Method and system for extracting structured data table of power grid engineering drawing and medium

Abstract

The invention relates to a structured data table extraction method and system for a power grid engineering drawing and a medium, and the method comprises the steps: obtaining a PDF picture file of the power grid engineering drawing, and carrying out the graying processing; inputting the grayed PDF picture file into a pre-constructed and trained intelligent picture recognition model, obtaining each cell region picture in a table region in the PDF picture file, and recording a corresponding row and column sequence number; performing character recognition on each cell region picture by adopting a character OCR detection algorithm to obtain a character rough recognition result; and matching the character coarse recognition result with a pre-constructed power grid project term library to obtain a character fine recognition result, and constructing a structured data table according to the corresponding row and column serial numbers. Compared with the prior art, the accuracy of a table region recognition result is improved, and the training requirement for a neural network recognition model is lowered.

Description

Technical Field

[0001] The invention relates to the technical field of power grid engineering drawing recognition, and in particular to a method, system and medium for extracting structured data tables of power grid engineering drawings. Background Art

[0002] After the design of a power grid project is completed, the design results need to be reviewed. Currently, the review work relies on manual review by review experts, which is more dependent on the personal experience of experts. The review results cannot be reused, resulting in low review efficiency and heavy manual workload. The design results of power grid projects include drawings, documents, and tables. The element categories in the drawings include graphics, text, and tables, which are rich in content. However, the specific review time left for review experts is short, which puts higher requirements on the review work. In this context, the demand for intelligent review of power grid projects has emerged.

[0003] Currently, there is an intelligent review platform based on pre-trained large models. It uses natural language large models to perform text recognition and semantic understanding on design documents, extract key fields, and then compare them with review specifications to draw review conclusions.

[0004] The existing technical solutions mainly perform intelligent recognition and extract key fields for document-type design results, and there are few intelligent recognition solutions for drawing content, which makes it impossible to support the intelligent review of drawings. The invention with publication number CN117275022A discloses a method and device for complex table recognition and structured data based on PDF files, including: performing image preprocessing on the PDF file to obtain a first image; performing table line detection on the first image to obtain the position relationship of the lines and the coordinates of each point; according to the preset merging rules and the position relationship of the lines, a simplified table structure and the connection relationship between each point and the adjacent points are obtained; according to the coordinates of each point and the connection relationship, multiple cell images are obtained; according to the multiple cell images, text recognition results and data information are obtained; according to the simplified table structure, the text recognition results and data information are organized and integrated to obtain structured data.

[0005] The above scheme obtains a simplified table structure through table line detection, but the scanned PDF image file may have missing and misaligned lines. Reconstructing the table only based on the line detection results may easily lead to recognition errors. After identifying the table structure, text recognition is performed through cell images, but the fonts in the scanned PDF image file may have some missing lines, which may easily lead to text recognition errors. Therefore, the above existing scheme still has the problem of low accuracy for the structured data table extraction results of PDF image files. Summary of the invention

[0006] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a method, system and medium for extracting structured data tables from power grid engineering drawings, so as to improve the extraction accuracy of structured data tables.

[0007] The purpose of the present invention can be achieved by the following technical solutions: A method for extracting structured data tables from power grid engineering drawings comprises the following steps: Obtain the PDF image file of the power grid engineering drawing and convert it into grayscale; Input the grayscaled PDF image file into a pre-built and trained intelligent image recognition model, obtain each cell area image in the table area of ​​the PDF image file, and record the corresponding row and column numbers; Use the text OCR detection algorithm to perform text recognition on each cell area image to obtain the rough text recognition result; The rough text recognition result is matched with a pre-built power grid engineering terminology library to obtain a precise text recognition result, and a structured data table is constructed according to the corresponding row and column numbers.

[0008] Furthermore, the intelligent image recognition model includes a table neural network recognition model and a cell neural network recognition model. The table neural network recognition model is used to identify the table area from the PDF image file and crop the table area image; the cell neural network recognition model is used to identify the cell area from the table area image, crop each cell area image and record the corresponding row and column numbers.

[0009] Furthermore, the processing process of the intelligent image recognition model includes: S201: Inputting the PDF image file to be tested into the table neural network recognition model to obtain the table area recognition result; S202: acquiring the edge outer local area and the edge inner local area respectively based on the table area recognition result; S203: Determine whether the average grayscale value of non-pure white pixels in the local area outside the edge is less than 128, if so, execute step S204; otherwise, execute step S207; S204: Determine whether the average grayscale value of non-pure white pixels in the local area inside the edge is greater than 128, if so, execute step S205; otherwise, execute step S206; S205: After the table area recognition result is clipped based on the local area inside the edge, it is re-input into the table neural network recognition model to obtain an updated table area recognition result, and step S207 is executed; S206: After expanding the table area recognition result based on the local area outside the edge, an updated table area recognition result is obtained, and step S207 is executed; S207: Outputting the final table area recognition result; S208: Input the final table area recognition result into the cell neural network recognition model to identify the cell area.

[0010] Furthermore, the process of respectively obtaining the edge outer local area and the edge inner local area based on the table area recognition result is specifically as follows: Based on the four sides of the table area recognition result, square areas are respectively intercepted to the outside of the table area recognition result with a preset expansion width to obtain four edge outer local areas; based on the four sides of the table area recognition result, square areas are respectively intercepted to the inside of the table area recognition result with a preset expansion width to obtain four edge inner local areas.

[0011] Further, step S203 specifically includes: determining whether the average grayscale value of non-pure white pixels in each local area outside the edge is less than 128, and if at least one local area outside the edge satisfies the condition, executing step S204; otherwise, executing step S207; Step S204 specifically includes: determining whether the average grayscale value of non-pure white pixels in each local area inside the edge is greater than 128, and if at least one local area inside the edge satisfies the condition, executing step S205; otherwise, executing step S206; Step S205 specifically includes: after clipping the table area recognition result based on the local area inside the edge that meets the conditions of step S204, re-inputting it into the table neural network recognition model to obtain an updated table area recognition result, and executing step S207; Step S206: After expanding the table area recognition result based on the local area outside the edge that meets the conditions of step S203, an updated table area recognition result is obtained, and step S207 is executed.

[0012] Furthermore, the training process of the intelligent image recognition model includes: Obtain a PDF image set with a structured data table for training, mark the table area of ​​each image in the PDF image set with a box as the table label corresponding to each PDF image, thereby obtaining a table training set; crop each PDF image according to the table label to obtain a table area image, mark the cell area of ​​each table area image with a box as the cell label corresponding to each table area image, thereby obtaining a cell training set; Inputting the PDF images in the table training set into the table neural network recognition model, obtaining the table area recognition results, and performing model training based on the corresponding table labels; The table area image in the cell training set is input into the cell neural network recognition model to identify the cell area, and the model training is performed based on the corresponding cell label.

[0013] Furthermore, the process of constructing the table training set and the cell training set also includes: The images in the obtained table training set and cell training set are rotated, flipped, and noise is added to obtain augmented data.

[0014] Furthermore, the method regenerates a table according to the acquired row and column numbers, and fills the generated table with the corresponding text recognition results to obtain a structured data table.

[0015] The present invention also provides a system for extracting structured data tables from power grid engineering drawings, comprising a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the method described above.

[0016] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and the computer program is used by a processor to execute the steps of the method described above.

[0017] Compared with the prior art, the present invention has the following advantages: (1) The present invention first identifies the cell area and the corresponding row and column numbers in the PDF image file through an intelligent image recognition model based on a neural network, and then performs text recognition in the cell area in two steps. First, rough text recognition is achieved through a text OCR detection algorithm. Then, in combination with the standardization requirements for economic and technical table terms in power grid engineering drawings, a power grid engineering terminology library is collected and established. By matching the rough text recognition results with the power grid engineering terminology library, a precise text recognition result that meets the power grid engineering drawing standards is obtained, thereby extracting a more accurate structured data table, reducing the impact of missing or unclear pixels in the PDF image file, and improving the accuracy of the extraction results.

[0018] (2) The present invention uses two neural networks to perform table area recognition and cell area recognition in sequence. On the one hand, they can be trained separately to improve the accuracy of the recognition results. On the other hand, in order to address the possible error problems in table area recognition, a method of combining pixel points to determine whether it is a table area or a label area can be used to improve the accuracy of table area recognition.

[0019] (3) The present invention aims at the fact that in the PDF image file of the power grid engineering drawing, the table of economic and technical indicators is located in the outer frame, and there are explanatory annotations at the bottom of the table of economic and technical indicators, and the pixel grayscale value of the explanatory annotations is generally low. The local area outside the edge and the local area inside the edge are respectively obtained through the table area recognition result based on the table neural network recognition model, and it is judged whether there are lines or fonts of non-light-colored explanatory annotations in the local area outside the edge, and whether there are dark-colored lines or fonts in the local area inside the edge, so as to further judge whether the table area recognition result is the outer frame or the inside of the table, thereby realizing expansion or cropping, further improving the accuracy of the table area recognition result, and reducing the training requirements for the table neural network recognition model. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of a flow chart of a method for extracting structured data tables from power grid engineering drawings provided in an embodiment of the present invention; Figure 2 A schematic diagram of a process for combining pixel points to determine whether a table area or a marked area is provided in an embodiment of the present invention; Figure 3 A schematic diagram of a PDF image file of a power grid engineering drawing provided in an embodiment of the present invention is only a schematic diagram of the position of a table in the power grid engineering drawing, and the unclear parts thereof do not affect the main scheme to be protected by the present invention; Figure 4 for Figure 3 A schematic diagram of a local area, wherein the red frame area is the outer frame where the economic and technical indicator table is located, the blue frame area is the explanation and annotation part, the purple frame part is the economic and technical indicator table, and the unclear text in the remaining part does not affect the main scheme to be protected by the present invention; Figure 5 It is a construction result of a structured data table provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions 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. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0023] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0024] Example 1 like Figure 1 As shown, this embodiment provides a method for extracting structured data tables from power grid engineering drawings, comprising the following steps: S1: Obtain the PDF image file of the power grid engineering drawing and convert it into grayscale; S2: Input the grayscaled PDF image file into the pre-built and trained intelligent image recognition model, obtain the images of each cell area in the table area of ​​the PDF image file, and record the corresponding row and column numbers; S3: Using a text OCR detection algorithm to perform text recognition on each cell area image to obtain a rough text recognition result; S4: Match the text rough recognition results with the pre-built power grid engineering terminology library to obtain the text fine recognition results, and construct a structured data table according to the corresponding row and column numbers.

[0025] This solution first identifies the cell area and the corresponding row and column numbers in the PDF image file through an intelligent image recognition model based on a neural network, and then performs text recognition in the cell area in two steps. First, rough text recognition is achieved through a text OCR detection algorithm, and then a power grid engineering terminology library is collected and established in combination with the standardization requirements for economic and technical table terms in power grid engineering drawings. By matching the rough text recognition results with the power grid engineering terminology library, precise text recognition results that meet the standards of power grid engineering drawings are obtained, thereby extracting more accurate structured data tables, reducing the impact of missing or unclear pixels in PDF image files, and improving the accuracy of extraction results.

[0026] Specifically, the intelligent image recognition model includes a table neural network recognition model and a cell neural network recognition model. The table neural network recognition model is used to identify the table area from the PDF image file and crop the table area image; the cell neural network recognition model is used to identify the cell area from the table area image, crop each cell area image and record the corresponding row and column numbers.

[0027] By using two neural networks to recognize the table area and the cell area respectively, on the one hand, they can be trained separately to improve the accuracy of the recognition results. On the other hand, in order to address the possible errors in the table area recognition, the following method of combining pixels to judge whether the table area or the annotation area can be used to improve the accuracy of the table area recognition.

[0028] like Figure 3 and Figure 4 As shown, for the processing object of this solution: the PDF image file of the power grid engineering drawing, the economic and technical indicators table area has the following characteristics: the economic and technical indicators table is located in the drawn outer frame, and the outer frame is as follows: Figure 4 In the red box area, there are notes at the bottom of the economic and technical indicators table, such as Figure 4 The blue box area in the figure, the pixel grayscale value of the annotation is generally low, that is, the color is lighter, and the grayscale value of most pixels is not less than 128, that is, the K value is less than 50%.

[0029] The PDF image file to be recognized may have line breakpoints and offsets, which may lead to recognition errors by the table neural network recognition model. There is also a problem that the recognition result of the table neural network recognition model is the outer frame of the economic and technical indicators table, or the recognition result is a part of the economic and technical indicators table.

[0030] In this regard, Figure 2 As shown, the processing process of the above-mentioned intelligent image recognition model proposed in this solution specifically includes: S201: Inputting the PDF image file to be tested into the table neural network recognition model to obtain the table area recognition result; S202: acquiring the edge outer local area and the edge inner local area respectively based on the table area recognition result; S203: Determine whether the average grayscale value of non-pure white pixels in the local area outside the edge is less than 128, if so, execute step S204; otherwise, execute step S207; If the average gray value of non-pure white pixels in the local area outside the edge is less than 128, there are non-light-colored lines or fonts in the local area outside the edge, and the current table area recognition result is the outer frame of the economic and technical indicators table or a part of the economic and technical indicators table, so further judgment of the local area inside the edge is required; If the average grayscale value of non-pure white pixels in the local area outside the edge is greater than 128, there are explanatory annotations in the local area outside the edge. At this time, the recognition result of the current table area is likely to be correct.

[0031] S204: Determine whether the average grayscale value of non-pure white pixels in the local area inside the edge is greater than 128, if so, execute step S205; otherwise, execute step S206; If the average grayscale value of the non-pure white pixels in the local area inside the edge is greater than 128, it means that there are light-colored lines or fonts, that is, the inner side of the current table area recognition result is an annotation, and it can be determined that the current table area recognition result is the outer frame of the economic and technical indicators table, then the table neural network recognition model is re-recognized after cropping in step S205; If the average grayscale value of the non-pure white pixels in the local area inside the edge is less than 128, it means that there are dark lines or fonts, that is, there are dark lines or fonts on both the inside and outside of the current table area recognition result. It can be judged that the current table area recognition result is part of the economic and technical indicator table. After the table is expanded through step S206, an updated table area recognition result is obtained.

[0032] The reason why it only needs to be expanded once is that the error between the recognition result of the tabular neural network recognition model and the true value generally does not exceed 10%, and expanding it once can meet the accuracy requirements.

[0033] S205: After the table area recognition result is clipped based on the local area inside the edge, it is re-input into the table neural network recognition model to obtain an updated table area recognition result, and step S207 is executed; S206: After expanding the table area recognition result based on the local area outside the edge, an updated table area recognition result is obtained, and step S207 is executed; S207: Outputting the final table area recognition result; S208: Input the final table area recognition result into the cell neural network recognition model to identify the cell area.

[0034] Specifically, in step S202, the process of respectively obtaining the local area outside the edge and the local area inside the edge based on the table area recognition result is as follows: Based on the four sides of the table area recognition result, square areas are cut outwardly from the table area recognition result with a preset expansion width to obtain four outer local areas of the edge; based on the four sides of the table area recognition result, square areas are cut outwardly from the table area recognition result with a preset expansion width to obtain four inner local areas of the edge.

[0035] The expansion width defined above can be adjusted according to actual conditions and tested to achieve the required accuracy.

[0036] In this regard, step S203 specifically includes: determining whether the average grayscale value of non-pure white pixels in each local area outside the edge is less than 128, and if at least one local area outside the edge satisfies the condition, it means that the table area recognition result is a part of the outer frame or the economic and technical indicator table, and then executing step S204; otherwise, executing step S207; Step S204 specifically includes: determining whether the average grayscale value of non-pure white pixels in each local area inside the edge is greater than 128. If at least one local area inside the edge satisfies the condition, it means that the table area recognition result is a part of the economic and technical indicator table, and then executing step S205; otherwise, executing step S206; Step S205 specifically includes: after cutting the table area recognition result based on the local area inside the edge that meets the conditions of step S204, re-inputting it into the table neural network recognition model to obtain an updated table area recognition result, and executing step S207; Step S206: After expanding the table area recognition result based on the local area outside the edge that meets the conditions of step S203, an updated table area recognition result is obtained, and step S207 is executed.

[0037] Preferably, the training process of the intelligent image recognition model includes: Obtain a PDF image set with a structured data table for training, mark the table area of ​​each image in the PDF image set with a box as the table label corresponding to each PDF image, thereby obtaining a table training set; crop each PDF image according to the table label to obtain a table area image, mark the cell area of ​​each table area image with a box as the cell label corresponding to each table area image, thereby obtaining a cell training set; Input the PDF images in the table training set into the table neural network recognition model, obtain the table area recognition results, and perform model training based on the corresponding table labels; The table area images in the cell training set are input into the cell neural network recognition model to identify the cell area and perform model training based on the corresponding cell labels.

[0038] This is equivalent to training the table neural network recognition model and the cell neural network recognition model separately.

[0039] Preferably, the process of constructing the table training set and the cell training set further includes: The images in the obtained table training set and cell training set are rotated, flipped and noise-added to obtain expanded data, thereby improving the recognition accuracy of the table neural network recognition model and the cell neural network recognition model.

[0040] Preferably, the method regenerates a table according to the obtained row and column numbers, and fills the corresponding text recognition results in the generated table to obtain a structured data table, such as Figure 5 shown.

[0041] The process of regenerating a table according to row and column numbers is mainly to realize the tabular display of text recognition results. It is necessary to define the coordinate position of the table lines and each table cell according to the row and column numbers.

[0042] Example 2 This embodiment provides a system for extracting structured data tables from power grid engineering drawings, including a memory and a processor. The memory stores a computer program, and the processor calls the computer program to execute the steps of the method for extracting structured data tables from power grid engineering drawings as described in Example 1.

[0043] This embodiment further provides a computer-readable storage medium on which a computer program is stored. The computer program is executed by a processor to perform the steps of the method for extracting structured data tables from power grid engineering drawings in Embodiment 1.

[0044] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may be a machine-readable signal medium or a machine-readable storage medium. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0045] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for extracting structured data tables from power grid engineering drawings, characterized in that: The following steps are involved: Obtain the PDF image file of the power grid engineering drawing and convert it into grayscale; Input the grayscaled PDF image file into a pre-built and trained intelligent image recognition model, obtain each cell area image in the table area of ​​the PDF image file, and record the corresponding row and column numbers; Use the text OCR detection algorithm to perform text recognition on each cell area image to obtain the rough text recognition result; The rough text recognition result is matched with a pre-built power grid engineering terminology library to obtain a precise text recognition result, and a structured data table is constructed according to the corresponding row and column numbers.

2. The method for extracting structured data tables from power grid engineering drawings according to claim 1, characterized in that: The intelligent image recognition model includes a table neural network recognition model and a cell neural network recognition model. The table neural network recognition model is used to identify the table area from the PDF image file and crop the table area image; the cell neural network recognition model is used to identify the cell area from the table area image, crop each cell area image and record the corresponding row and column numbers.

3. The method for extracting structured data tables from power grid engineering drawings according to claim 2, characterized in that: The processing process of the intelligent image recognition model includes: S201: Inputting the PDF image file to be tested into the table neural network recognition model to obtain the table area recognition result; S202: acquiring the edge outer local area and the edge inner local area respectively based on the table area recognition result; S203: Determine whether the average grayscale value of non-pure white pixels in the local area outside the edge is less than 128, if so, execute step S204; otherwise, execute step S207; S204: Determine whether the average grayscale value of non-pure white pixels in the local area inside the edge is greater than 128, if so, execute step S205; otherwise, execute step S206; S205: After the table area recognition result is clipped based on the local area inside the edge, it is re-input into the table neural network recognition model to obtain an updated table area recognition result, and step S207 is executed; S206: After expanding the table area recognition result based on the local area outside the edge, an updated table area recognition result is obtained, and step S207 is executed; S207: Outputting the final table area recognition result; S208: Input the final table area recognition result into the cell neural network recognition model to identify the cell area.

4. The method for extracting structured data tables from power grid engineering drawings according to claim 2, characterized in that: The process of respectively obtaining the local area outside the edge and the local area inside the edge based on the table area recognition result is specifically as follows: Based on the four sides of the table area recognition result, square areas are respectively intercepted to the outside of the table area recognition result with a preset expansion width to obtain four edge outer local areas; based on the four sides of the table area recognition result, square areas are respectively intercepted to the inside of the table area recognition result with a preset expansion width to obtain four edge inner local areas.

5. The method for extracting structured data tables from power grid engineering drawings according to claim 4, characterized in that: Step S203 specifically includes: determining whether the average grayscale value of non-pure white pixels in each local area outside the edge is less than 128, and if at least one local area outside the edge satisfies the condition, executing step S204; otherwise, executing step S207; Step S204 specifically includes: determining whether the average grayscale value of non-pure white pixels in each local area inside the edge is greater than 128, and if at least one local area inside the edge satisfies the condition, executing step S205; otherwise, executing step S206; Step S205 specifically includes: after clipping the table area recognition result based on the local area inside the edge that meets the conditions of step S204, re-inputting it into the table neural network recognition model to obtain an updated table area recognition result, and executing step S207; Step S206: After expanding the table area recognition result based on the local area outside the edge that meets the conditions of step S203, an updated table area recognition result is obtained, and step S207 is executed.

6. The method for extracting structured data tables from power grid engineering drawings according to claim 2, characterized in that: The training process of the intelligent image recognition model includes: Obtain a PDF image set with a structured data table for training, mark the table area of ​​each image in the PDF image set with a box as the table label corresponding to each PDF image, thereby obtaining a table training set; crop each PDF image according to the table label to obtain a table area image, mark the cell area of ​​each table area image with a box as the cell label corresponding to each table area image, thereby obtaining a cell training set; Inputting the PDF images in the table training set into the table neural network recognition model, obtaining the table area recognition results, and performing model training based on the corresponding table labels; The table area image in the cell training set is input into the cell neural network recognition model to identify the cell area, and the model training is performed based on the corresponding cell label.

7. The method for extracting structured data tables from power grid engineering drawings according to claim 6, characterized in that: The process of constructing the table training set and the cell training set also includes: The images in the obtained table training set and cell training set are rotated, flipped, and noise is added to obtain augmented data.

8. The method for extracting structured data tables from power grid engineering drawings according to claim 1, characterized in that: The method regenerates a table according to the acquired row and column numbers, and fills the corresponding text precise recognition results in the generated table to obtain a structured data table.

9. A structured data table extraction system for power grid engineering drawings, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of any one of the methods according to claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which is used by a processor to execute the steps of the method according to any one of claims 1 to 8.

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