete 3D point cloud data model of the patient's oral cavity. However, the more times the splicing is done, the greater the error. Therefore, statistically recording the number of scans N for the current time helps monitor the scanning progress and provide basic data for subsequent error calculation and optimization.

[0047] By counting the number of scans N, the system can clearly understand the progress of the current scan and ensure the continuity of the scanning operation. Recording the number of scans provides a key reference for subsequent error analysis and repair, and helps to understand the relationship between errors and scan times.

[0048] Step S102: further calculating the error between the point cloud of the current scan and the global point cloud by calculating the Euclidean distance between the point pairs.

[0049] After two scans have been performed, for example, and the point cloud data of the two scans have been aligned, the point cloud after the two scans have been aligned is used as the global point cloud. The error E between the point cloud of the current scan and the global point cloud is further calculated. The implementation process is as follows:

[0050] ① Preliminary alignment: Use feature matching method to preliminarily align the point cloud of the current scan with the global point cloud.

[0051] ② Accurate registration: Use the existing ICP algorithm to accurately align the point cloud of the current scan with the global point cloud.

[0052] ③ After completing the registration, calculate the distance difference between the point cloud of the current scan and the global point cloud, and use the distance difference as the error E between the point cloud of the current scan and the global point cloud.

[0053] First, the point cloud of the current scan is recorded as P current , the global point cloud is recorded as P global , for each point P in the current scan i ∈P current , find the point P closest to it in the global point cloud j ∈P global , calculate the Euclidean distance between the i-th point in the current scan point cloud and the j-th point closest to the global point cloud:

[0054]

[0055] Among them, (x i ,y i , z i ) represents the coordinates of the i-th point in the current scanned point cloud, (x j ,y j, z j ) represents the coordinates of the jth point in the global point cloud.

[0056] Then calculate the mean square error of the Euclidean distance between the point cloud of the current scan and all the point pairs in the global point cloud, and use the mean square error value as the error E between the point cloud collected currently and the global point cloud. The calculation formula is as follows:

[0057]

[0058] Where N represents the number of point clouds scanned currently.

[0059] The size of the error E reflects the matching degree between the current point cloud collected and the nearest point in the global point cloud, quantifies the matching degree between the current scan data and the global point cloud, and is used to evaluate the accuracy of the current scan. If the error is large, it means that there is a large deviation between the current scan and the global point cloud, and it is necessary to adjust the scan path or optimize the parameters of the scanning device.

[0060] This solution can quantify the accuracy of the current scan and judge the scan quality by calculating the error between point clouds. It helps identify the deviations generated during the scanning process, provides a basis for further optimization and repair, and avoids the reduction of subsequent modeling accuracy due to excessive errors.

[0061] Step S103: Calculate the degree of difference between the original point cloud obtained by the current scan and the repaired point cloud data.

[0062] Since each scanned point cloud data may contain noise or be partially missing, further processing and repair are required. The statistical denoising method is used to remove noise, and the nearest neighbor interpolation method is used to fill the missing area to complete the repair. After the repair, the difference before and after the repair is calculated to obtain the difference degree S. The difference degree reflects the effect of the repair operation on reducing errors. The specific calculation process is as follows:

[0063] First, read the point cloud data of the current scan, denoise the data, and use the nearest neighbor interpolation method to fill in the missing points to complete the repair. Then calculate the error E1 between the repaired point cloud collected currently and the global point cloud, and finally calculate the difference degree S = |E1-E|.

[0064] This solution removes unnecessary noise and fills in missing areas through denoising and repair, significantly improving the integrity and accuracy of the data. The difference between before and after repair is calculated to help evaluate the effectiveness of the repair operation and ensure that the error of the repaired point cloud data is minimal.

[0065] Step S104: construct an objective function to minimize the number of scans and the error.

[0066] According to the calculated current scan times N, the error E of the current scan point cloud, and the difference S of the current scan point cloud, the objective function is constructed to minimize the scan times and obtain the optimal oral 3D point cloud data model under the minimum scan times. The objective function calculation formula is:

[0067] F=W1N+W2E-W3S+W4R(N,E)

[0068] Among them, W1, W2, W3, and W4 are weight coefficients used to balance the influence of different targets. R(N, E) is the relationship function between E and N. It is a quantitative indicator used to evaluate the pros and cons of the current scanning strategy after comprehensively considering the number of scans N and the error E.

[0069] The goal of the relationship function R(N, E) is to dynamically adjust the scanning strategy by evaluating the scanning error E and the current number of scans N in real time, avoiding unnecessary redundant scans and optimizing the data repair process. The output value of this relationship function will affect the value of the objective function, thereby automatically adjusting the scanning strategy during the scanning process. The relationship function R(N, E) needs to consider both the accuracy and efficiency of the scan, that is, while reducing the number of scans, ensure that the error of the final scan result is minimized. The expression of R(N, E) is:

[0070]

[0071] in, It means that as the number of scans increases, the scanning efficiency will decrease, so the fewer the scans, the higher the target value R; this term encourages reducing redundant scans. E means that as the error increases, the scanning accuracy decreases, so the larger the error, the higher the target value R; this term encourages increasing the number of scans or performing data repair when the error is large; W5 and W6 are weight coefficients used to balance the relationship between the number of scans and the error.

[0072] This solution optimizes the balance between scanning efficiency and accuracy by dynamically adjusting the weights in the objective function. By using the number of scans and errors as feedback items, the scanning strategy can be flexibly adjusted according to the actual scanning progress and error changes, realizing an adaptive multi-objective optimization mechanism. The system can optimize the scanning process based on real-time data to ensure that the error can be kept small with the minimum number of scans.

[0073] By using the relationship function R(N, E) between the number of scans N and the error E, the strategy can be adjusted according to the real-time error and the number of scans, thereby avoiding unnecessary redundant scans, reducing resource waste, and improving scanning efficiency, so as to better balance the number of scans and errors and achieve more efficient and accurate scanning.

[0074] Step S105: monitor the scanning quality and number in real time, and adjust the scanning strategy according to the feedback of the objective function.

[0075] The objective function value F is a comprehensive evaluation index of the current scanning strategy. During the oral scanning process, the objective function will be calculated once each time a scan is performed, and the effect of the current scanning strategy will be judged based on its value.

[0076] Select the optimal scanning strategy based on the value of the objective function. When the calculated current scanning objective function is greater than the threshold, it may mean that the number of scans is too many or the error is too large, and the scanning strategy needs to be adjusted in time. The scanning path or angle can be optimized to reduce overlapping areas and scanning blind spots, improve scanning efficiency, and capture point cloud data more accurately. If the calculated current scanning objective function is less than the threshold, it means that the requirements of scanning efficiency and accuracy have been met, and there is no need to adjust the scanning strategy. This means that the current strategy saves scan times and reduces errors, achieving better results. The setting of the objective function threshold can be adjusted according to the actual situation on site.

[0077] During the scanning process, the output value of the objective function will reflect the current scanning effect in real time. When the error is too large, the system may need to increase the number of scans or repair the existing data. When the number of scans is too many, the system will encourage the reduction of redundant scans to optimize efficiency.

[0078] As the scan progresses and the error changes, the value of the objective function will also change. According to the changing trend of the objective function, dynamic strategy adjustments can be made to obtain the most accurate results in the shortest time and with the least resource consumption.

[0079] The significance of the objective function is to comprehensively optimize the scanning process, balance the number of scans, error and accuracy, and achieve efficient and accurate scanning by minimizing the objective function. In actual operation, the calculated objective function can be used as feedback after each scan to help the decision-making system choose whether to continue scanning or adjust the scanning strategy, ultimately ensuring the most accurate scanning results with the least number of scans.

[0080] With each scan, the global point cloud data will be continuously updated, which will affect the subsequent error evaluation and repair strategy, forming a closed-loop feedback optimization: after each scan, the global point cloud data needs to be updated, and the error E and repair difference S between the current point cloud and the global point cloud will be re-evaluated. By continuously updating the global point cloud, error and repair status, the scanning process is continuously optimized and the final model is guaranteed to be as optimized as possible.

[0081] Corresponding to the above method, this embodiment also proposes a scanning strategy dynamic adjustment system based on multiple oral scan optimization, including a scanning times statistics module, an error calculation module, a restoration difference calculation module, an objective function construction module and a strategy adjustment module, wherein:

[0082] The scanning times statistics module is used to obtain the three-dimensional point cloud data of the patient's oral cavity using a high-precision oral scanning device and to count the current scanning times.

[0083] The error calculation module is used to further calculate the error between the point cloud of the current scan and the global point cloud by calculating the Euclidean distance between the point pairs.

[0084] The repair difference calculation module is used to calculate the difference between the original point cloud obtained by the current scan and the repaired point cloud data.

[0085] The objective function building module is used to build the objective function to minimize the number of scans and the error.

[0086] The strategy adjustment module is used to monitor the scanning quality and number in real time and adjust the scanning strategy according to the feedback of the objective function.

[0087] Unless otherwise specifically stated, the components, steps, numerical expressions and values ​​set forth in these embodiments do not limit the scope of the present invention.

[0088] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0089] The units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented by 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 the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person of ordinary skill in the art may use different methods to implement the described functions for each specific application, but such implementation is not considered to be beyond the scope of the present invention.

[0090] Those skilled in the art will appreciate that all or part of the steps in the above method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a disk or an optical disk. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits, and accordingly, each module / unit in the above embodiment can be implemented in the form of hardware or in the form of software function modules. The present invention is not limited to any specific form of combination of hardware and software.

[0091] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A scanning strategy dynamic adjustment method based on multiple oral scan optimization, characterized in that: include: Use high-precision oral scanning equipment to obtain the three-dimensional point cloud data of the patient's oral cavity and count the number of scans for the current time; By calculating the Euclidean distance between point pairs, the error between the point cloud of the current scan and the global point cloud is further calculated; Calculate the difference between the original point cloud obtained from the current scan and the repaired point cloud data; Construct an objective function to minimize the number of scans and the error; Monitor the scanning quality and number in real time, and adjust the scanning strategy according to the feedback of the objective function.

2. The scanning strategy dynamic adjustment method based on multiple oral scan optimization according to claim 1 is characterized in that: Calculating the error between the current scanned point cloud and the global point cloud includes: The point clouds obtained from all scans are registered as the global point cloud; Use feature matching method to preliminarily align the point cloud of the current scan with the global point cloud; The ICP algorithm is used to accurately align the point cloud of the current scan with the global point cloud; After the registration is completed, the distance difference between the point cloud of the current scan and the global point cloud is calculated, and the distance difference is used as the error between the point cloud of the current scan and the global point cloud.

3. The scanning strategy dynamic adjustment method based on multiple oral scan optimization according to claim 2 is characterized in that: Calculate the distance difference between the point cloud of the current scan and the global point cloud, and use the distance difference as the error between the point cloud of the current scan and the global point cloud. First, the point cloud of the current scan is recorded as P current , the global point cloud is recorded as P global , for each point P in the current scan i ∈P current , find the point P closest to it in the global point cloud j ∈P global , calculate the Euclidean distance between point pairs; Then the mean square error of the Euclidean distance between the point cloud scanned currently and all point pairs in the global point cloud is calculated, and the mean square error value is used as the error E between the point cloud collected currently and the global point cloud.

4. The scanning strategy dynamic adjustment method based on multiple oral scan optimization according to claim 1, characterized in that: The difference between the original point cloud obtained by the current scan and the repaired point cloud data is calculated as follows: First, the point cloud data of the current scan is read, and after denoising and smoothing the data, the nearest neighbor interpolation method is used to fill in the missing area to complete the repair; then the error E1 between the currently acquired point cloud and the global point cloud after the repair is calculated, and finally the difference degree S = |E1-E| is calculated, where E is the error between the currently acquired point cloud and the global point cloud before repair.

5. The scanning strategy dynamic adjustment method based on multiple oral scan optimization according to claim 1, characterized in that: According to the calculated current scan times N, the error E of the current scan point cloud, and the difference S of the current scan point cloud, the objective function expression is constructed as follows: F=W1N+W2E-W3S+W4R(N,E) Among them, W1, W2, W3, and W4 are weight coefficients, and R(N, E) is the relationship function between E and N.

6. The scanning strategy dynamic adjustment method based on multiple oral scan optimization according to claim 5, characterized in that: The expression of the relational function R(N, E) is as follows: in, It means that the scanning efficiency will decrease with the increase of the number of scans. This item encourages reducing redundant scans. E means that the scanning accuracy will decrease with the increase of errors. This item encourages increasing the number of scans or performing data repair when the error is large. W5 and W6 are weight coefficients used to balance the relationship between the number of scans and the error.

7. The scanning strategy dynamic adjustment method based on multiple oral scan optimization according to claim 1, characterized in that: Real-time monitoring of scanning quality and frequency, and adjustment of scanning strategies based on feedback from the objective function include: During the oral scanning process, the objective function will be calculated every time a scan is performed; if the calculated current scanning objective function is greater than the objective function threshold, the scanning strategy needs to be adjusted to optimize the scanning path or angle; if the calculated current scanning objective function is less than the objective function threshold, the requirements for scanning efficiency and accuracy have been met; After each scan, the global point cloud data needs to be updated, and the error E and the degree of repair difference S between the currently acquired point cloud and the global point cloud need to be re-evaluated.

8. A scanning strategy dynamic adjustment system based on multiple oral scan optimization, characterized in that: It includes a scanning times statistics module, an error calculation module, a repair difference calculation module, an objective function construction module and a strategy adjustment module, among which: The scanning times statistics module is used to obtain the three-dimensional point cloud data of the patient's oral cavity using a high-precision oral scanning device and count the current scanning times; The error calculation module is used to further calculate the error between the point cloud of the current scan and the global point cloud by calculating the Euclidean distance between the point pairs; A repair difference calculation module is used to calculate the difference between the original point cloud obtained by the current scan and the repaired point cloud data; An objective function building module is used to build an objective function to minimize the number of scans and the error; The strategy adjustment module is used to monitor the scanning quality and number in real time and adjust the scanning strategy according to the feedback of the objective function.

9. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. 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 7 are implemented.

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